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From Big Data to Business Value How to leverage new insights on any data for competitive advantage

By Gib Bassett

If you’re tired of the hype surrounding Big Data, you are not alone. Many business and technology leaders no longer wish to talk about Big Data conceptually. Instead, they are interested in understanding how analytics can improve their businesses and drive competitive advantage.

This shift is borne out of frustration many report getting value from investments in new

technologies intended to unleash insights quickly, without the constraints of the past – constraints such as the type of data employed in analyses and the forms of analytics which can be applied.

Here in a single document is a compilation of my learnings and observations working with real customers over the past couple of years. My thought in consolidating these posts from LinkedIn was to provide an easy hyperlinked reference for leaders interested in breaking through the clutter to learn ways to leverage data for competitive advantage into 2017 and beyond.

These are my thoughts alone and do not reflect the views or opinions of my employer or anyone else. I won’t fool you though – I do believe that cloud computing has a lot to do with simplifying access to Big Data capabilities such that you can focus more on business outcomes and less time managing technology.

Thanks for looking! Connect with me on LinkedIn and follow me on Twitter.

Scroll down for hyperlinks to an outline of posts.

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Part 1: The Fundamentals

Why just the potential for value won't cut it with big data analytics

What exactly is a Big Data problem?

Why Big Data is so hard…And what to do about it

Anatomy of a successful Big Data project

The Big Data Platform versus Point Solution Debate

Has Your Big Data Train Left The Station?

Part 2: The Business View

Executives Raise their Big Data Analytics IQs

What a CEO should understand about their company’s approach to Big Data

Overcoming the #1 Challenge to Realizing the Value of Big Data

Part 3: The Industry View

Winning the Back to School Dollar with Big Data Analytics

Making the Case for Better Retail and Consumer Goods Analytics

Will CPG find the right balance between trade and consumer investments in 2016?

Optimizing a Complex Marketing Mix in 2016

Bringing Big Data into the Business

The Human Side of Retail Digital Transformation

Busting Big Data Myths

Big Data at Walmart #LoveData

Big Data at Starbucks #ThirdPlace

Part 4: Identifying and Prioritizing Use Cases

Fishing for Big Data Success

Do Something Different in 2016

Prioritizing your Big Data Use Cases

How to architect your talk with a businessperson about analytics

The Art of the Doable

3 ways to gain an edge with Big Data Cloud Services

Part 5: All About Skills and People

Organizing for Analytics

The Data Scientist is Here to Stay

Smartsourcing your Marketing Analytics

A good day for a Data Scientist is a good day for everyone

Now Hiring: Retail Marketing Scientist

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Why just the potential for value won't cut it with big data analytics

Working in the Big Data market has been a very interesting experience. As a business-minded person, I find the ability for data and analytics to inform decisions that help out-maneuver the competition and keep in step with customers among the most compelling ways some companies are addressing their utmost challenges. Despite many successes, I continue to see companies fail to capitalize on this opportunity. Some thoughts recently came to mind about why this is happening. Perhaps you have had similar experiences? Read on and let me know.

I think Big Data continues to perplex many companies and their executives

Big Data is still often viewed by many as just a bigger pile of data, to be used for some unspecified end - and is therefore just noise. Maybe it’s cheaper storage, or a way of cutting further costs out of your IT budget by moving a proprietary data warehouse to a Big Data

technology like Hadoop. People who think these ways haven’t been exposed to the strategies, roadmaps, and successes of companies that adopt a “data driven mindset.” They don’t understand how the data available today, plus new sources yet to be obtained or explored, can help answer new questions about their businesses. It’s really not as muddy as figuring out whether a sales report is representative of Big Data or not. It’s clearly about exploring factors

that influence your sales that you can’t see based on whatever data and analytics are available. This is just an example.

The Illusive “Black Box”

The Big Data Analytics that reflect your business likely differ from your competition. Not necessarily the measure itself, such as customer value, but the data sources and business processes that provide data and are impacted by new analytics. Consider too that most executives understandably view their analytic methods as trade secret. Executives who seek a black box with pre-defined answers to questions they already know are not thinking about Big Data in the right context. They may have operational reporting problems but that’s different.

Separate Skills / Same Foundation

At a Gartner presentation this week by Doug Laney, he highlighted the separation of SKILLs related to operational reporting from those employed for advanced analytics. I capitalized SKILLS, because he was speaking organizationally (similar to what I heard at this event earlier this year). When it comes to the enabling technology that underpins the work of both groups, there is a lot of value in considering an integrated environment where data and analytics are

easily shared, moved and improved across the two areas. The fact you organize resources uniquely on one hand, yet they should share a common foundation, is another point where people get confused. Laney mirrored this sentiment when he said that IT was slowly separating into an “I” group focused on analytics and information, and a “T” group focused on technology.

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Big Data isn’t really an application with a targeted set of users

Big Data’s potential lies in the capabilities to support use cases. This is where Big Data is falling down in some companies. Companies that have moved on “Big Data” in the name of lower costs have placed their bets on a use case that will simply let down businesspeople. And ultimately kill any potential benefit of Big Data.

Lowering costs is fantastic, but what’s happening is that execs in companies that pursue Big Data to this end are starting to scratch their heads about the reported business value of Big Data (like Tom Davenport talks about here). The head scratchers are people in the business who are not seeing any benefits and these are the ones who are absolutely KEY to taking advantage of Big Data. More data driven managers are needed, said Walmart Labs’ Esteban Arcaute, not necessarily more data scientists.

The resolution to these questions is to start with a plan to develop a portfolio of use cases in partnership with those in the business. So that Big Data represents more than just potential value, it forms the centerpiece of a plan to drive business performance in very specific ways using analytics. Laney said business leaders finally serious about Big Data would within 90 days bring together analytic and business talent to work together to start outlining projects.

By bringing together IT, analytics and the business, specific analytic problems and opportunities can be approached, operationalized, and socialized. Building wins and momentum is key. Without it, the promise of Big Data is likely to go unfulfilled.

It’s being said in many reports that business culture is the biggest barrier to becoming more data driven. Without an attack plan to fulfill the promise of Big Data Analytics with a business use case oriented roadmap, I’m afraid a lot of companies are missing the value boat.

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What exactly is a Big Data problem?

In the iPhone world they say “there’s an app for this and an app for that.” In the business world, when it comes to analytics, you could say the same. Whether it’s social media, marketing, advertising, supply chain, and others, there are analytic applications (or services) available to inform these discreet business areas.

So when it comes to Big Data Analytics, what’s the point versus an application? That’s a more common and logical question than you might imagine.

Most applications are pre-defined around a particular problem or analysis. This accelerates time to value and is especially appreciated by businesspeople hungry for new insights about their social media campaigns, advertising effectiveness, supply chain efficiency and so on.

The challenge is that the packaged nature limits the extent to which different or new data sources might improve upon what an application was designed to account for. That word “might” becomes a central point of focus in addressing the question. You also have to think about the way Big Data Analytics are unfolding successfully in companies large and small.

The idea behind Big Data Analytics is to leverage both new technology and new skills to uncover new insights. In practice, companies are using their existing analyst teams, newly hired data scientists, and partners to work with new technologies like Hadoop, Map Reduce, Spark, R and others to tap into the value potential of examining data beyond what pre-defined applications account for. Companies that do this well ensure close collaboration with business sponsors who help shape the projects and put them into operation.

This would be a fool’s errand were it not for companies experiencing significant gains in financial performance as a result.

With most business functions interrelated as it pertains to something important such as the customer experience, it’s beneficial to look across the business and beyond to inform a variety of decisions which could impact key moments in the customer journey. Check out Using Big Data to Understand Small Moments of Truth for more about this idea.

This is challenging when most companies have already invested in a variety of applications and services which align to different business areas. It begs for a means to collect, integrate and analyze data across these different business domains, and extend that analysis when it’s beneficial to add new data sources (such as a new social network that arises). It’s become apparent that most companies’ legacy systems are ill suited to this new world.

This is where a business problem presents itself that begs for a Big Data solution. The next challenge is to understand which decisions could be improved upon and are worth exploring beyond whatever insights the business currently employs. It’s both a function of what can or should be improved, and how much value is attainable.

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This is where businesspeople often get stuck in trying to divine the difference between an application and Big Data Analytics. Some helpful ways to think about this include:

It’s about differentiation

Analytics are how industry leaders differentiate themselves and maintain their top dog positions. They are less interested in the analytics their peers use, and more interested in the analytics that pertain specifically to the unique characteristics of their business. The difficultly many companies have is figuring out what decisions are important enough to approach from a Big Data point of view.

It’s about business improvement

Just the other day I posted a piece to LinkedIn called “The Art of the Doable.” Check that out, it describes how to go about identifying the high yield decisions to consider for Big Data Analytics.

It’s about valuing data

Beyond the rhetoric about valuing your data as an asset is the reality that companies who embrace this idea at their core do better financially than their peers. It’s a cultural as well as technical hurdle documented by analysts and other researchers who have studied Big Data adoption across industries.

It’s about specificity

Once again, I refer to “The Art of the Doable” to read about some ways to be as specific as possible when considering Big Data use cases. Honing in on those use cases that are both high value and can be operationalized quickly is crucial to early success with Big Data.

The beauty of the fast paced Big Data technology industry is how quickly the software is evolving to account for all the stages of the analytics process so that businesses can spend more time thinking about what questions to ask and less time managing technology.

When you can in one fell swoop buy space in the cloud necessary to land whatever data you want securely, then integrate, prepare and enhance it as well, you can greatly accelerate the process of applying new forms of analytics to uncover those specific insights you are looking for.

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Why Big Data is so hard…And what to do about it

If you’re like most managers, you’ve been approached by vendors proposing new ways to gain more insights from your data. Unfortunately, this is easier said than done. Why? Besides having a day job, you’ve come to rely on some form of internally produced Business Intelligence or externally sourced analytic service. These more or less get the job done. How could you ever consider doing something differently?

Just because something is possible doesn’t mean it’s worth doing. This is the essence of the

conundrum organizations face with the promise of Big Data – which is developing valuable new insights leveraging data historically unaccounted for by your internal Business Intelligence or external services provider. Critical is that these insights be worth the effort, in that they result in a meaningful improvement in business performance. That’s where investments in Big Data pay off.

Getting over this hump for all but the most sophisticated companies can be very challenging. Big Data, just the concept, let alone the options available, is complex. Not many of us have the time to understand what’s most important and how to get started. Here is what I have found helpful.

Whether you are a business manager or work in information technology, recognize first that Big Data is closely related to Data Science. I say related, because they are not one in the same but rather leverage one another. Data Science is a relatively new discipline that brings forward the roles of statisticians, analysts, data miners and programmers, into the world of analytics possible when data can live in any format and at any scale. Big Data implies the use of data to this end in a way that today’s Business Intelligence or your existing data warehouse simply cannot account for.

Second, understand that Data Science is a “science,” which means experimentation is

fundamental to the job. Data experiments might be another way to describe use cases which address questions such as how to attribute online and offline marketing investments to sales. Businesses have little tolerance for technology projects that lack a clear ROI, so it’s a good idea to think about how you might remove as little of the uncertainty around data science as possible.

Data science skillsets may be scarce, but they are growing. If your company is waiting it out, hoping for the hype to pass, know that in the U.S. the top 10 retailers have hired an average of almost 100 data scientists. At the other end of the maturity curve, smaller companies are

making their first hires and augmenting these with consultants. This is no trend but the new competitive battleground. I think the challenge many face today and will into the future, is how to speed up the work of the Data Scientist so they can focus on more use cases, more quickly, and thus increase the likelihood of surfacing meaningful insight.

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Thirdly, Big Data is not all about Data Science, but a whole lot of technologies that account for what I call the “analytics process” – all the steps required to go from data to operationalized

insights. For those in information technology roles, I’m referring to your data management platform, where you might have both relational databases and unstructured data lakes managed apart or together. I’m also describing the products you leverage to integrate, enhance and prepare the data for use by Business Intelligence users, analysts and data scientists. And I’m referring to the burgeoning set of capabilities which help business managers and Data Scientists perform discovery on Big Data, create and deploy descriptive, predictive or prescriptive models, and share visualizations of the results.

This collection of technology broadly defines the architecture most companies possess today and which comprise products and cloud services from dozens of companies.

What excites me about the Big Data space today is the momentum around bringing all the required capabilities to support the Big Data Analytics process to the cloud. What this means in practice is that you, as a manager or information technology professional, can start thinking more about capabilities you need to execute a use case, versus products you need to buy, implement and support for some discreet aspect of the analytics process. You can also start to identify inefficiencies in your current process that hamper speedy execution of use cases and which could be engineered out by leveraging cloud capabilities instead.

Where this makes a big difference and simplifies Big Data is in the ability to try out a variety of

use cases using a combination of cloud capabilities, either in tandem or apart from your current technology architecture, much faster. This is especially valuable considering that companies gaining the most value from Big Data are outsourcing less of their analytics, and instead developing their own in pursuit of competitive differentiation. Cloud makes doing this simpler.

Remember too, your company today probably outsources a number of analytics to services companies. From an information technology professional point of view, I would rather get in front of my business users outsourcing analytics to help them in-source as much of this as possible, add security and governance, while also helping my burgeoning Data Science team start making a real difference to my company’s performance.

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Anatomy of a successful Big Data project

In my last post, I described how achieving the value of Big Data Analytics is so difficult for so many companies, and how to think about overcoming the challenges. What often comes next is a decision to proceed with a project. So I thought it useful to describe what a successful one looks like.

The easy but incomplete answer is: a project that yields business value. This fails to illuminate all the markers that line up and which lead to business value. So think about this.

First, very few Big Data Analytics projects succeed without a business sponsor. As an information technology professional, it’s alluring to develop the technical capabilities necessary to execute a project independent of a business sponsor. It’s easy to see why: the architecture and its components can be deceptively easy to identify.

