business analytics€¦ · ““fifty- eight percent of organizations now apply analytics to...

Business AnalyticsOpportunities and Pitfalls

IntroductionWe at DecisionPath Consulting have advocated for nearly a decade that business intelligence – which encompasses business analytics – should be seen as a profit improvement tool and a potential source of competitive advantage. More recently, major consulting and IT services firms have started advancing the argument for wider adoption of business analytics. And they have been joined in that effort by leading IT analyst firms, who work closely with major business intelligence (BI) and analytics packaged software companies to understand the vendors’ product road maps and communicate their value propositions to companies that subscribe to their IT market/product analysis services. In this BI White paper, we will share what we hope will be a useful perspective on the opportunities – and potential pitfalls – associated with investing in business analytics. We will also outline steps you can take to avoid those pitfalls and optimize your investment in analytics.

Opportunities & Pitfalls �Opportunities & Pitfalls�

“ …companies need to decide whether they want to be innovative and imaginative …or whether they can safely be content as followers. The innovator path requires greater investment, focus, and risk-taking, with the potential for greater profits.

From The Profit Impact of Business Intelligenceby Steve Williams and Nancy Williams, (2007)

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“ “Fifty- eight percent of organizations now apply analytics to create a competitive advantage within their markets or industries.

From Analytics: The Widening DivideBy MIT Sloan Management Review and IBM (2011)

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Business AnalyticsOpportunities and Pitfalls

By Steve WilliamsPresident, DecisionPath Consulting

“Business analytics are

data-based applications of

quantitative analysis

methods“

What are “Business Analytics?”Simply put, business analytics – or “analytics” for short – are data-based applications of quantitative analysis methods in use in businesses for decades. When I was in business school in the mid-1980s, we were required to take a course in quantitative analysis. One book we used was Quantitative Methods in Management, which was written in 1977 by four Harvard Business School professors. And I could point the reader to dozens of books that apply various quantitative analysis, operations research, and discrete mathematics methods to specific business domains, ranging from sophisticated customer segmentations and predictions of customer lifetime value to demand forecasting and supply chain optimization. So the field of analytics, per se, is not new. Rather, tried and true quantitative analysis methods have been implemented as packaged software applications and bundled together into “analytics platforms” that can be leveraged to build a wide range of company-specific analytical applications that address common business challenges. SAS Institute (founded in 1976) and SPSS (founded in 1975) are well-known examples of companies that sell analytics platforms, and they are many others.


“Business Analytics” and “Business Intelligence”: What’s the Difference?

We define business intelligence as the use of business information (data) and business analyses to support business decisions in the context of core business processes that drive profit and performance.5

� Williams, S. and Williams, N. The Profit Impact of Business Intelligence, Morgan Kauffman 2007


Standard, pre-formatted reports for backward-looking analysisUser-defined analyses based on standard input variables selected by the userAd hoc analysis by power usersScorecards and dashboards (i.e. performance analysis information)Multi-dimensional analysis (a.k.a. On-Line Analytical Processing)Alerts (which analyze performance variables, compare to a standard, and report variances outside defined performance thresholds)Advanced analytics (typically backward looking and descriptive)Predictive analytics (typically forward looking and predictive or prescriptive)

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Typical business intelligence (BI) applications – all of which leverage historical data – include:

Simply put, BI has always been about analysis, and business analyses come in a wide range of types and uses, from simple analyses such as accounts receivable aging reports to the sophisticated anti-fraud ana-lytics used by major credit card companies. Our focus for this article is on the BI subcategories “advanced analytics” and “predictive analyt-ics” – which we will refer to as “business analytics” or just “analytics.”