The issue here isn’t so much about architecture as it is about operationalization. How would an insight be put to use? Most people won’t buy into a project which targets analytics related to their job, without having been involved in the idea to begin with. It’s just human nature. Yet this is the most commonly reported issue facing companies that fail to achieve the expected value from their Big Data investments.

A wise former CIO for one of the most progressive consumer-centric CPG companies recently told me the following in response to my question: What do you think are the most important considerations for leveraging analytics in the cloud?

“I believe there are three considerations to adoption of cloud for analytics. First, what is

functionally of significant difference to what is already in place on premise or otherwise. The

second is whether there is the talent internal to the customer available to take advantage of the

capability. The third is a combination of ease of getting the relevant data sets and the

associated security established in the cloud.”

His comments illustrate first the importance of identifying something new. As I described in my prior post, besides the challenge of identifying a business sponsor, the next issue to tackle is how to accelerate the execution of a use case. Successful projects should be measured in part by how quickly an insight is generated and evaluated. If you cannot benchmark this against a current state, at the least target a realistically short timeframe that allows you to imagine supporting many such use cases over time.

This is another barrier to success cited by many companies. It’s similar to trying to take your craft beer business and scale it nationally. How do you scale the value of your investments in Big Data human capital, not just the technology? This in turn relates to his second comment.

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Maximizing the value of your internal talent means streamlining the analytics process. Cloud capabilities offer a path by providing access, on demand, to any element of that process – from data management through modeling and deployment.

You may have holes in your architecture, use a wide variety of technologies, or spend too much time and money managing technology as opposed to focusing on business sponsors and use cases. These are all good justifications to explore Big Data cloud capabilities with a business sponsor. This meets the “something new” hurdle and leads into determining the scope of skills necessary to execute a use case.

The third consideration he mentions, access to the relevant data and security, are somewhat obvious but you would be surprised how some companies consider use cases without first

documenting their Big Data assets. In fact, that’s a good exercise for almost any company to do – produce an audit of the data available to your business, internal and external, regardless of where it’s stored, the format or whether you believe it has business value or not. This greatly accelerates the prioritization and scoping of an analytics project. Consider this an element of a success as well.

Regarding security, it’s interesting how in reality enterprise cloud services have better documented security than most on premise data centers. As companies gain more experience taking a use case centric approach to bite sized projects, this no doubt become less of an issue.

Circling back to the first point, business value, it’s important to keep in mind the reason to engage in this work is to move the needle on business performance. Successful Big Data projects happen when an organization works collaboratively across IT, data science and the business to leverage cloud capabilities that help accelerate the execution and operationalization of new insights.

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The Big Data Platform versus Point Solution Debate

As I wrote recently, many marketers outsource analytics that could be more efficiently in-sourced while providing greater value. Fact is, many companies outsource analytics for a variety of business areas, or use “point solutions” -- pre-built applications or software tools focused on specific tasks. All of this has happened over the past 10 to 20 years in the pre-cloud era, but I think the world looks very different today. Especially in the Big Data space.

Fighting for mind- and wallet-share against services and point solutions are platforms which

enable multiple use cases, including many unaccounted for in those same services or points solutions. The rationale for the platform instead has always been that it offers greater value and more specificity to the business. For analytics, that kind of specificity is what drives real value. Consider Netflix this week reporting their recommendation engine is worth about $1 billion dollars to their business. Competing on analytics is not just a catchy slogan.

The historical challenge with platforms has been the on premise model required a lot of services and people to implement, operate and maintain. In the cloud era, much of this necessary but not terribly valuable investment goes away.

In a Big Data context, what’s exciting is how formerly on premise technologies related to database, data management, integration, discovery, analysis and visualization can be collectively or individually provisioned on demand to address a variety of use cases that apply specifically to a business’s challenges, opportunities and most relevantly, available data.

With these formerly disparate parts working together to speed up what is actually the analytical process, companies have the opportunity to address some of the greatest pain points related to getting value from Big Data. Namely, that data science investments are not paying off because these very smart resources spend more time wrangling data than on focusing on a greater variety of use cases in collaboration with business owners.

Without greater business participation in Big Data projects, it’s not realistic to expect investments in Big Data to pay off. IT meanwhile is trying to lead with Big Data by leveraging the core technology to reduce costs but offer no net-new analytical capabilities. Many executives report frustration at their lack of value from Big Data, so you can see why for many businesses the promise of Big Data is simply not happening.

With cloud, companies have the opportunity to course correct and adopt the agile, test and learn culture of Big Data Analytics that is quickly becoming best practice for the Netflix’s of

every industry. In this realm, the tradeoff between leveraging a platform and a point solution goes away, business owners get more engaged in analytics projects, and the value of Big Data investments surfaces to executives anxious to generate new insights to truly improve the business.

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Has Your Big Data Train Left The Station?

Despite evidence that success with Big Data starts at the top, the fact is few companies’ leadership have yet to embrace analytics in this way. Instead, from the grass roots and often within IT, companies have embarked on uncertain Big Data journeys and the results have been underwhelming in many cases.

The failure to demonstrate large scale value from the bottom up is simply evidence of the need to get buy in from the very top of the organization. It’s become a self-fulfilling prophecy. Are these companies doomed to fail?

The journeys are underway, investments have been made and continue, new hires are coming into the organization, and many CEOs and top lieutenants are just now starting “to get it” that data and analytics need to be woven into the fabric of the organization.

Having started in the wrong place, can these companies course correct? They simply don’t have time to hit the pause button and unwind what’s already happened. Without the time or resources to step back and re-evaluate, what can companies do to leverage what’s already happened and get on the right track?

That’s the essence of many conversations I have with business and IT executives in retail and consumer goods organizations. I suspect the same happens across many industries. The Big Data train has left the station but the destination isn’t clear and the locomotive cannot be stopped. If your company sounds like what I’m describing, here is what I recommend:

For IT

Almost every company has deployed an instance of the open source technology Hadoop and its related projects - somewhere. The use cases vary, but tend to be focused on reducing the cost

of storage or a dependency on a pricey proprietary data warehouse. In the absence of such a use case, the technology was adopted with an eye on a possible application, but beyond creating a data lake, the use case wasn’t clear.

Often unrelated, perhaps your organization has a data scientist or a small team of advanced analytics practitioners – maybe they use SAS or open source R to develop and deploy predictive models focused on helping the business understand demand patterns or customer segments. These folks may or may not know about or leverage your Hadoop investment. In many cases, they do not – they may in fact have their own server right under their desk or rely on far fewer data sources than are actually available.

Data scientists have reported preoccupation with tracking down and integrating data to support projects. For them, productivity is very important – spending as much time on higher value actions than lower value ones which can potentially be automated or abstracted, but without restrictions.

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If you identify with this situation, it’s really incumbent on you, as a grass roots Big Data evangelist if you will, to connect the dots between the current investments in Hadoop and your Data Science team. The reasons are borne out of best practices from Big Data leaders. Namely:

Think of the use case first and foremost – not that you just might be able to leverage Hadoop to some unclear end. A use case may require scale access to very structured data in a data warehouse, so maybe connecting the dots between the lake and warehouse is necessary. Architecture is important but don’t get bogged down by it – think instead about agile pilot projects leveraging cloud service options where possible.

Work to strip out as much administration of the Hadoop and related open source technologies as possible. You need to focus your scarce resources on serving and understanding the business and use cases. Companies are struggling to manage up to dozens of open source projects to facilitate a single use case which is not scalable.

Inform the use of Hadoop with the needs and requirements of the Data Science team, or the type of new role I wrote about last time being hired to act as business/analytics liaisons between managers and IT. Focus on making their jobs easier too, so that they may focus on collaboration with the business.

For the Business

If you work in the business and don’t care about IT, but leverage the “BI” or “Business Intelligence” software provided by them, you may have no clue that the above is happening.

What you care about is your day job and spending as little time analyzing data as possible, but also trying to be as data driven as the tools available allow.

Even without a mandate from the top, smart managers are right now working with their data science or analytics teams and in partnership with IT to attack any number of powerful questions simply unanswerable by Business Intelligence tools today. This happens because those skilled technical and analytical resources have streamlined and scaled the underlying Big Data capabilities such that they have time to collaborate.

In a consumer market context, deceptively simple questions such as, “What’s a satisfactory

shopping journey look like and what can I do to create more of them?” requires expertise, technology and data of the kind available only when taking a Big Data Analytics approach to the problem.

If IT and Data Science are organized as described here and proactively work with the business to deliver an internal “insight as a service” capability, the potential to improve results drastically increases.

Whether your CEO is on board or just getting acclimated to the potential of Big Data Analytics,

you have the power to elevate the visibility and value of Big Data within your company. For your career and future job prospects, that’s a strong value proposition.

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Executives Raise their Big Data Analytics IQs

For non-technical executives, the thought of Big Data analytics can be either confusing or nauseating, depending on how much exposure they’ve had to vendor pitches over the past few years.

The hype, promised new benefits and urgency to move quickly, all have combined to create a perfect storm to either perplex or frustrate executives hungry to improve their business with better insights.

It’s a well-documented situation. On one hand, executives realize that their organizational structure relative to the data which underpins the business is suboptimal. Operating off of “one version of the truth,” is of course best practice.

It’s also become incredibly challenging to get your head around this idea, given the years of silos building up in the business. At the same time, there are a whole slew of new data sources available that might offer a path to some breakthrough insight. Certainly executives in the industries I focus on – retail and consumer goods – are in a desperate search for new ideas to address the challenges stunting their growth.

For vendors that provide Big Data technologies, the lens has shifted to a logical view of the data available to inform business insights. There’s just no longer a single place, in a single form, for data to live. It’s everywhere, internal and external, and in a variety of formats. The question is now, how to manage all of this data in a way that is secure and supports decisions across the enterprise?

Interestingly, the challenges executives face to taking action remain the same as in the past, when there was only the monolithic relational data warehouse. How do you get started? It’s debatable that a logical data warehouse or data lake, is still just a conceptual database in need of compelling and actionable use cases to make it a worthwhile pursuit.

As a consequence of changes in the data landscape, there’s been a slow-down in demand for technologies which support basic reporting on long-common data sources – I’m sure if you work in this space, you know what I mean.

Instead, what’s resonating with executives, is exploring new forms of analytics on data they have long held, in combination with new data sources, to develop new insights. The technologies supporting such projects tend to be non-relational in nature and emphasize discovery and hypothesis testing, over creating a highly structured reporting environment.

The challenge stemming from this situation is how to operationalize insights. There’s a building perception that Big Data analytics projects are little more than science experiments that are never woven into the business. It’s often not as simple as tying a new metric to a record in your data warehouse. That’s what should happen anyway, but what executives want are analytics

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that get fed into the core of their business to drive better performance. That’s not solely a data integration exercise.

Savvy executives are starting to understand what’s happening and approach Big Data Analytics from a different perspective.

While recognizing the opportunity to manage their data as a logical unit available to support all manner of use cases, executives are rightly demanding that this “end state” be approached in a manner that creates and builds rapid value.

It often takes a change agent to lead the charge. With the end state in mind – managing and leveraging data as an analytical asset – leaders need to be able to demonstrate value quickly

and socialize this with their peers. Absent this step, the ultimate goal is highly unlikely ever to happen and the status quo will persist.

To get started, CIOs and analytics teams should approach big data with an eye on attacking key

processes, in collaboration with executives from other areas of the business. For consumer goods and retail organizations, you can imagine analytics informing all the functions which affect the consumer’s shopping journey. From communication, to fulfillment and the post purchase experience, it’s the execution of activity across this process which separates a leading organization from the pack. Better analytics, leveraging current and new forms of data, are the engine for better execution of the activities which affect the shopping journey.

Vendors have to rise to this challenge by plotting out clear paths for embracing new analytic methods that become living parts of the business. It’s about managing data such that it’s easily sharable in an analytically-consumable way across the company, securely and reliably – whether on premise, in the cloud or some combination.

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What a CEO should understand about their company’s approach to Big Data

Becoming a more data driven organization is at the heart of the Big Data movement. Most industries have been disrupted, or are in the process of being disrupted, by accelerating changes in their markets. Shifting consumer behaviors, enabled in part by digital technologies and the Internet of Things, are the prime movers of the headwinds numerous industries are facing. Enormous amounts of new data are being created and coming online every day, that presents the opportunity to do something differently and move the needle on business performance.

Another force of change, however, are the companies leading the way in Big Data. These companies are illustrating how analytics and new insights can not only differentiate you from the competition, but in some cases literally crush the competition (as pointed out in this recent article about Amazon’s dominance). For everyone else, this march of progress is creating urgency to act now.

What companies in many industries are experiencing is a gap between their investments in the underlying technologies of Big Data, and the anticipated benefits. That gap looks a bit different

between companies at the bleeding edge of adoption and those just beginning their journeys. Yet there is one thing in common between the two: wide-ranging views on what Big Data represents in terms of value creation and how it fits within the organization.

CEOs have enough to worry about and focus on, without getting into the weeds of Big Data technologies. But I do think they have a mandate to understand at the level I describe in this post how their companies view Big Data through the lens of strategic, competitive differentiation.

Considering your data as an asset

Gartner’s Doug Laney has described how companies can value data as a financial asset or

instrument, with data being accounted for on a company’s balance sheet. Even if you do not literally approach your data in this manner, consider this: When retailer Radio Shack filed for bankruptcy recently, initial reports around the sale of its assets suggested Amazon highly prized their store locations to facilitate large scale local pickup and return service. In the end, the bankruptcy court found Radio Shack’s most valued asset to be its customer data.

The moral of this story is this: had Radio Shack used its decades of consumer insight to

understand changing consumer buying preferences and adapt their business model accordingly, could they have avoided bankruptcy in the first place?

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Your Big Data leaders

Some companies are creating new roles, such as Chief Digital Officer, or Chief Analytics Officer, to own Big Data strategy. Many have yet to appoint such an executive, and are instead assembling a proxy for the role: some combination of the CIO, CMO, CFO or COO, an Analytics Leader and line of business leaders. Whichever model your company employs, it’s important to know that Big Data is a multi-dimensional value driver.

What I mean here, is that Big Data is not a functional application, or a single enabling technology for one silo of your business. The difference with Big Data is that it represents ways for individual areas of your business to improve with new insight, based on an understanding of all the possible data available to the company – across silos, and even extending outside the

company to the dozens, if not hundreds, of vendors working on behalf of line of business managers.

The role of line of business managers

The implication of the above directly reflects the experiences of progressive adopters of Big Data analytics as well as those less mature. Companies at the bleeding edge are finding the #1 inhibitor to Big Data value is having more line of business managers engage with them in projects.

We all know data science and advanced analytics talent is in high demand, with a scarce supply, and the accompanying challenges of retaining these people. Irrespective of the hiring and retention challenges, however, the lesson is that we must remember: most managers, in most companies, still make most decisions by gut feel based on inaccurate or incomplete information – and we have conditioned them in this way. That’s the data driven gap that companies are desperately trying to close with Big Data, no matter their maturity.

Therefore, you must also remember that Big Data value is as much a function of culture and mindset, as technology. You want managers interested and engaged in making data driven decisions. So how do you approach this problem?

Throwing technology at a business problem

Many companies report struggles with very fundamental problems such as daily business performance reports. Your IT organization is likely the provider of this internal service, and likewise struggles meeting business manager needs, let alone appreciating the decisions on which the information depends. This constant push and pull is the rallying cry behind more IT collaboration with the business. It’s also why many business managers have become conditioned to rely more on their intuition for making decisions, and less on data driven insight.

Many companies see Big Data innovations as a way of leapfrogging these problems by giving even more technology to business users. There is absolutely innovation happening here, with new ways for an average business person to tap into new insights offered by many data sources.

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This leads to a debatable question: do business users want better reports, interactive visuals and new metrics, based on a lot of new data? I’m not so sure, especially if an IT organization

alone is behind the effort. Would not the same issues persist around the push and pull I mentioned? To me, it sounds like throwing technology at a business problem and hoping for a better outcome.

Start with the end in mind

Instead, I would step back and start with the end in mind. Your ideal state is one in which your business managers leverage data to make better decisions, your organization is realizing the value of data and analytics as differentiators, and functions collaborate with one another based upon the new insights offered when you can leverage all available data sources to fuel a data

driven business. That sums up the strategies being pitched by technology and consulting providers alike – a roadmap, journey, or vision.

Today, some of these activities happen in your company, some do not. For many companies, there are dozens of technology and consulting providers involved. So how, as an executive, do you even consider a roadmap when the existing environment is so complex? You don’t have 3 years to implement a strategy; you want to act in 90 days or less.

The changing role of IT

At a Gartner conference recently, a large roster of companies succeeding with Big Data was cited. Led by the aforementioned Doug Laney, the session also mentioned what Gartner was seeing among these companies: a bifurcation of IT, into an “I” organization for information analytics and a “T” group to manage technology. The rationale? A singular group could not easily balance the needs to operate technology at the lowest cost, while simultaneously helping the business unleash the value of Big Data.

Even if this is a very nascent idea, I think it’s super predictive of what we will see happening within the next 36 months in many industries. It’s reflective also of the moves many companies have made to develop new departments outside of IT for Data Science and Advanced Analytics.

Organizational versus technology considerations

However, Gartner also found that the same division would not occur with the underlying technologies supporting both “I” and “T.” This is not a fine point, but a very important one.

A single “thing” (platform or technology) should ideally account for the needs of both your day-to-day business performance reporting that IT manages, as well as the needs of analytics professionals who need a productive environment to explore, model and deploy new insights

back into the business to execute and measure. The underlying data is therefore available to both, and a single approach also offers the most efficient and cost effective solution versus maintaining separate and loosely coupled technologies.

These ideas differ substantially from how companies on the bleeding edge have adopted numerous open source products outside the core technologies used for business performance

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analysis and reporting. This is the where the battle among technology and consulting companies resides in my opinion. Whoever helps you set this table and makes you successful most quickly, will disproportionately benefit from the budget you allocate to these efforts.

Paint the vision, pursue the project

The way to think about all of this is to adopt a vision for your company – such as the end state I mention above, and proceed with specific actions, or projects, that demonstrate steps to achieving the value. Your teams have the opportunity right now to rapidly spin up projects leveraging cloud technologies to start delivering value today.

Why now and why the reference to a technology delivery approach such as cloud computing?

Because to help managers believe in the value of data driven decision making, they must see the value first and you must deliver results quickly – which is what cloud computing represents. People’s attention spans and personal agendas simply will not accommodate the typical timelines associated with traditional technology projects aimed at addressing core business culture and old school decision making methods.

Managers must see for themselves the difference analytics makes to their peers, and how it contributes to their professional success. Doing this well with an initial set of internal champions creates positive chatter and momentum for others to engage in the process. This is how you overcome the challenge that progressive adopters of Big Data analytics are experiencing with a deficit of data driven managers.

As a CEO, you should expect the leaders in your company carrying the Big Data torch to proceed along these lines – not getting bogged down by how Big Data can save money or power innovative new insights leading to significant increases in sales, because ultimately Big Data done right offers both! You want your leaders to drive to an integrated vision for data and analytics, which leads to more managers adopting and championing data driven decision making that materially improves the performance of your business.

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Overcoming the #1 Challenge to Realizing the Value of Big Data

Some Retailers and Consumer Goods companies are in the early stages of adopting Big Data to explore new ways of doing business, improving existing operations and differentiating from competitors. Many more have just dipped their toes into the Big Data waters by focusing on cost savings around data storage and alternatives to proprietary data and analytic technologies. I think the same could be said of many industries.

A select few have moved beyond the honeymoon stage, and have gone “all in” on Big Data as a

distinct competency outside of IT (e.g. Data Science or Advanced Analytics). Generally speaking, these are the large market leaders who set the agenda for everyone else.

What’s emerging in all of these cases is a common thread – the importance of having business managers engaged to realize the value potential of Big Data Analytics. That’s what this post is all about.

Big Data is a complex idea to get your head around. Just a few years ago, you were an expert if you understood Big Data in terms of new forms and higher volumes of data, which old technologies were not designed to accommodate. That was the hype phase, but those days are long gone.

Too many companies have shown and continue to show the value of exploring new insights on all manner of data types. Think about pure internet companies literally born from data, such as LinkedIn, a company with a market capitalization of nearly $30 Billion and not the consumer darling that is Facebook. Consider too, that Amazon has a valuation of nearly $300 Billion. That’s why many of the largest companies in both Retail and Consumer Goods sectors are well along on their Big Data journeys – yet, if you look at their performance and that of the industries’ overall, sales, margin and consumer loyalty remains challenged. What’s wrong?

The blocker that many market leaders now face is too few “data driven managers” interested in helping shape the way Big Data analytics are applied and put into practice.

For the many less progressive companies first adopting cost-centric Big Data use cases, the problem is similar: they have created the perception that Big Data is about saving money, not improving business performance. To change course at this point requires a change in mindset by the business – no easy task. Analytic skills remain scarce, but alone are insufficient to scale the business value of Big Data.

Few leaders in IT, insights and analytics roles have formally approached business leaders to

bridge this gap. The reason is, how do you have this conversation? What would be the focus? Certainly businesspeople don’t care about the technicalities of data or analytics -- they just want capabilities. Their primary job is around managing a process, people and their careers.

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Among the most progressive companies, they haven’t been able to do this enough. Their challenges tend to be a bit different. Instead, they are busy doing the work of analytics and not the business side of analytics. They need more engagement with the business.

Here’s how I’ve heard IT and businesspeople describe their typical interaction:

IT – “give us your requirements.” Business – “tell us what’s possible.” Result: Gridlock!

A great way to frame this conversation instead, is to first address a few questions. Such as: What exactly is Big Data? What does it mean to your company? How can Big Data make a difference to how business managers perform their jobs today?

On a functional or departmental basis, it makes sense to hold collaborative meetings with business managers to outline these answers and engage them in a formal process around exploring use cases focused on very specific business outcomes.

I think there’s a three-legged stool emerging that is necessary here – IT, analytic talent and the business – who must unite behind this effort. Even if you don’t have a Big Data strategy or it’s in development, it’s critical to have these conversations now, not later. The reason?

It’s become fact that done right, you can move the needle on business performance with Big Data Analytics, at a significant return on your investment. The challenge is getting it “right.”

Regarding the question: “What is Big Data, in general or to your company in particular?”

Businesspeople use data or have access to it every day, to some extent. As I’ve said before, most managers still use gut feel to make decisions, even when they have access to data. This is a well-documented problem that flies in the face of efforts to give ever more powerful software tools to business users. A certain population of businesspeople desires this but they are not the majority.

Personally speaking, I think the value of analytics isn’t an “either / or” question as to a particular type of employee with a particular set of skills. Right now, it’s about collaboration given the specialization of talent necessary to do analytics right.

Today IT provides reports and dashboards, as well as access to data that many people interrogate in Excel. This is operational analytics, which has been used for decades. This is not Big Data Analytics. The key word here is “Analytics.” Big Data is just the identification of data sources, some of which could form part of a report a sales manager sees every day. They don’t care how that metric is pushed to their dashboard, just that it’s there.

So, if you are staging messy sales data in a Hadoop file system somewhere and generating a query against that data every day or week that feeds someone’s dashboard, that’s a great use case for the underlying technologies of Big Data management and storage – but it’s not a form of advanced analytics.

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Gartner mirrors this sentiment, saying that their clients in many industries taking the lead on big data are carving out resources, specialized skills and budgets to attack advanced analytics

problems apart from IT. In fact, they envision IT splitting into two groups, one aligned to operational IT and cost containment, and another on information analytics. If this comes to pass as true, it will be a major shift in the technology market.

You should clearly explain to business managers that Big Data is about leveraging new forms of data, along with data that you have already, to answer questions about your business that you simply cannot today. Or, discover questions of your business that you never thought to even ask. This leads right into the next question.

Regarding the question “How can it make a difference to my job today?”

This is where the rubber meets the road and the problem companies are struggling with today. Once business managers understand Big Data, the next step is to have them engage in shaping projects. And doing so quickly, as in 90 days or less, not 6 months. Gartner is reporting this as best practice.

Business managers don’t care about technology, but they must actively participate in projects to ensure that new insights are operationalized into the business, measured, and improved. Remember, asking business managers to engage in this process is not the same as asking them to care about technology -- it’s asking them to collaborate to leverage new forms of analytics to move the needle on business performance.

A great way to frame the conversation is around how the different functions in your company serves your consumer throughout the purchase journey. Every Retailer and Consumer Goods company professes a focus on their consumer, yet silos define the way many go to market. That creates blind spots for sales, marketing, service, supply chain, and operations that Big Data Analytics can illuminate to improve outcomes with consumers.

Lead with Business Outcomes Focused Use Cases

I prescribe a focus on business-outcome focused use cases, to appeal to the needs of line of business managers and which requires the type of collaboration I talk about in this post.

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Winning the Back to School Dollar with Big Data Analytics

There are a few seasonal events that retailers count on to contribute the lion’s share of sales every year. The upcoming back-to-school shopping season is one such example. eMarketer recently reported the July through August period should generate $56 billion in e-commerce sales. This represents 16.5 percent of full-year online sales for the industry.

With so much at stake, you expect retailers to leverage all of the data and analytics at their disposal to capture wallet share amid a hyper-competitive marketplace. Clearly there is a need:

another recent eMarketer article reports just 25 percent of marketers are confident in their marketing mix.

More often than not, the challenge retailers face is identifying where to focus their analytic energies – those very specific use cases that differentiate and win many thousands of moments

of truth that lead to sales. These moments happen over a fragmented time horizon according to eMarketer’s research. Their conclusion: retailers need to be always-on when the shopper is ready to shop, or you risk missing that customer.

To execute a winning seasonal marketing strategy, retailers have the opportunity to optimize

the mix of media tactics, content, and channels on an individual customer basis. This extends to the planning and analytics of programmatic advertising.

Cloud and analytic technologies are now available that greatly speed the ingestion, analysis and operationalization of insights. Regardless of season, marketing leaders should partner with their analytics and IT teams to identify new ways of engaging customers in more relevant and effective ways. See this checklist to direct your planning.

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Making the Case for Better Retail and Consumer Goods Analytics

For several years, Retail and Consumer Goods industries have been converging on the same target – the consumer. Specifically, developing an understanding of consumers to inform marketing, product design and assortment, commerce channels, supply, service, and cross industry collaboration. Many have made moves in the right direction, but challenges remain.

Both industries continue to face price oriented buyers and declining loyalty. For branded goods suppliers, the bar is that much higher for creating compelling products that justify higher

margins. Trends have some looking for growth instead via mergers and acquisitions, the latest example of this being Heinz and Kraft. It’s arguable that this is a sustainable strategy for brands increasingly falling out of favor with consumers (even if international markets have proven growth engines for certain categories).

For retailers trying to transform into more attractive experiential businesses, the hurdle is just as great for consideration over lower cost channels. News this week that many retailers are dialing back discounts and promotions – sensing a recovering economy – suggests consumers accustomed to lower prices can be convinced to spend more. Absent substantial changes in the shopping experience, experts rightly question the approach, some calling it a gamble.

As shoppers, we are nearing the point of anticipating and preferring a personalized purchase path. The implications for retailers and suppliers are many. These include breaking through a crowded digital marketing landscape featuring many businesses targeting the same consumers and households. It extends to an almost individualized assortment and fulfillment capability needed to capture sales at a moving moment of truth.