Business Analytics Opportunities

Given that business analytics are simply data-based applications of long-established quantitative analysis methods, there are a large number of potential opportunities for leveraging analytics to create competitive advantage – and ultimately to drive profit improvement. Tom Davenport and Jeanne Harris provide some very useful examples in their book Competing on Analytics, published in 2007. Other examples may be found in The Profit Impact of Business Intelligence5 - where we discuss how advanced analytics and predictive analytics can be used for such purposes as:

2 Ibid. Chapter 7.

Customer segmentationCategory managementRisk analysisInventory optimizationDemand forecastingSales trend analysisStatistical process controlCash flow forecastingMarket analysis

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Business analytics are essentially are toolkit that sophisticated business analysts can use to inform a wide range of business decisions in different parts of companies – all with a view toward increasing revenues, reducing costs, or both. And while these opportunities are many, so are the potential pitfalls, which we will illustrate with a case study and then discuss.

Business analytics are essentially a toolkit used to inform a wide range of business

decisions.

Readiness Factors for BI Success �Readiness Factors for BI Success�

Packaged BI applications for such purposes as sales analysis, pricing analysis, marketing analysis, financial analysis, and supply chain/order analysis;Packaged BI platform tools; Packaged multi-dimensional analysis technology;Packaged advanced/predictive analytics software;Pilots of an advanced/predictive analytics application; andConsulting services and training

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When BLDR embarked on its journey in 2010, it had very little experience with data warehousing, business intelligence, or business analytics. There was no enterprise data warehouse, though there were some reporting databases within some of the business units. To learn more about business analytics, BLDR turned to leading BI/analytics software vendors and well-known consulting firms. After several months of conversations, they sought and received various proposals from these sources. Based on conversations, the various vendors responded by proposing to sell/license BLDR various combinations and permutations of:

Business Analytics Strategy - A Case Study�

DecisionPath was recently engaged by a company – whom we’ll refer to as “BLDR” - to help define its strategic approach to analytics and to develop a roadmap to guide their course forward. BLDR is a publicly-traded leader in its industry, with over $2 billion in sales. The company operates mainly in the United States, and it sells over 20,000 SKUs to over 80,000 customers. The goal of its analytics initiative is to enhance its competitive advantage, and thereby increase profits and its stock price. In that way, BLDR is absolutely typical of the many companies that want to leverage business analytics to drive profits.

� The information presented here is disguised, but the company and situation are real.

Readiness Factors for BI Success �Readiness Factors for BI Success�

Given that the vendors and consultants were given an unstructured problem, it is not surprising that their bids were diverse, with prices ranging from $700,000 to over $4 million. Among the more instructive proposals was from Vendor X and included:

Approximately $400,000 for enterprise licensing of packaged advanced/predictive analytics software;Approximately $100,000 in annual maintenance for the advanced/predictive analytics software; Approximately $600,000 in consulting services to develop a non-production predictive analytics pilotApproximately $1.5 million for packaged BI applications marketed as analytics;Approximately $250,000 for annual maintenance for the packaged analytics software; andApproximately $700,000 in consulting services to implement packaged analytics software for one of the BLDR business units.

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The total of the above proposal elements was approximately $ 3.5 million. While BLDR is a large company, the pricing variations and diversity of proposed approaches motivated BLDR to seek an independent assessment, for which DecisionPath was selected. We helped BLDR sort through its strategic options for deploying analytics, and they ultimately chose a lower-risk, lower-cost approach. That being said, we will use the BLDR situation and the Vendor X proposal to illustrate some of the strategic choices and potential pitfalls associated with enterprise deployment of business analytics.

We helped BLDR sort

through its strategic

options for deploying

analytics, and they ultimately chose a lower-

risk, lower-cost approach.


Business Analytics and Potential Pitfalls

There are a variety of potentially successful approaches to just about any significant business opportunity. Examples include the “ideal” approach, the low-cost approach, the least change approach, the fastest time-to-value approach, the low-risk approach and various permutations thereof. From a business analytics strategy perspective, the proposal from Vendor X presents some interesting considerations and potential pitfalls, as described below.

Potential Pitfall #1: Lack of Clarity Regarding Investment Hypothesis

The term “analytics” is becoming ubiquitous in the BI vendor, IT analyst, and BI/IT consulting community. In the case example, Vendor X proposed that BLDR invest:

$2.2 million up front and $250,000 annually thereafter for packaged software applications for financial analysis, sales analysis, and purchasing analysis and the consulting services to implement the packaged applications; an

$1.0 million up front and $100,000 annually thereafter for advanced/predictive analytics software and the consulting services required to develop a non-production predictive analytics pilot.

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Examples include the “ideal” approach, the low-cost approach, the least change approach, the fastest time-to-value approach, the low-risk approach.


Being new to BI and analytics, BLDR was unclear about the meaning of the term “analytics” and the value propositions associated with the proposal. More specifically:

BLDR did not know how to determine whether $2.2 million was a fair and reasonable price for the packaged BI applications being sold as “analytics” and that consisted of pre-defined reports;BLDR did not know how to estimate the cost of the up-front data integration work they would have to do, which was not included in Vendor X’s proposal;BLDR did not know how to determine whether $1.0 million was a fair and reasonable price for the non-production predictive analytics pilot;BLDR did not know how to determine the extent to which the packaged BI applications being sold as analytics (the pre-defined reports) and the predictive analytics pilot matched their actual business requirements for BI and analytics; andBLDR did not know how to determine how much business value would be created as a result of the $3.2 million investment proposed by Vendor X

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Based on these factors, it is fair to say that BLDR did not know if the “I” in ROI was reasonable, and they had no idea of the “R” in ROI. It is a situation of information asymmetry, and all the economic risk lies with the purchaser.

Opportunities & Pitfalls 11Opportunities & Pitfalls10

Potential Pitfall #2:Lack of Clarity about Deployment of Advanced/Predictive Analytics The argument put forth by Vendor X and others is that advanced and/or predictive analytics have been proven to create value, and that purchasing a large number of licenses up front would allow BLDR to rapidly change its culture by widely deploying analytics across the enterprise. While that approach could have merit in some circumstances, it is fair and useful to understand other strategic options for deploying analytics. Simply put, analytic capabilities can be expensive and therefore risky for companies that lack appropriate experience or a supportive culture. At BLDR, a company with no enterprise data warehouse (EDW), limited BI experience, limited in-house expertise in sophisticated quantitative analysis, and no serious competitive threats, the proposal was that BLDR invest approximately $400,000 in enterprise analytics software licenses and $600,000 for consulting. Key considerations that were in BLDR’s interest to investigate included:

whether enterprise licenses were required up front, or whether a time-phased approach of licensing a few desktop analytics packages for roughly $12,000 should be used to test the use of advanced and/or predictive analytics to create business value at BLDR;which business processes would BLDR seek to improve via leveraging sophisticated analytics, and how much process change would be involved in order to create business value;

how would advanced/predictive analytics be used within BLDR, i.e. by 4 to 6 domain experts who would develop the analytical insights, predict the improved results, and then disseminate suggested process improvements to create value, or by a wider population who would need tool training as well as training in quantitative analysis; andwhether development of the non-production pilot could be accomplished more economically by using an independent consultant or academic with relevant domain expertise.

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Opportunities & Pitfalls 11Opportunities & Pitfalls10

The proposed approach was again a situation of information asymmetry, with the economic risk lying with the purchaser. While an argument can be made for a more aggressive roll out, BLDR is under no serious competitive threat, and thus a more measured, lower cost and lower risk approach could also make sense.