There’s a case to be made for stepping back from what’s not working today, and consider new forms of analytics. Technologies and data sources exist that can illuminate ways to better serve consumers throughout the purchase journey. Industry trends suggest current approaches are struggling.

Sales Growth

Blame economic conditions or consumer price sensitivity, but sales have not been improving much in either Retail or Consumer Goods industries. According to this article:

Dollar sales of foods and beverages edged up a modest 2.1% to $470bn in the US retail market

in 2014, but unit sales (an indication of volumes) were pretty flat (up just 0.9%).”

While pure online retail growth continues to outpace traditional peers, omni-channel shopping behavior is having an impact (such as browsing online and buying in-store). Consider this:

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Online retail spending grew by 9 percent in the year to January to $16.6 billion, almost double

the growth rate of traditional bricks-and-mortar retailers. But the days of double-digit annual

growth rates for online appear to be numbered.”

Retail industry growth has been trending down, as shown by McKinsey, which also notes that prospects are brighter for retailers able to target segments that promise to outspend others (Baby boomers, Hispanic consumers and Millennials).

Products and Assortments

Category-creating new products are growth drivers for the Consumer Goods industry, but the difficulty of doing this well and the cost of failure remains staggering.

According to McKinsey, SKU proliferation has led to structural problems in how traditional retailers determine assortments. Keeping up with Amazon, for example, places many retailers at a huge cost disadvantage.

Amazon and other online pure plays exploit this -- taking sales away from bricks and clicks retailers amid a market of price sensitive shoppers.

Marketing

Marketing effectiveness across both industries has declined as promotion and mark downs reflect an almost systemic reliance on discounting. As a result, marketing costs for most exceed returns.

Value-based marketing is more difficult than ever when it depends on understanding cross channel shopping behaviors. Consumer use of technology is continually evolving. Leading retail marketers say keeping a pulse on this is their greatest challenge.

Collaboration

There’s no doubt that collaboration between suppliers and retailers has made great strides, but there’s a lot more to be done. Slow growth and continued pressure on better demand and supply synchronization demonstrate opportunities for improvement. On shelf availability remains a costly problem for both industries (in terms of sales and shopper satisfaction). For example, Walmart reports that in 2014 it lost $3B in sales due to out of stocks.

Of note, when technology and analytic competencies are out of balance between supplier and retailer, effectiveness suffers. The more dependent party pays for this disparity with greater concessions in the relationship.

Gartner finds it necessary for suppliers to accelerate Demand Signal analytic capabilities that extend beyond transactional, order and shipping data, to include new, prescriptive indicators like social sentiment, loyalty, trading partner promotional plans, and others. It’s essential that

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supply chain capabilities become a competitive differentiator for Consumer Goods makers, which will not happen via incremental improvements.

X-Commerce

In search of growth opportunities, Consumer Goods companies are exploring direct sales to consumers. As these efforts ramp up, there will be pressure to rationalize direct versus retail partner channels.

Retailers also have a rationalization challenge. The pressure is on to create a seamless shopping experience regardless of channel, making inventory and supply chain integration for brick/mortar and online sales essential. Some suggest right now this is table stakes for

competitive parity in retail, with the lens shifting next to how to effectively message and connect with consumers most relevantly around their omni-channel shopping journey.

Analytics Illuminate the Path

According to research reported in the October / November 2014 issue of RIS News, more than 20 percent of retailers and CPG brands will invest at least 10 percent of their IT budgets in analytics. With so much at stake, making the most of these investments is critical.

To get started, CIOs and analytics teams should approach their challenges with an eye on

attacking key processes, in collaboration with executives from other areas of the business. For retail and consumer goods organizations, you can imagine analytics informing all the functions which affect the consumer’s shopping journey.

From communication, to fulfillment and the post purchase experience, it’s the execution of activity across this process which separates a leading organization from the pack. Better analytics, leveraging current and new forms of data, can be the engine for solving many challenges in the Retail and Consumer Goods industries.

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Will CPG find the right balance between trade and consumer investments in 2016?

It’s been well established that manufacturer trade promotion budgets are considerable, reportedly the second greatest expense after cost of goods sold. These dollars target store and shopper interaction to drive sales and volume using incentives such as price reduction and coupons.

Retailers have come to depend on trade promotion for their bottom lines and that isn’t likely to

change. Their margins are even slimmer than manufacturers’. If anything, trade funds requirements will continue to grow as branded goods face pricing pressure from macroeconomic conditions and growing competition from retail private labels.

If you haven’t noticed, private labels are beginning to innovate and market themselves just like big brands — in the process taking share from manufacturer brands — while these same retailers continue to demand margin-eating trade funds and own the relationship with the consumer.

That latter point - the consumer relationship - has many consumer goods brands struggling to

balance trade promotion with direct consumer marketing investments. Will this continue into 2016?

Budget Right

I think the situation begs for diverting a percentage of trade and marketing funds to innovative data and analytics projects that attack many of the underlying problems branded CPG companies face. I often pose the hypothetical question, “If you were to start a CPG company today, you would probably start with an emphasis on consumers, their needs, wants, and desires – not your factory – right? You would make the requisite investment in data and

analytics necessary to develop innovative, differentiated and high demand products, before sinking untold millions into systems to manage manufacturing processes.”

I think the concrete answer to this question is one that marries the goldmine of consumer and shopper insights from every source available within and outside the company, with ERP/supply chain and detailed sales data from retailers and syndicated sources. Such a vision is one that companies in many industries are adopting, but in the CPG space starting with such critical business processes as trade and consumer relationships makes a lot of sense.

This integrated foundation then underpins and powers a real-time consumer facing business agile enough to counter trends hurting growth in the CPG industry. Insights are not just offered monthly or weekly, but daily, by the hour, by the minute if necessary.

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I’m not describing a siloed consumer database, demand signal repository (DSR) or ERP reporting system, but rather all of these in a single living record which ties insights together in ways not otherwise possible.

Justification

What if less than 1 percent of the 20 percent of revenue that comprise the typical trade funds budget were allocated to detailed, cross-company data acquisition, integration, and analytics?

To avoid a continual decline in share as store brands begin to innovate, while at the same time taking margin hits due to escalating trade promotion, the time is now to consider doing something differently in 2016.

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Optimizing a Complex Marketing Mix in 2016

The television spot remains a mainstay in many b-to-c companies’ never ending quest to reach millions of eyeballs, differentiate their brands, and ultimately increase sales. Meanwhile, all things digital have swept over most industries because that’s where consumers are spending more time at the expense of television.

In parallel to mass media, agencies acting on behalf of brand marketers execute all manner of digital marketing campaigns, but typically without an eye on capturing, integrating and

measuring these interactions. Amid these dual approaches to influencing consumer behavior, brand marketers struggle to apply highly organized, integrated and proven-effective Data Driven Marketing techniques. For many, it feels unrealistic, and seems to hamper the creativity at the heart of the most successful brands.

Progressive brand marketers recognize the opportunity to marry the brand storytelling exemplified in mass media with direct response marketing principles to develop measurable 1:1 connections with their consumers. This intersection of marketing disciplines is where Data Driven Marketing provides brand marketers and their agencies with a measurable storytelling platform.

When in place:

The quality of marketing content in addressable channels can be gauged by indicators of brand affinity. For example, how many consumers communicate with your brand through multiple channels versus one, how many consumers that you have direct relationships with express affinity in social channels and also participate in your loyalty program? How much content is shared, in either emails or social channels, and what is the reach afforded?

Responses to digital channels like web and email are recorded, evaluated and targeted for improvement over time. When enabled at scale, “test and learn” serves to make the most of your marketing budget by applying dollars to the programs that promise the highest response. Permission-based digital channels are addressable – meaning you have the opportunity to communicate with consumers on a one-to-one basis, measure response, and accrue knowledge about consumers that informs brand creative, segmentation and targeting.

The value of agency creative can be evaluated objectively for either addressable or mass media. Indicators of quality and response for addressable channels allow brand marketers to quantify the value of their agency investments. In the case of mass media, brand marketers are increasingly tying it to complementary digital activities such that they work in unison to yield an overall better result.

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Engagement and Sales

A primary benefit of Data Driven Marketing is that the dials on many of these codified consumer interactions can be tuned in-market to increase the likelihood of positively impacting sales. If you have engaged with your consumers correctly, sales should correlate. If sales remain flat or down, it indicates that something is amiss – you haven’t connected with the right consumers, your strategy is flawed, or your agency creative and execution is off. The key is to be able to turn these dials quickly to improve affinity with the right audience, which in turn should meaningfully impact sales figures.

The stakes for engaging well are high. A CMO.com article titled “20 Years After Peppers And Rogers, A Wake-Up Call For Brands” reported:

“According to a Janrain/Blue Research study from earlier this year, 98 percent of people receive

information and offers from brands that are simply not relevant. And the impact is beginning to

be felt, with consumers taking action on their annoyance. Nearly half of them report

abandoning brands after just two mistargeted communications. They stop using products,

unsubscribe from email lists, and abandon Web sites. This is obviously a big problem--

companies are potentially losing nearly half of their customers due to frequent mistargeting. It’s

a wake-up call to fix this problem or risk losing customers, prospects, and market share.”

Thinking about the value of Data Driven Marketing in this context sets the stage for embracing consumer engagement as an ongoing brand mission that can sit at the table among other measurable business processes like retail sales and supply chain.

Digital Marketing is Dead?

In September of 2013, Procter & Gamble Chief Marketing Officer Marc Pritchard said:

“Let’s celebrate the end of digital marketing. Let’s focus on creating the great ideas that move

people and build great brands. And let’s leverage the tools, platforms and technology to make

them bigger and engage with people like never before. Our brand building teams, our agencies

and most of the people who see our stuff and buy our products will thank us for it.”

He further said that P&G’s marketing teams no longer think of digital in terms of “the tools, the platforms, the apps, the QR codes, augmented reality, holograms or whatever is coming next” or as a “mysterious medium with its own set of metrics,” but for what it is: “a tool to build out brands by reaching people with fresh, creative, campaigns.”

Prichard reported that P&G campaigns were now formulated by starting with digital, then other channels and approaches added to the mix – or “digital back.” In this way, marketing teams are able to think creatively and not focus on the latest digital “shiny object.” One of Prichard’s colleagues, Roisin Donnelly, corporate marketing director and head of marketing at Procter & Gamble UK and Ireland, echoed this sentiment when she said:

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“As the consumer is spending more time on digital media, it will continue to be a growing part

of our marketing activity. However, it is complementing traditional media. In the UK consumers

are watching more TV on more devices than before and we need to be present where and when

they are most receptive there. Online newspapers and magazines are complementing offline

print. Digital radio brings more availability of more stations to more people.”

The result of a “digital back” approach ensures addressable and mass media channels leverage one another and are measurable in near real-time. A campaign for Braun shavers was cited by Pritchard as an example that employed digital and mass media channels in unison to exceed sales targets eight times over. In this instance, mass media advertising was added to the mix right after awareness was created among the target consumers through digital channels.

Every marketer wants to be more effective, and data and insights are the ways to achieve it. At the same time, brand marketers know that killer creative, done well, spurs consumers to action. Thinking about the Data Driven Marketing opportunity as a blend of time-worn brand marketing principles and direct response techniques is a good step to understanding how to succeed amid these seemingly conflicting disciplines. Will we see examples of this in 2016?

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Bringing Big Data into the Business

This week at the National Retail Federation Big Show in New York, the theme that emerged was very clear: how to deliver a differentiated experience across the shopping journey, no matter the channel.

With retailers focused on building out e-commerce channels for the past few years to address the Amazon challenge, what’s happened is the in-store experience has suffered. That’s where most brick and mortar stores make most of their money. Recent news and financial results

reflect the struggle – brick and mortar retailers are trying to find that secret sauce to win with connected consumers who buy in store, but are heavily influenced by digital.

So omni-channel is the new rallying cry to improve retail performance.

Walmart Moves

In case you didn’t notice, Walmart is in the midst of making organizational moves that I think reflect this trend -- integrating today’s separately operated data science function embedded within its e-commerce business, with the “on the ground” and “day to day” operational

analytics used by store operations every day (“Walmart Merging Arkansas, Silicon Valley Teams To Speed Up New Tech”).

Stated another way from a technology perspective: marry Big Data analytics focused on generating new (actionable) insights from online behaviors, with the foundational operational analytics that business managers use every day to understand how their division, store or staff is performing.

Another theme from NRF was how sensors and the Internet of Things were now capable of instrumenting the store much like a website – so the opportunity for data analysis across the

online and physical shopping experiences was now possible. This must be part of the motivation for Walmart to bring the smarts of data science closer to execution at the store level.

Implications

Throughout 2015, I’ve written about the need to operationalize Big Data insights. From a purely architectural standpoint, what’s necessary is an approach which blends Big Data with the day to day operational data management and analytics used to measure and power the business. Gartner has said as much based on their work with their clients.

Retailers face many challenges around this problem. Having placed bets on various technologies to embrace unstructured data and advanced analytics, as well as measure their core operations, retailers need to decide how to marry these capabilities both organizationally as well as technically.

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The probability of chaos resulting from these attempts is high. Consider that many data science organizations are based on open source technologies and require as much administration as

analytic talent to extract new insights. That contrasts with the commercial grade and legacy systems that today power the daily reports that managers rely upon, as well as serve many operational systems.

Retailers require a simplified approach to bringing two worlds together to unleash the value of Big Data insights. Developing and operationalizing analytics must achieve a level of commercial scale to ultimately make a meaningful difference to retail performance.

Consider too that business executives possess OpEx budgets to fund technology projects, but these must be “as a service” or cloud based. So expect to see progressive IT and analytics

groups leveraging cloud capabilities to work with their business counterparts to fund and execute projects focused on business-outcomes focused use cases.