Potential Pitfall #3: Lack of Attention to Strategic Barriers to BI and Analytics Success

While every company has different strengths and challenges when it comes to BI and analytics, we’ve identified 5 strategic barriers that can cause companies to struggle with capturing the demonstrable business benefits.5 Based on our BI strategy experience over the past decade, we’ve noticed that:

� Williams, S. “Five Barriers to BI Success and How to Overcome Them” Strategic Finance, July 2011

Confusing terminology can make the value of BI/analytics hard to determineThe mission and strategic importance of BI/analytics may be unclearThere may not be a clear link to business strategy and critical business processesUpper management may not have created a sense of urgency to deploy BI/analyticsInternal people, processes, and technology may not be aligned

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While not all of these barriers were present at BLDR, enough were that it created a substantial risk that an investment in advanced and/or predictive analytics may not have created business value commensurate with the $1 million up front investment.

Ultimately, there is no silver bullet when it comes to analytics – or BI in general. While there are great products available, companies still have to do the hard work of figuring out how they want to use analytics to have a business/competitive impact, and they still need to build the right analytics skill sets and culture. We’ll discuss how to meet the challenges and avoid the pitfalls in the next section.

Opportunities & Pitfalls 1�Opportunities & Pitfalls1�

Business Analytics: A Framework for Avoiding the Pitfalls

Because advanced and predictive analytics are a subset of business intelligence (BI), the same strategic considerations come into play when determining the appropriate investment level and best road forward for your particular company as it seeks to leverage analytics to improve business performance. Figure 1 below shows these strategic considerations, and they are discussed briefly thereafter.5

� This discussion is adapted from Williams, S. “Five Barriers to BI Success and How to Overcome Them” Strategic Finance, July 2011. The article provides a deeper discussion of the barriers and how to overcome them.

F ig u re 1 . A n alytics S trateg y : L in kin g to B u sin ess S trateg y an d P ro fit Im p ro vem en t

Industry Characteristics & Com pany Business Strategy

Strategic Im portance of Analytics

Analytics M ission

Analytics O pportunities :o Increase Revenueso Reduce Costso Im prove Perform ance

Analytics Barriers /Risks :o Business Barrierso IT /BI Barrierso O rganizational Barriers

C ritica l B u s in ess Pro cessesth at Im p act Pro fits

Im p act

Im p act

A n alytics S trateg y

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5


Based on our extensive BI/analytics strategy work with leading companies in many industries, we believe it is important to approach formulation of a business analytics strategy from two primary perspectives:

A top-down perspective that determines an analytics mission based on a company’s business strategies and the strategic importance of analytics given the way the company elects to compete within its industry or industries; andA bottom-up risk-reward perspective that identifies specific analytics opportunities related to critical business processes that impact profits (rewards), and that takes a hard-nosed look at the business, organizational, and IT barriers to being able to succeed with analytics (risks).

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These perspectives are shown in Figure 1. The five colored and numbered circles within Figure 1 correspond to five strategic barriers to analytics success.

The first barrier occurs when business people don’t understand what analytics are and how they can improve processes that drive profits. The second barrier arises when a company has not conducted a structured opportunity analysis that links the specific use or uses of analytics to critical business processes in a way that business leaders can understand and validate. The third barrier occurs when companies have not determined a suitable analytics mission for their company, i.e. whether the company needs to have industry-leading analytics that confer competitive advantages, or whether competitive parity or even lagging capabilities would be appropriate. The fourth and fifth barriers arise when business people don’t accept responsibility for analytics and lead its adoption, and when IT does not align its strategy, systems, processes, and people to support the business in its quest to leverage analytics to drive profits.


All of these barriers, plus the many tactical and operational barriers that we’ve chronicled elsewhere, can be readily identified and addressed via a well-structured analytics strategy. Further, a pragmatic analytics strategy helps to build buy-in from business and IT leaders, a key prerequisite for funding. Of these barriers, we believe the lack of an explicit analytics mission that is accepted by top management may be the most damaging.

If the company has not determined how strategically important analytics are, it is difficult to establish a burning platform, to decide what level of investment analytics merit, and to determine what degree of change to IT policies, methods, and processes are in order. Further, it is hard to define an analytics strategy that is suitable if there is no defined and accepted mission. The investment pattern and pace depends on the mission. If the mission is simply to avoid impeding efficient and successful execution of the business strategy, that requires less investment and urgency that if the analytics mission is to create a competitive advantage. Companies tend to drive change effectively if they understand the importance of changing, and the other strategic barriers are more readily overcome if analytics has a defined mission.