Operationalizing Big Data Analytics has been a rallying cry for a couple of years now, but in 2016 it has to scale quickly lest retailers as a whole continue to struggle. I think Walmart’s moves reflect what we will see among many retailers seeking to extract more value from investments in Big Data capabilities.

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The Human Side of Retail Digital Transformation

I recently made purchases from two different well-known mass merchants. Both experiences seemed to highlight the main challenge and opportunity facing brick and mortar retail today.

One, a more upscale retailer, I visited to purchase a pair of shoes. In the shoe department were several clearly identifiable store associates who ignored me while I browsed the selection. I eventually found a shoe style I liked and asked one of the associates for my size. I wasn’t offered to have my foot measured and was handed the box, then left alone. The price was about $150.

My other visit, to a discount retailer, was to buy a few pairs of pants. In the men’s section were no associates to be found. I eventually located the style and size I needed. Total price was about $200.

In both cases I purchased. But was the experience memorable? Did my experience do anything to make me interested in returning again in the future? Not really. Since I made the purchases anyway, does it matter?

I think so. Know also that I really don’t enjoy shopping. I have the mobile applications for these retailers installed on my phone, but did not use them for these purchases. I didn’t do any research online beforehand either. Since both retailers were located close to my home and I wanted to shop as quickly as possible, I was satisfied with myself that I was able to get the job done quickly. I was happy with myself, but not feeling anything for either retail experience.

Sales growth is pretty flat for most retailers. My experience, multiplied by millions of consumers, would explain the problem.

The drive to serve customers well regardless of channel is leading many retailers to embrace

digital transformation. Yet my personal experience highlights a piece of digital transformation that should not be forgotten and which is decidedly human – the role of the store associate.

Retail is a service business but the “service” part of the equation seems to be lacking in many retail shopping experiences. Arming associates with tablets, applications and analytics to engage customers and be more helpful is part of the solution. The other though requires a cultural or organizational shift that considers store associates as ambassadors of the retail brand. How do you affordably hire, train and retain such employees for what many consider a low wage, low skill position?

To solve this dimension of retail transformation requires thinking about retail as a “business” in a different way. The Apple Store may not be a model that any retailer can embrace from an employment perspective but it’s a good start. It’s not an easy challenge, but I also think placing blame on tax laws and regulation is just another example of focusing on a symptom and not the core problem.

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Just as most retailers begin to ramp up investments in e-commerce, Amazon is doing the reverse. The reason? While e-commerce growth has greatly exceeded in store sales for many

years, the growth rate is slowing -- and Amazon is all about growth. Most of the money spent in retail happens in physical stores and that’s where Amazon is chasing customer walletshare next.

In case you didn’t notice, Amazon has been expanding beyond e-commerce into brick and mortar book and grocery categories. They are also exploring the creation of their own shipping and delivery service.

You may not know about Amazon’s business model whereby they happily fund new businesses at a loss with profits from other businesses. This helps them dominate markets by scaling fast

and crushing competition. If Amazon could make it easier and more helpful for a shopper like me, would I choose to shop an Amazon brick and mortar store versus other options? I probably would.

What helps Amazon succeed is their vast amount of customer data and the analytics they apply to that data. Other retailers have a similar opportunity, but arguably have an advantage in possessing the chance to engage customers face to face in the last mile of the shopping journey. Amazon’s not there -- yet.

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Busting Big Data Myths

Earlier today I sat on a panel discussion at the Retail and Consumer Goods Analytics Summit here in Chicago. Thematically, the event emphasized the need to experiment, fail fast, and approach analytics from a test and learn point of view.

That is one way of separating traditional reporting and analytics, from Big Data Analytics (a challenge cited during the conference last year when content revolved around staffing for analytics). This is also counter-intuitive for many people who find it challenging to consider a technology project without a confident ROI plan.

Our panel moderator, David Weinand, Vice President & General Manager, Technology Brands, Stagnito + Edgell, posed questions about analytic myths within the retail and consumer goods industries (and others too I expect).

I thought the questions were provocative and the conversation productive. Following are my thoughts on the topics we discussed if you were not able to be there.

Myth 1: Predictive Analytics is at the top of the analytics maturity ladder -- but no companies can achieve this maturity without millions in analytics budget.

Whether it’s predictive -- where you are looking to anticipate what might happen -- or prescriptive -- where you are seeking a recommended action to take to affect an outcome -- both are relatively advanced concepts. Many companies continue to struggle with operational reporting problems in terms of data currency and accessibility. This unfortunately oftentimes blocks thoughtful attempts to sketch out a strategy for Big Data. Companies in every industry now have the mandate to explore more advanced analytic methods simply because their competition is and is gaining an advantage as a result. Fortunately, it doesn’t take millions to get started and achieve the benefits of Big Data insights. The key is to focus on bite-sized business centric questions and leverage cloud wherever possible to speed up analytic projects.

Myth 2: To utilize analytics effectively, a large staff of data scientists and analysts are required.

If you attended the Summit last year, you heard from several speakers about the different analytical roles in their organizations, from analysts to data scientists. The skills you need depend largely on the business problems you want to address. While it’s true a data scientist can go off on his or her own to identify a problem or opportunity, that’s the exception to the rule. Today most successful analytics projects involve business people in the shaping and

execution of the projects. That’s how insights get put into operation and value is rendered. Last year one speaker said their larger challenge to the data science skill glut was identifying data driven managers. I expect this to be the case for many retailers and consumer goods companies. This trend is reflected in stories I hear about analytics projects failing when led by IT without much or any involvement from the business. One of the wrinkles here is the emergence

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of the Citizen Data Scientist who is enabled to become an active participant in data analysis. New software tools are emerging designed to help businesspeople engage in the analytics process beyond traditional dashboards and business intelligence.

Myth 3: For retailers, leveraging customer analytics to be able to market to customers based on behavior is an invasion of privacy.

This is a hot topic of conversation. I think there are several dimensions to this. On one hand, retailers and brands need to make clear to their customers how data is used. This renders itself in the form of a privacy policy or terms the customer accepts when opting into email communications, registering on your website or downloading your mobile app. Secondly, when marketed to at a segment level based on aggregate attributes, the “creepy” factor is less in play

than when very specific recommendations are made based on attributes that could only be associated with a particular customer (like a pregnancy if you recall that now infamous case). Regardless, the idea is to be helpful, respectful and transparent as you can be. So no matter how the analytics are derived, it’s in the application of that insight which determines how it’s perceived by the customer.

Myth 4: Consumer goods business executives believe that their analytics tools are their biggest tactical inhibitor, even more than resources or data quality.

I’m not sure business executives know what their inhibitor is, but it’s true that many consumer

goods executives, as well as those in retailers, are frustrated by the lack of value from their investments in analytics. Last year’s moderator cited research saying as much. Many still struggle with the fundamentals of operational reporting. What many companies lack is a data management and analytics strategy closely tied to business objectives and rendered in terms of specific business use cases.

Myth 5: Leveraging Big Data in the consumer goods industry is just 3-5 years out.

I’d say most consumer goods companies are trying to figure out how to take advantage of Big

Data Analytics to address their core issues around sales growth, collaboration, supply chain effectiveness and product innovation. Many are at different stages of maturity and progress, but all are making some moves in this direction. I’d say it’s a bit of a race for many to catch up with companies already well down the maturity curve.

Myth 6: The #1 analytics area that retailers will focus on this year is social media analytics.

Analytics related to Customer Experience and the factors behind it are a much higher priority. Consider the trend toward omnichannel business where customers are served equally well regardless of channel. There are numerous marketing, sales and supply chain issues that take

precedence over analytics focused on social channels alone. Social channel data however is a helpful part of these broader analytical problems. From a Big Data perspective, I think you need to consider how social channel data could yield a new insight when looked at in combination with data from other areas of the business. That is a bit different than focusing on metrics related to social brand sentiment alone.

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Myth 7: Getting to advanced analytics requires huge investments, which most mid-sized firms can’t afford.

With the advent of cloud computing, powerful Big Data Analytics capabilities are now available to companies both large and small. The requirement for skilled analytical professionals doesn’t go away though, and it’s important to start with a clear use case roadmap to make sure your dollars are well spent. The beauty of advancements in analytical software is that many of the pain points of projects are getting engineered out of the process so that teams can focus on asking questions more so than worrying about data wrangling and managing the overall environment.

Myth 8: The data-sharing collaboration between retail and consumer goods manufacturers has been steadily improving.

Honestly this seems to be at a standstill but innovators on either side are looking for ways to improve. On the consumer goods side I have heard of sales teams seeking analytics to inform their efforts to become whole store, cross category advisors to their retail partners. That necessitates closer data collaboration. On the retail side, the opportunities to pull supplier partners into the omnichannel fold through more targeted in store mobile marketing via Beacons also implies the need for greater data sharing and collaboration. Supplier brands often have decent consumer insights, better than their retail partners, and these can help drive higher performing marketing in the store. E-commerce and online advertising are two other areas that represent data collaboration opportunities.

Myth 9: Connected devices (IoT) is creating more data than is logically usable. It’s too early to make investments here.

The data from products with sensors and devices in the supply chain, as well as from in store beacons engaging shoppers or monitoring product shelf placement, is completely useable to inform a variety of business decisions right now. The key is to identify the right use case first that may necessitate sourcing data from sensors and smart devices. The challenge many

companies face is getting too fixated on IoT as a shiny new object and skipping the critical business use case.

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Big Data at Walmart #LoveData

At a conference recently, an attendee asked me what was so special about Big Data. Specifically, he asked what was different about Big Data from the types of analyses companies have always performed with their data warehouses and business intelligence software for years. Nothing answers such a question better than real examples, some of which I described to him.

Those of you in the LinkedIn ecosystem probably often ask the same question. It’s a good time

to review examples, given recent news reports about how elusive Big Data success can be. So I thought I would compile public sources of insights into exactly what market leaders are doing with Big Data to stay on top of their games and beat the competition.

Examples from leaders also illustrate lessons that any company less progressive can take away and learn from. This first post covers Walmart – others to follow.

Walmart is about as complex a company as you can find, so it’s not surprising to learn the company uses Big Data to many ends. Honing in on exactly what, why and how though, brings to light a very strategic and forward looking approach to taking advantage of new data and new forms of analytics. Consider this:

Walmart collects 2.5 petabytes of unstructured data from 1 million customers every hour.

Lesson: Even if you feel much less data intense at your company, understand there is likely an opportunity to collect much more data about your customers than you do today. It’s why many companies have gone down the path to build a data lake based on Hadoop to store data in any format and any volume over any number of years. The key question then is: what do you do with it?

Walmart’s main objective of leveraging big data is to optimize the shopping experience of customers when they are in a store, browsing the Walmart website or browsing through mobile devices when they are in motion.

Lesson: Have an over-arching strategy for how Big Data applies to your business, from which multiple business-focused use cases follow. In a retail or consumer market context, there is no better framework than the purchase journey.

Walmart employs a portfolio of use cases. Some of these include market basket analytics, product launches, product recommendations, shipping or fulfillment optimization and supply chain efficiency.

Lesson: Note that these are not sexy use cases. For Walmart and others, the idea is to pursue new insights without the constraints of the past – no constraints on data and no constraints on the analytics possible. At the same time, demonstrating value quickly is important at the onset

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of your big data journey. Developing new and improved versions of existing analytics are good places to start and don’t usually carry complex operationalization challenges. The question then

becomes, how do you do this most productively since the human element (i.e. data science) becomes as big a part of the solution as the data and technology?

Walmart also employs very sexy use cases.

Lesson: The beauty of developing Data Science as a core competency is that it sets the stage for developing “data products” with which your company can differentiate in super valuable ways. Walmart’s creation of a “Social Genome” is one example: The Social Genome product combines public data from the web, social media data and proprietary data like contact information, email address and customer purchasing data. This data helps Walmart better analyze the context of customer journeys.

Walmart is well past the experimental phase of Big Data and today expects these investments to pay off in big ways. Walmart observed a significant 10% to 15% increase in online sales for $1 billion in incremental revenue.

Lesson: Operationalizing insights is where the payoffs occur. So scaling the winning use cases to impact business performance is how leaders create a self-sustaining big data value system. It’s that scale piece which less progressive companies can learn from. For example, how do you scale the utilization of your data science team to surface more winning insights, more

frequently? Resolving this question needs to be part of your strategy lest you get bogged down in too few use cases and become discouraged.

Walmart is having a tough time finding professionals with experience in cutting edge analytics applications and working knowledge of data science programming languages like Python and R to build machine learning models. Walmart used the hashtag #lovedata for its recruitment campaign to raise its profile amongst the growing data science community in Bentonville and Arkansas.

Lesson: Walmart is able to do this because of their scale and reach, but less progressive companies would be challenged to crowdsource talent like this. It’s worth trying, but it’s equally important to think about how to take the scarce talent you do have, and make them as productive as possible. What’s become apparent, and is behind recent news reports, is that Big Data projects are complex. So anything you can do to reduce complexity while maximizing focus on a business outcome is worth your consideration.

The following was the single source of information for this post. Check it out for full details and stay tuned for more posts about the inner workings of Big Data leaders.

May 23, 2015 - How Big Data Analysis helped increase Walmart’s Sales turnover?

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Big Data at Starbucks #ThirdPlace

In the first post of this series, I described how Big Data was unfolding at Walmart according to public sources. For Walmart, a large part of their work centers on executing against the consumer’s shopping journey.

In this second post, I cover what can be gleaned about Starbucks’ Big Data efforts from the interwebs. Like Walmart, Starbucks holds its cards close to the vest when it comes to details.

What you can discern from public information about Big Data at Starbucks relates to their loyalty program, new site selection and store execution. All three reinforce the company’s longtime efforts to be the third place in consumers’ lives – at home and work being the first two.