Business Analytics Strategy

The path forward toward effectively leveraging analytics depends on where your company stands today. To avoid the common pitfalls, and depending on the analytics mission, your company can take the steps identified in Table 1 as part of formulating a business analytics strategy.

The steps identified in Table 1 are straightforward, and there a proven methods for accomplishing them. These are essentially business tasks.


TABLE 1: Analytics Strategy Considerations Based on Company-Assigned

Mission for Analytics

Company Analytics Mission

No Defined Mission

Internally Neutral

Analytics capabilities

do not impedebusiness

execution

Internally Enhancing

Analytic capabilities

enhancebusiness strategy

execution

Externally Neutral

Analytics capabilitieson par with competitors

Competitive Advantage

Analytics capabilities superior to

competition

Steps to Consider1

Educate executives about profit impact/competitive implications of analysisDetermine strategic importance of analysis for company or industryDecide on Analytics MissionPerform structured opportunity analysis to link use of analytics to core business processesAddress barriers and risks and develop roadmap to achieve Analytics Mission

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Perform structured opportunity analysis to link use of analytics to business strategies and core business processes through which strategies are achievedAssess current state analytics capabilities in relation to analytics mission, analytics opportunities and ability to execute business strategyDefine analytics capabilities gaps and prioritize roadmap forward based on potential business impact, technical risk and other relevant factors. Align analytics roadmap with broader BI strategy, architecture, and roadmapExecute projects, manage change, and measure analytics adoption and impact

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Same as for Internally Neutral and Internally Enhancing missions, plus benchmarking capabilities against competitors (if known or knowable) or against known analytic capabilities within the same or other industry.

1Adaptable based on company’s current state of analytics maturity


That being said, the more ambitious the analytics mission, the more important it is to consider analytics in concert with and as part of a broader BI program. While a technical discussion of data architecture is not the intent of this paper, it is well-known that systematic application of quantitative analysis, operations research, and discrete mathematics methods requires historical information about business transactions, customers, process performance, costs, product performance, and other relevant business variables. Essentially, advanced analytics deliver descriptive information about what has happened in the past, which information is then used to predict future outcomes and to prescribe future business actions that should be taken based on risk-adjusted economic or business objectives. To achieve these purposes, business analytics draw on the same data required to enable other styles of BI, including reporting, ad hoc analyses, scorecards, dashboards, and multi-dimensional analysis. Accordingly, we also recommend that your analytics strategy consider data architecture.

Summary

Many have argued that analytics is the next competitive frontier. We generally agree, though we believe many companies will need to walk before they run with respect to becoming analytics-based competi-tors. With that in mind, we recommend that your company develop an analytics strategy as the key first step to ensuring that you capitalize on the opportunities, avoid competitive threats, avoid common pit-falls, and invest in business analytics at a level and pace that is suitable for supporting your business strategy


About DecisionPath Consulting

DecisionPath Consulting is a recognized leader in leveraging business intelligence and data warehousing technologies for large and mid-sized organizations in a wide range of industries. Our mission is to guide our clients along the path to impactful uses of information and analytics.

Since 1999, DecisionPath has been at the leading edge of developing custom business intelligence solutions – leveraging our consultants’ deep knowledge of business analytics, data warehousing, business intelligence and performance management – to help a wide range of companies improve their core business processes and increase profitability.

Want more information?

If you want more information about developing a strategically sound analytics program, and how to leverage information to improve the bottom line impact of BI, please feel free to contact us:

Email: [email protected]: (301) 926-8323Web: www.decisionpath.com

business analytics€¦ · ““fifty- eight percent of organizations now apply analytics to...

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