Loyalty Program

I am a very loyal Starbucks customer, usually buying a cup or two every day. For some reason, I don’t get any offers pushed my way. I only receive “stars” based on how much I spend, which at some point can be redeemed for a free drink. What gives?

Well, Starbucks isn’t afraid of losing my business because I am so consistent in my behavior – so why waste time and money on driving business from me?

Instead, Starbucks has laser focus on cases where a once loyal or infrequent customer is not buying as much as they could, and attempts to isolate their efforts on these customers.

This data is used to deliver targeted advertising and discounts directly to the mobile devices of its customers. However, do not expect as a loyal customer that you will receive such a discount; Starbucks is not afraid of losing them so they are not willing to give them a

discount. Instead, the discounts are sent towards customers whose buying behavior shows that they may not be returning soon.

Lesson: A best practice use case I’ve seen emerge for direct digital marketing like this, as well as advertising, answers the question: “How do I avoid spending marketing dollars on consumers who will buy no matter what or will never buy no matter what I do?” That is a tough question to answer because you have to also understand what motivates an inactive customer to buy. It’s a great customer segmentation and personalization Big Data Analytics use case.

Store Site Selection

Figuring out the optimal places to open stores is an analytical question by definition. Starbucks has apparently made a science out of answering this question using Big Data.

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Starbucks has been very smart in its use of data analytics to not only determine optimal store location, but to customize its menu offerings by location and therefore capture significant value.

Starbucks is able to project foot traffic and average customer spend of a given location, therefore helping Starbucks to determine the economic viability of opening a store in that spot.

Lesson: The analytics here not only inform where to place a store, but how to operate that store based on the attributes of the surrounding community. That form of hyper-localization, to the extent it can be cost effective, is a concept any retailer can learn from to stand out from the competition.

Store Execution

Traditional Business Intelligence (BI) is often confused with Big Data Analytics. BI is typically

backward looking and helps to report what happened. Big Data Analytics is about taking the data underlying that analysis, possibly adding other data sources, and identifying useful trends, patterns and prescriptions related to how business processes can be improved.

What’s interesting about Starbucks is that the company seems to have applied Big Data Analytics to its approach to Business Intelligence.

All that data is used to at least make the lives of the store managers a bit easier. In the past they used to provide 300 reports to its managers on a regular basis in order to evaluate and improve their stores. Thanks to all the data, this has been brought down to 11 most important key performance indicators, including cleanliness of stores or customer satisfaction, which can be downloaded by the managers.

Lesson: If your company suffers from analysis paralysis due to a proliferation of reports, key performance indicators and scorecards, consider applying Big Data Analytics to identify the

metrics which matter most – down to the store level. This is a very hard question when you are not certain which levers are the most important. Make answering this question the use case.

What you see from Starbucks is a very practical approach to Big Data aligned with the fundamentals of their business. This isn’t a coincidence, but rather a deliberate strategy that other companies can model when taking their initial steps into Big Data.

The following were sources for this post. Check them out for full details and stay tuned for more posts about the inner workings of Big Data leaders.

Starbucks, The World Largest Coffee Shop, Grinds A Lot Of Data

Starbucks: Brewing up a data storm!

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Fishing for Big Data Success

There’s no shortage of technology available right now to help companies take advantage of big data. No matter how you categorize the providers in this space, it has all the characteristics of an early stage market yet to shake out to clarify options for buyers.

Beyond the challenges of understanding who does what and why, there remains the opportunity to take advantage of new data sources to uncover new insights that can make a real difference to your business. The examples are out there, but tend to be scarce.

The most tech-savvy and data science-centric companies have little problem sorting through the details and driving value. For the majority of commercial entities, the challenges are immense. What I’ve found helpful is to view big data not so much through a data management lens, but an analytical one – to answer the question “what new insights does big data offer my

business and how do I approach the opportunity with confidence?” There are many supposed experts standing ready with prescriptions for assessments, roadmaps, and strategy. Where big data projects have been successful, I have found a series of elements come together to set a company on the right path.

NEED

Executives in the industries I work with are hungry for new ways to make significant improvements to their businesses. In Retail and Consumer Goods industries, incremental improvements that move the needle are also desirable, especially when a percentage or two adds up to millions in new revenue or cost savings. The key is to identify a business need, and more specifically a sponsor who will champion the use of big data analytics within the company. A lot has been written about the cultural and management challenges related to big data adoption, which leads back to one or a few individuals who embrace the opportunity. They are hungry to do things differently.

SKILLS

Data science skills, when combined with a strong sense of business, are in high demand. The reason is that these are usually a required aspect of any big data analytics project. From identifying and preparing multi-source data for analysis, to the application of various analytical and statistical methods, companies need these smarts to unlock the value potential of big data. In some cases, a company may have dedicated analytics or insights groups, sometimes multiple ones aligned to different areas like customer, consumer, shopper, category and supply chain. In

others, they may need help. In either case, companies often desire an independent view and expertise based on experiences within and across other industries. This places a mandate on technology and services providers to recognize the whole solution need. A fine yet important point, is that companies should think through ways to scale, productionize and speed the path to value for big data analytics projects. Bottom line: you need someone who understands how to bait a hook and find the best spots to fish for insight.

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DATA

The data sources which fuel projects cannot be overlooked in terms of their availability, format, cleanliness, location, security, and privacy. Among the most interesting sources to be explored are ones that reside outside the direct control of the company. For both Retailers and Consumer Goods companies, these sources include social media, data generated by digital agencies, syndicated providers and DMPs (Data Management Platform providers). For Consumer Goods brands, there is the added challenge of obtaining and making sense of point of sale, loyalty and other transactional sources provided by their retail partners. Marrying these sources in helpful ways with market basket, shipping, inventory and financial data is the launching pad to big data success. A diverse data ecosystem in which to fish for new insights is crucial.

TECHNOLOGY

To organize and prepare data for analysis -- at scale across many use cases -- high performance and interoperable mixed workload data management, analytics, discovery and business intelligence capabilities are essential. The state of the market today is such that there are many specialized companies addressing various aspects of big data analytic project requirements. All things being equal, most companies will prefer integration and simplicity whenever possible. This extends to leveraging the cloud to alleviate the burden on customers to staff the more administrative aspects of big data projects, and instead focus on how to differentiate themselves with analytics.

OPERATIONALIZE

Big Data insights must be put into practice to achieve the value of the investment; either automated or delivered in the most relevant manner to decision makers. From a process point of view, what usually works is ensuring that a proof of concept is more of a “productionized concept” put to use in a targeted business process. In this way, the champion I described in the first point of this post has a hook to hang her hat on to demonstrate value to her peers and the

company at large. Absent this step, momentum is lost. This is the point at which many projects fail to reach commercialization within the business to truly scale the value of big data analytics.

What has been your experience recently or over the past several years with Big Data analytics? Do you agree with my characterization of the elements of success?

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Do Something Different in 2016

Much of the value in approaching any Big Data Analytics project is tied to the question: how will doing something differently positively impact my business? That “something” is a decision or collection of decisions informed by new kinds of analytics on many forms and sources of data. For business leaders who recognize the potential, the challenge is how to go about justifying the effort – the business case.

Today, most businesses leverage some form of analytics to measure their operations,

processes, people and results. The promise of Big Data hype is that whatever analytics are employed today lacks the ability to see beyond the barriers of the current state to a new universe of data and insights. The Big Data reality is a bit more complex and depends on a variety of factors.

Business managers should understand their companies’ philosophy and strategy on analytics. Such attempts often take the form of a short interaction between a businessperson and someone in IT that plays out like this:

“The Business”: tell me what’s possible? IT: tell me your requirements? Result: gridlock!

In cases like this where business leaders haven’t engaged in a more productive dialog with IT, the value focus of Big Data typically sways in favor of efficiency and cost savings for IT services delivered to the business today. These “use cases” are very much transparent to the business in that they involve drawing out costs to meet current Service Level Agreements. While these are certainly examples of “doing something different,” they don’t offer any real improvements to the business.

A simple framework to justify exploring a business value oriented Big Data Analytics use case begins by level setting: does your company have a strategy to leverage Big Data?

If “Yes,” seek out answers to questions such as:

What are some example projects conducted with the business? What are the steps and typical time involved to complete a project? How are insights put into operation and measured?

Except for progressive adopters of Big Data, most businesspeople will find few example projects

focused on business problems, lengthy project timelines involving many steps and skillsets, and projects that were stamped science experiments without any operational aspect. Quantifying the value of accelerating the delivery of projects focused on use cases that map to improving business metrics is a good first step to doing something different.

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If the answer is “No” with regard to Big Data strategy, seek out answers to these questions:

Do current analytic capabilities support the acquisition of new data from sources outside the company like marketing agencies and social sites?

Can this data be combined with internal sources like customer transactions for analysis? Do our tools and people support analytic projects based on data from these various

internal and external sources?

Many companies struggle with issues around operational reporting and feel constrained by this when contemplating doing something new with Big Data. The beauty of cloud capabilities available today that support the entire Big Data Analytic process means any company can quickly spin up projects without feeling weighed down by legacy tools and technologies. The business case for improving an existing process or metric is therefore more easily made.

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Prioritizing your Big Data Use Cases

If you’ve read anything of mine before, you know I write a lot about the importance of Big Data strategy and how it’s essential to getting started down a winning path. Absent a plan or understanding of what Big Data means to your organization leads to what many companies are now discovering: a disconnect between their investments in Big Data technologies, and the realized business value.

Where I’ve seen this NOT be the case, and business value IS realized, is when companies approach their opportunities from two perspectives:

Recognize the value inherent in taking advantage of all the data available to your organization. Much is said about “small data” and getting value from current data sources before exploring new ones. Fact is, it’s not difficult to incorporate new sources into analytics if you can source the data. It’s really the only true method of developing a unique insight your competition may not be able to replicate. Know too, that many data science experts are discarding the concept of sampling data to fuel projects, to instead leverage a complete representation of the data. That by itself means you have to be ready to examine a lot of potential data sources.

Think about use cases in a portfolio context. You have a fixed amount of talent, budget and bandwidth, and want to pursue the use cases that have the most near term return. You want to trade away projects that run their course or don’t pan out quickly. In this case, you need to identify your options – what projects to pursue?

This is where prioritization becomes key -- which analytics present the greatest opportunity for business value? To begin, think about decisions in the business that could be improved today without considerable re-engineering of a business process. The rationale?

Companies at the start of their journeys have to create quick wins, promote them, and get

more data driven managers engaged in the process. This is how the value of Big Data analytics

scales. The quickest projects are often ones aimed at drastically improving existing decisions, such as how to optimally allocate a marketing budget across numerous media and channels.

Implied in that simple case is the idea of operationalization – or putting the analytics to work against a business process. This is where the value renders itself.

Using this thought process helps lay a foundation on top of which more complex use case can be pursued which may require non-technical changes to the way your company does business – such as the rapidity of replenishing products at the shelf and all that means in terms of supply

chain processes. To get the most from Big Data investments, I think this is a logical, yet under-utilized approach to prioritizing use cases.

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How to architect your talk with a businessperson about analytics

In a prior life not long ago, I was part of a team studying inhibitors to the adoption of Big Data Analytics – not traditional BI and operational reporting, but leveraging as much data as necessary to develop and execute against new insights that drive business value. Top performers are doing this – and so what is so special about them?

What came of this from interviewing many customers, consultants and analysts was very clear: there were simply too few businesspeople who understood the concept, let alone what may be possible.

So the inhibitors were less technical, and a lot more human, organizational and skills oriented.

Contrast this with the emergence of the hybrid businessperson/analyst called the “citizen data scientist,” who is data-oriented and desires better access to data and more helpful analysis tools. This person may not be a line of business executive or manager, or work in traditional IT.

This new, data-driven role, should be viewed as an exciting development for companies struggling to get more value from their investments in Big Data capabilities. They represent a

key skillset that must be leveraged and understood within the context of the strategy a business employs around Big Data.

Right or wrong, most companies have embarked on acquiring new analytic technologies like Hadoop from an IT or technical perspective – it’s new, it’s technical, and initial applications of it tended to support frustrations IT had with legacy data management and analytic platforms. Improving efficiency and reducing cost are solid justifications.

Unfortunately, these metrics don’t really help improve the top line, which is where executives are now expecting Big Data investments to impact. That is leading many companies toward a

tipping point where they will either get it right or wrong with respect to realizing business value from Big Data Analytics. The consequences could be pretty significant, especially in the Retail industry where I tend to focus.

I talk about analytics as a process with multiple steps and that involves different people. IT most certainly always plays a role, but without the support of a line of business sponsor to identify an opportunity or problem, and access to skilled support resources like your citizen data scientist, projects tend to fail to yield the hoped for value. It’s collaboration among these individuals, at scale, that helps an organization “commercialize” analytics to the extent it can be as repeatable a process as any other in the business.

To reach that point, I talk about a portfolio approach to analytic use cases that roll up to support a business process a businessperson not only appreciates, but views as critical to their success. A process like the consumer journey, from consideration to purchase and the post-

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purchase experience. There are numerous decisions within this journey that could be improved with access to new data sources and new forms of analytics.

Collaboration among a business sponsor, an analyst or data science professional, and IT, can leverage such a business process construct to identify a “suite” of use cases to then prioritize and attack.

Prioritization can happen along a number of dimensions:

Business impact or value: how much can a decision or process be improved relative to the current state? What does it mean in hard currency?

Data availability: what data sources are required and can they be accessed in a timely manner?

Skills required: based on the desired outcome, what analytical skill will be required? What modeling type will be necessary? Do you need help from a third party to execute this?

Timeline: how soon can the project be executed and operationalized? If you are just starting off, focusing on analytics that support an existing decision process can make more sense than one that implies re-engineering a process.

A simple representation of these considerations serves as a very clear way of communicating an actionable Big Data strategy to executives. That is very important as these same executives start to wonder if Big Data hype can be realized in terms of tangible business value.

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The Art of the Doable

Those of us who work in tech, especially Big Data, love to talk about “the art of the possible.” It’s a great way of framing an innovative idea for a customer that is difficult to visualize and understand against the backdrop of all the priorities competing for attention.

The key word in the phrase is “possible,” because something has the potential to happen if something changes (usually in the customer). I was first introduced to this idea about five or six years ago and still find it helpful to connect the dots between technology and business value.

Yet in the Big Data space, I think there’s a new phrase that customers are coming to respond to better – the art of the doable. What’s the difference? Look at the definitions of the words possible and doable:

pos·si·ble /ˈpäsəb(ə)l/ a person or thing that has the potential to become or do something.

do·a·ble /ˈdo͞oəb(ə)l/ within one’s powers; feasible.

While one’s a noun and the other an adjective, I think there’s another important difference

between the words. What’s “possible” may be both too abstract and also require too much change in the customer’s business to be realized within a reasonable timeframe. What’s “doable” should involve as little change as possible to allow the customer to act literally now – it’s within their power with the right support. These are not mutually exclusive. It speaks to the challenges that all but the most sophisticated Big Data adopters are struggling with.

For every Big Data pundit like Tom Davenport that write about cognitive computing and streaming analytics, the same pundits (Tom included) find that companies in many industries struggle to get businesspeople engaged in the most fundamental Big Data projects (Big Data meaning new analytics on current and new data sources targeting a measurable business problem or opportunity).

My sense is that frustrated or confused businesspeople have seen too many “art of the possible” presentations, and not enough “art of the doable” proposals. The difference lies in how actionable the idea is, given many variables – such as the readiness of the customer to engage in the project given their maturity, skillsets, partners, available data and ultimately budget.

Many businesspeople and their peers in IT need bite sized, yet modular ways of ramping up their Big Data capabilities before approaching large scale, “big bang” projects that cascade

throughout the company. When you think about how cloud computing offers the ability to quickly try out a new application, many business and IT people are becoming conditioned to expect easily provisioned solutions over complicated projects that could span years.

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What I find helpful to address this expectation is to gain agreement on the distinction between an application and a Big Data solution. It’s all technology, and for a long time buyers have come

to expect references from vendors as a way to reduce risk and make a more confident “buy” decision. I think Big Data is a bit unique, however, in this respect.

Consider that while companies achieving value from Big Data readily admit publicly to using certain technologies, they tend not to share how it was applied to their business (in terms of the way data is acquired, organized, modeled and operationalized). That’s because analytics is how leading companies differentiate and compete. For many companies, the analytics and approach behind them are trade secret. Therefore, the litmus test to make a “go” or “no go” decision needs to expand beyond an existing reference. In fact, customers who understand their Big Data opportunity expect this!

This is where “art of the doable” becomes critical at a time when companies are interested in moving quickly, in the cloud and testing a lot of ideas. Businesspeople exploring Big Data use cases should challenge who they work with to come up with realistic and executable projects - which should of course be compelling and delivered in a unique fashion. These ideas could form part of a larger “possibility,” but the ideas themselves are bite sized, modular and focus on very specific business outcomes.

Some of the ways to identify and prioritize these analytic building blocks include:

Business Value

Identify a business process, metric or strategic initiative which can be measured and compared with others. Focus on use cases that represent the greatest value but refine your list based on the other factors which follow below.

Data Availability

Identify the data that relates to the use case you are exploring. Consider the availability and

currency of the data. Some use cases may require data beyond the four walls of your business so you must understand what’s involved to include them in your analysis.

Skill Required

The ultimate goal is to produce a new insight, which depends on having people skilled in preparing and analyzing multistructured data. Sometimes, your most skilled analysts are capable, other times you may need a true data scientist or consulting firm. Either way, the complexity of the analysis must be considered as well.

Time to Execute

This is perhaps the most important variable. Attention spans are short, so it’s essential to focus on initial use cases that can be executed and operationalized quickly, measured and socialized with business sponsors. Speed is becoming a key success factor in companies leveraging

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analytics for competitive advantage. This has implications for the underlying capabilities used to obtain, prepare, model, and analyze data, then produce and operationalize new insights.

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3 ways to gain an edge with Big Data Cloud Services

One:

Execute a business-outcome focused use case, end to end, with little to no dependency on your company’s technology architecture – for example, marketing mix analytics which predicts and prescribes the optimal actions to take with customers throughout their purchase journey. If you believe that you do this today, either with your own technology or via a services company, challenge yourself to leverage new data sources and new analytics to improve.

Two:

Evaluate how an analytic capability in use today (like integration or modeling) can be improved, executed faster and yield better insights when applied to a business outcome focused use case – for example, market basket analytics which looks at factors behind large baskets, not just the products which comprise the baskets. If you think you have market basket nailed, challenge yourself to identify optimal actions you can take upstream and downstream of the transaction to improve value and frequency.

Three:

Add something new to your analytic competencies, such as machine learning, quickly, and improve the results of an existing business outcome focused use case, or execute an entirely new one – for example, analytics which determines individualized incentives for customers participating in a loyalty program. Loyalty programs in many retail segments have figuratively died. Those that continue and flourish look beyond purchase-based rewards to the steps which can be taken to produce satisfying shopping experiences.

The Big Data market is as wide as it is deep. It’s confusing frankly, even for those like me who

live it on a daily basis. For customers, it has to be infuriatingly difficult to see past the glossy pitch to how precisely we can unlock an insight that makes a real difference.

The vastness and speed of the open source community is a big part of the problem. Businesses without the resources to maintain fluency with all the pertinent open source projects cannot partake in the benefits of Big Data at the scale of companies like Amazon and Netflix. The cost to entry is simply too dear for many companies.

To scale the benefits of Big Data requires a balance of open source with commercial software which leverages open source components. Companies with few resources or just beginning their journeys need some level of abstraction from the complexities of open source.

Unfortunately, cloud computing in the Big Data arena has been defined mostly around the storage and management of data at scale, cheaply. I think that undersells the value potential that cloud brings to the challenges most companies report gaining value from Big Data

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investments. Namely, make the most use of scarce talent, collaborate with the business on use case definition and operationalization, and embrace test/learn, data experiments and fail fast approaches.

It’s not just where you store and manage data that matters, but rather what use cases are supported, how quickly they are executed, and the business value of new insights created and put into operation. That requires end to end capabilities which extend beyond storage, to all aspects of analytics such as security, governance, integration, enhancement, discovery, modeling, visualization and activation.

Unfortunately, many companies have not approached Big Data from this perspective and I think are struggling as a result. They are often stuck working with dozens of open source tools which consume too much time and resources to think about much else.

If you’re like most managers, you believe analytics represents an opportunity to do something differently with an eye on competing better. Apply this logic to any of the three scenarios at the start of this post to chart a course to gaining an edge on the competition.

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Organizing for Analytics

A recap of the 2015 Retail and Consumer Goods Analytics Summit

Analytics, especially the Big Data variety, is a hot topic these days despite the hype over the past several years. In fact, Gartner finds that the hype has crested, as mentioned during the Retail and Consumer Goods Analytics Summit in Chicago this past week. This jibes with analytics guru Tom Davenport’s recent assessment that we’ve moved past generic overinflated expectations. Instead, most business leaders now acknowledge the value of Big Data analytics

given reported successes in many industries. The lens is now focused on the question, “what’s the business value for my organization and how do I get started?”

The majority of content at the Analytics Summit was about the emerging discipline of Data Science. Implied in the representation of both retail and consumer goods organizations was the

topic of cross industry collaboration. Theme-wise, I’d say the event was intended to help retail and consumer goods companies understand how to organize for analytics, an observation I raised in a panel discussion on the final day.

To this end, market heavyweights such as Walmart and Macy’s were represented on the

agenda. Juxtaposed with their expertise and processes were the majority of companies in these industries that are far less sophisticated. Conversations with attendees seemed to bear this out.

Well-funded and visionary companies are at the bleeding edge of leveraging multi-structured data to drive their businesses forward. Meanwhile, many companies are struggling with the most fundamental operational reporting issues. Forrester recently confirmed this, based on research into certain retail and consumer goods companies showing that few understood Big Data, let alone were on a path to success.

I came away from this event with a point of view that could have been a session on the agenda

– based on your analytics maturity, there are actions you should take and for which technologies and approaches are available to embark on a journey to becoming a data driven organization.

First, understand what Big Data represents to your business, from both efficiency and effectiveness perspectives. Some executives still view Big Data as inexpensive storage or a chance to move reporting workloads off a proprietary data warehouse – all in the name of saving costs. These are great use cases, but the real opportunity with Big Data are the questions you can ask of your business that current capabilities cannot support. It was clear that Walmart and Macy’s have embraced this concept to their respective advantages.

Second, for those still struggling with operational reporting issues, you must make it a priority to invest in Big Data analytics. The question then becomes how to proceed with laser precision, when faced with the immensely more mature leaders in both retail and consumer goods industries. It was said during the conference that competition for data

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science skills was fierce. It was also said that most companies have staff right under their noses with many of the competencies of a data scientist. Organizationally, IT is not the place for these people to live. Perhaps more so than other functions, those in data science straddle the business and technology sides of their companies. They view themselves as strategists, not technologists, and do not want to work in an IT organization. One presentation suggested successful companies were establishing Centers of Excellence for data science. That’s a pretty important idea to understand since many companies view reporting and analytics as IT capabilities.

A now well-known McKinsey report highlighted the thousands of data science jobs that will go unfilled because of a lack of skilled talent. Walmart Labs’ data science leader Esteban Arcaute raised this stat to illustrate the larger challenge of developing many times more managers who understand data driven decision making. It’s consistent with my view that business sponsorship is critical for data science to become more than a short-lived experiment.

Cross industry collaboration was a topic raised during a panel I was a part of on the final day. The interesting thing about collaboration is that it implies some degree of equality between participants. When there is a huge disparity in capabilities, one party is likely to have an advantage over the other, making a true partnership challenging. For suppliers to Walmart and Macy’s, the situation must be intimidating. They have no choice but to embrace analytics beyond operational reporting to avoid the race to the bottom with respect to price. The situation begs for a precision effort around developing and applying data science to the highest impact use cases. A roadmap if you will.

Identifying and recruiting business leaders who understand the value of analytics is critical. To approach this conversation, it’s helpful to align with the commercial execution of the business in a way that clarifies the challenge and opportunity. I prescribe a focus on the consumer’s journey, and all the actions that both retailers and suppliers take to influence this process apart and in collaboration with one another. It’s a Big Data problem by definition. There are many use cases that span this process that can serve as a precision guidance system to unlocking the value of Big Data analytics. It also helps develop the data driven managers organizations require on top of the data, analytics and technical capabilities needed to realize the value of Big Data.

Back to the topic of skills, companies just recognizing the potential of data science should consider looking at the analytically-gifted individuals in their companies today to head up the effort. Perhaps labeled an “innovation” team, they could tap into a budget earmarked to augment their skills as needed. This function over time could grow to support many aspects of the business. In one session, it was said that analytically mature companies were outsourcing less of their data science in the name of intellectual property and trade secret. This makes absolute sense, but I also think there could always be a need to augment internal talent. This is different than buying access to third party analytic IP for projects such as marketing mix or response propensity models.

The “data lake” came up during the panel discussion as well. This is a great idea, but only within the context of why a business would care about it. To me, it’s about viewing your data as an asset, or as capital, and therefore it needs special attention. To extract the most value from your data, you should manage it as a secure unit, and expose its value appropriately to users throughout the analytic value chain; from those requiring access to daily sales reports to those mashing up multi-structured data to understand

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and improve a critical customer process. This is the path that the most advanced companies are marching on.

I think a more important collaborative concept than retail / supplier is the internal partnerships among IT, data science and managers necessary to become truly data driven. Getting that mix right may be the big competitive battleground in all industries this year and into 2016.

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The Data Scientist is Here to Stay

Some people are on the fence as to whether the role of Data Scientist is a fad that will pass when packaged advanced analytic capabilities become available to less skilled analysts or line of business managers. I actually think we will see both co-exist and relate strongly to one another because of how really successful analytic projects unfold within most organizations.

Coming back to the first point, while it’s true that the role of Data Scientist has emerged almost as fast as that of “webmaster” 20 years ago, I think it has quite a bit more staying power --

especially within the retail industry. Consider the following excerpt from a June, 2016 McKinsey article*:

“Amazon employs more than 1,000 big data professionals; traditional retailers, too, have made

big data investments. The top ten US retailers now have an average of almost 70 big data

analysts on board.”

So the top 10 retailers in the U.S. have an average of 70 “big data analysts” on staff – that’s simply code for Data Scientist. That’s a lot of brainpower to help retailers recover from the

deep discounting of the recession that has transformed consumers into brand agnostic deal seekers who spend more wallet share on experiences than physical goods.

Few retailers have the resources to compete with Amazon in data science talent, yet have little choice but to hire skilled professionals. Competing on Analytics isn’t just a slogan; it’s a reality for all industries. It’s true that hiring and retaining data science talent is challenging for retailers, so scarcity is simply a fact of life for the foreseeable future. So how do you actually then “compete on analytics?”

Focused Experimentation!

Progressive adopters of Big Data Analytics (advanced analytics/modeling on varied data) have come to recognize the importance of test and learn when it comes to use cases. It’s simply unrealistic to view analytic projects through the same lens as a traditional IT project with a Gantt chart, multi-quarter schedule and ROI calculation. I suggest that this point could very well be behind struggles many retailers have had getting value from their Big Data investments. This is what I heard during the recent Retail and Consumer Goods Analytics Summit in Chicago.

Executives who hire people with the word “scientist” in the title need to recognize that experimentation is implied in the job. The challenge is to figure out how to strip out as much

uncertainty of analytic experiments as possible without limiting the value of exploring different hypotheses aimed at real business problems (like getting a read on who your true, real valuable customers are, and how best to serve them with content and products they will buy at a profitable price, for example).

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To do that, consider analytics as a process or capability your organization needs to develop and improve over time. It involves multiple departments and stakeholders, not only Data Scientists.

It includes people in IT who manage the systems which create the data necessary to support an analytic use case -- like sales transactions, budgets, call center interactions and loyalty program data. It includes third parties who may hold valuable data about your customers and markets (for example, social channels, your agencies or syndicated data providers). It includes most critically someone in a line of business who sponsors the project and commits to operationalizing an insight so value can be measured, learnings recorded and if helpful, repeat the process.

The potential for chaos and uncertainly is high with this apparent complexity. Given the financial challenges retailers continue to face, you can be sure many are struggling to “get it

right.” Getting it right means figuring out a way to accelerate the process that focuses on the highest value use cases.

Cloud First!

Most retailers have made technology investments to support data science. This includes hardware and software to account for data acquisition, integration and management, data discovery and wrangling, predictive modeling and business visualization. Some commercial, some open source.

For those organizations yet to move beyond traditional data analysis – like operational reporting from a data warehouse or data mining on transactional data – they face questions about how to take their next steps with technology (including where and how to source Data Science talent).

For both, the most ideal situation is to source any and all capabilities necessary to execute a true Big Data use case quickly, securely and reliably – in the cloud. Why cloud? Cloud offers numerous benefits. Among these are typically better security than what can be found in your data center today, but also the ability to provision what you need and when you need it, and

pay for it with OpEx budget dollars that line of business managers control. Business sponsorship is now mandatory for analytics projects to succeed, and there’s no better way to pull in a business sponsor than having them fund a project.

This is now a reality for many organizations, but challenges remain. Collaboration is critical. IT almost always plays a role and should want to – it’s among many ways they can plug into the strategic direction of the company. Data Science is a unique role and the “tools of the trade” tend to be statistical and modeling tools like R. But they should spend more time talking with line of business managers and working on answering questions and telling data stories, than figuring out how to acquire and integrate data from many places.

Meanwhile, line of business managers use visualization to monitor and interact with data to identify and take action on new insights, and should be directing projects with a business question or hypothesis. The interactions among these different roles must be orchestrated in a way that use cases can be executed in a confident and expedient way.

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This can certainly be accomplished using a variety of technologies stitched together, but I think a “whole is more valuable than the sum of its parts” approach that emphasizes integration is

better. Amazon has more than 1,000 Data Scientists. Focusing scarce talent on the highest value aspects of Big Data Analytics needs to take priority over managing technology and data, which is a big part of the Cloud value proposition. Especially when open source capabilities that your Data Scientists are already familiar with are part of the solution.

For organizations early in their analytic journeys, cloud will be particularly valuable since they should focus less on the enabling technologies, and more on identifying the internal skills or external resources necessary to support the experimentation at the heart of Data Science.

* Playing catch-up: How to partner with the retailer of the future, June 2016, McKinsey

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Smartsourcing your Marketing Analytics

“Give a man a fish and you feed him for a day; teach a man to fish and you feed him for a

lifetime”

That sums up the state of marketing analytics, as described in a recent Gartner report (Market Guide for Advanced Analytics Service Providers for Marketing*). Due to a lack of skills, marketers spend a good portion of their budgets outsourcing analytics. According to that Gartner report:

“A notable share of the analytics budget — more than technology and nearly as much as

internal talent — goes to outside experts. The majority of mature data-driven marketers expect

external sourcing to grow over the next two years, and 30% of them expect to decrease their

internal team size, taking more advantage of the efficiency, scale and expertise of service

providers.”

This is surprising. Many of these same companies are striving to become more data driven and thus making moves to build analytics into their corporate DNA by hiring data scientists. In the

retail industry, for example, the top 10 U.S. companies employ an average of 70 data scientists according to recent McKinsey research. You can be certain the situation is the same in many industries and even smaller companies are making their initial hires in data science.

Consider too that companies realizing business value from Big Data are typically not buying off the shelf services and pre-defined models. Instead, they are doing the work themselves in order to create competitive differentiation. They are in essence trying to “fish for themselves,” be self-sustaining, and rely as little on third parties as possible.

Bafflingly, Gartner finds “concerns such as ongoing costs, data security, black-box models and

lack of transparency, and loss of competitive advantage are all valid reasons organizations may choose not to use external service providers. However, for most marketers, these considerations will not deter investment.” These are not concerns most C-Level executives treat lightly.

There seems to be a disconnect when you have marketers outsourcing analytics on one hand, while on the other they have access to talent to drive these projects more efficiently, and ultimately yielding greater differentiation. What’s happening? There are a couple possibilities:

1. Marketing doesn’t believe that their data scientists, or advanced analytics staff, have the right skills and business understanding. This is certainly a possibility, especially for companies less analytically mature. What smart companies are doing is providing budget to emerging data science teams to augment their growing talent with needed skills on demand – but not outsourcing what should be intellectual property. It would be wise for marketing to ensure data science understands their use case requirements to

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inform which outside skills are required. The bleeding edge companies have the opposite challenge: data scientists cannot find managers with an analytical mindset to work with. Those who are may simply be unaware of the skills available, so communication and collaboration on a high value use case is often the solution.

2. Marketing doesn’t believe that their data scientists, or advance analytics staff, are able to execute projects in a timely way. This is also possible, considering the amount of time and effort required of most analytics projects on data management and data preparation. Services firms can strip out some of this pain by building repeatable components around common use cases. While it speeds the project, the opportunity for differentiation is largely lost as the services company pursues scale of its own sales over creating customer-specific insight. Another dimension here are new ways for marketers to become a value added piece of the analytics process, assuming the role of “citizen data scientists.”

Smartsourcing is a good way to think about resolving this conflict:

Acquire the talent on demand you need to support a use case your team cannot, and / or

Acquire a cloud service to account for a part of the analytics process you lack or which takes too much time.

Marketers owe it to themselves and their organizations to work on a pilot with their data

science team, and if need be, smartsource human or cloud capabilities. eMarketer summed it up well when they wrote: “Measurement can be a challenge for some organizations, and therefore, testing and learning is important.” Test and learn is becoming best practice for companies gaining the most from their big data investments.

Clearly there is an opportunity to do something different, especially for retail marketers. As reported in June 2015 CMO Council research:

“Lack of measurement can hold marketers back and could be a reason why many are not

adopting omnichannel strategies at all. Only 11% of CMOs worldwide said they have a

sophisticated omnichannel strategy implementation at their company, which is defined as cross-

channel integration, including between online and offline.”

* Market Guide for Advanced Analytics Service Providers for Marketing

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A good day for a Data Scientist is a good day for everyone

Extracting meaningful new insights from data is not typically something which can be bought off the shelf. It’s the reason many businesses struggle to get the value they expect from investments in Big Data technologies, services and people.

Some of the most progressive retail Data Science leaders cite the inordinate amount of time their teams spend on lower value tasks, than on ones that can have the biggest impact. This is of course for those companies that have hired Data Scientists (or people versed in advanced analytic methods like machine learning).

At the root of the problem, these leaders identify the structure around which analytic use cases unfold. There are lessons to be learned, and which less advanced companies just making their first moves into Data Science can benefit from as well.

What a good day looks like

In an ideal case, someone with the explicit or implicit role of “Data Scientist” would be engaged on a daily basis with a business problem or challenge. Such as “How can we improve customer

satisfaction?” This is a fairly broad question that could involve a variety of people from the business, and touch many systems and sources of data to fuel the exploration of the question. Properly enabled, the Data Scientist could rapidly explore the various obvious and unobvious factors that correlate to customers having a satisfactory shopping experience which leads to loyal behavior - or the inverse. The Data Scientist would also have time to partner with someone in the business connected to this metric that operationalizes the insight.

What a real day looks like

From what I have been able to discern, the above scenario is a rare occurrence. In reality, many

Data Scientists attempt such projects, but are mired in managing the operational workflow of extracting and cleaning data, developing models, testing them, and developing useful visualizations to help business leaders understand the insights. These are all critical tasks, but when the discrete steps consume so much time, it leaves little quantitative or creative energy to work on the business problem at hand.

It’s for this reason many companies well down the path to developing a true Data Science function struggle to not only achieve some measure of value, but scale what value they can demonstrate. It’s why there’s such great demand for ways to streamline and speed the analytics process. Particularly when the capabilities can be sourced as cloud services and

provisioned on demand, as needed. Taking Big Data Analytics from a cottage industry to a core high value business competency necessitates a focus on speed and scale.

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For those on the Big Data Fence

There are lessons here too for companies considering their first hires in Data Science, or working with a consulting firm. Realize that the talent pool is growing and continues to grow (The Myth of the Data Scientist Shortage). Meanwhile, right under your nose are analysts, customer insights professionals and other “quants” in your company today who could form the basis of your new advanced analytics or Data Science function. Seeding such teams with your best and brightest, then allocating budget for them to source specialized external skills as needed is becoming commonplace.

Consider the experience of the most mature companies. Look at how important it is to focus less on managing the analytics process, and instead working on problems while involving the business. Doing this well is unlikely to work with current approaches.

Cloud services that reflect the entire analytics process offers a “fast button” to supporting analytics in a way that frees your scarce Data Science skills from the sink hole of low value work. The worst possible situation for the Big Data market is for companies to get discouraged at the value potential, while Amazon, Netflix and other companies doing it right run all the way to the Big Data Bank.

The real beauty of cloud services is the ability to quickly augment what you have today, explore new ways of handling existing analytic requirements, or add net new capabilities. This flexibility

promises to bring Big Data business value to more companies, so that Data Science pros can focus on business problems, not process.

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Now Hiring: Retail Marketing Scientist

This week at Oracle OpenWorld I had the chance to give a talk about the benefits of the next generation of Customer 360 analytics – one that brings old concepts of the customer profile into an era of any data and new forms of analytics, to execute better against the customer’s journey. You might be surprised how relevant this use case is to retailers and other businesses today.

Key to doing this well is to make the most of your scarce analytic talent, so the effort focuses

more on the problem and marketer, and less on managing the steps necessary to go from data to insight. It’s this productivity gap which companies must close to gain the most value from Big Data Analytics.

Many are addressing the challenge with integrated cloud services that bring the power of open

source technologies to those with fewer resources than the big guys. Even the big guys are struggling and looking for ways to improve their analytic process, and get away from lower value tasks to focus on business problems.

For retail, the stakes are high for getting Big Data right. While many struggle, Amazon is making

moves into physical retail. Just announced this week was the expansion of their pop up store business.

You have to recognize Amazon is all about growth and e-commerce sales are leveling off. To grow, Amazon sees a huge opportunity to take advantage of traditional retailers still struggling to understand how to execute in a retail world where consumers are accustomed to discounts, have low brand loyalty, and prefer spending on experiences over products.

We all know everyone for the most part shops across channels. The question is how do you construct your unique shopping experience given the challenges your business faces? Old ways

of measuring your business and analytic methods constrained to data not fully reflective of your customer relationships are not going to help.

McKinsey recently reported that on average, the top 10 U.S. retailers had 70 data scientists on staff. In my experience, retailers both large and small are making moves to develop “data science” or advanced analytics as a competency different from existing departments that report what’s happening in the business. These new roles are instead focusing on what will happen and how to prepare and execute in a prescriptive way, without limits on the data or analytics which can be applied. The skills are not as scarce as they once were.

This week during OpenWorld, I was kindly emailed a job opportunity with a major apparel retailer. I would describe the role as a “Retail Marketing Scientist.” It’s just further evidence that retailers are investing in analytics.

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This isn’t a marketer or an analyst, but a blend of the two. Such roles are becoming increasingly important as the focus turns to what use cases and business problems to solve, and bringing the parties together necessary to execute.

I’m quite happy in my role with Oracle, plus this position requires relocation to the company’s main office which I am not interested in doing. If interested in the role yourself, contact me and I will refer you to the recruiter.

In the interests of privacy I have removed from the job description the company’s name, location and the actual title.

Retail Marketing Scientist (my term, not the actual position title)

We are currently looking to grow our team of data and analytics experts to support our marketing efforts. We are looking for Retail Marketing Scientists to drive our strategy, consult with marketing teams, lead experiments and find solutions.

Whether you design clothes or business strategies, crunch numbers, lead projects or write code, we have a place for you.

We are building a team of data and analytics experts to support the company’s marketing efforts. We need experienced people to help the different business units transform into data-driven marketing organizations.

A day in the life...

Consult with marketing teams and lead the development of reporting and measurement roadmaps.

Lead projects to create reports and dashboards providing marketers with access to the data they need to make the right decisions at the right time.

Provide advanced analytical support to answer critical questions related to Marketing performance.

Provide consulting on Design of Experiments (DoE), including advice on statistical power.

Diagnose portfolio performance and identify opportunities. Influence others through effective communication and subject matter expertise, with a

high level of proficiency crafting presentations to support sound, objective-based decision making.

Execute and drive projects to completion in a timely manner and work collaboratively with cross-functional partners across the enterprise.

You own this if you…

Bachelor’s degree in math, statistics, operations research, economics or related quantitative discipline with experience is required. (Advanced quantitative degree preferred).

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7+ year’s professional experience in analytics or other quantitative disciplines (5+ years of professional experience for advanced degree holders).

Experience in managing projects and analysts in delivery of analytic projects. 5+ years of experience in use and application of SAS/SQL/R OR Python to extract and

manipulate data (advanced skills in programming preferred). Experience with reporting and visualization tools. Hands-on solutions architecture experience – designing from ETL to end user output. Prior experience managing and measuring digital advertising campaigns and social

media campaigns/programs. Enjoy developing brand engagement metrics. Passionate about retail.

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