Operational Analytics: Best Software for

Sourcing Actionable insights Ian Tomlin, Workspend Inc. 22nd July 2013

Actionable Insights are those views of data that cause managers to ask new questions about how

processes work and take action. They differ from traditional key performance measures and daily

operating reports that focus on delivering a picture of progress against a strategic objective, operating

budget or forecast. What software is best for your business to source these game-changing perspectives

of your enterprise?

…

There are many software applications and tools to source reports and views of data that it can become

despairing difficult to judge which is right for your business. Many of these tools serve a specific genre of

the art of information management, and prices vary from a few pounds to many thousands. In this article

therefore I give some pointers to the types of tools that are available to business people now and how

they help to create a game-changing view of your business world of data; help buyers to understand what

they can expect from the various types of tools they can employ.

Actionable Insights are a relatively new topic of conversation though people have been sourcing reports,

scorecards and dashboards for decades. For a while now the IT industry has been talking up the idea of

‘Business Intelligence’ (BI) without really describing what it is, or how it helps people in business to get

their jobs done better. From the deployments I’ve directly been involved with while working for NDMC,

US Tech Solutions and Workspend, the key areas of BI are:

1. Enterprise Performance Management – Scorecards, dashboards and alerts that report on

progress against strategic business objectives, normally focused around Balanced Scorecard

principles and methods. Essentially, once the objectives have been used for more than 12-

months, managers find they can quickly glance across a dashboard and see that all of their key

performance indicators are green, not red and then focus on ‘new issues’,

2. Daily Operating Controls and Forecast Reports – A range of reports to qualify how business units

are progressing towards their daily and cumulative targets, giving managers the opportunity to

take action on operating behaviors and sub-optimal performance before it’s too late to do

anything about it.

3. Operational Analytical Tools That Present Actionable Insights – Ad hoc or structured reports

designed for the purpose of revealing ‘new questions’ that demand managers to question norms

of behavior and seek to improve processes.

4. Social, Community and Predictive Business Intelligence – Dashboards and reports that enable

communities to share their insights and predict what might happen next in a sector, industry,

team or community. Increasingly, these tools source real-time insights from data that has hardly

had time to rest in a database somewhere!

Actionable Insights are a hot topic because of a fundamental shift that’s occurred in the application of

Business Intelligence thanks to recent developments and new learning lessons in cloud computing and big

data. For years, the general approach to Business Intelligence has been to create a ‘new data structure’

that pre-processes data views so that users could instantly access their favored reports and data views

without having to wait for computers to crunch the numbers.

This is not the first time that technology vendors have presented data harvesting and analytical tool-kits

in the business-to-business market: In the late 1990’s the first generation of operational analytical tools

emerged in the market under the banner of ‘enterprise mashups’. These products included offerings from

Corizon, Just Systems, Twinsoft, Nexaweb, Serena, JackBe, Encanvas (Mashups), Interneer and IBM (IBM

Mashup Center) and they provided the means to blend together disparate sources of data (and applets).

These solutions pre-dated the advent of cloud computing and ‘big data’ so while mashups were predicted

to generate millions of dollars of revenue they never achieved high levels of adoption because buyers

simply didn’t understand what they were for. It’s very difficult for buyers to consider or apply innovations

that have no ‘common reference point’ or ‘basis of understanding’ – which is probably why people still

talk about mobile phones instead of portable mobile computers even when most Gen-Y users hardly ever

make a call – hence, the enterprise mashups generation of analytical apps never went main-stream.

The two-tier strategy of employing transactional systems (and databases) and then adding a layer of

Business Intelligence tooling is now breaking down because cloud computing and web-born technologies

provide vendors with smarter ways of working with data. Database technology and thinking has moved

on. More recently vendors, like SAP with their SAP HANA proposition, are introducing database engines

designed to both support operational transactional systems AND provide effective tooling to master

analytics WITHOUT needing to create a separate Business Intelligence data warehouse.

In the last few years it’s also become much easier to access vast amounts of third party data on ‘the cloud’

and vendors have realized they can produce ‘new value’ by harvesting and analyzing operational data with

tools that no longer require data to be pre-purposed, reshaped or built into a data cube before users can

get their hands on it. Products like Locationary (just bought by Apple), Tibco Spotfire and Encanvas

BusinessIntel enable analysts, Business Intelligence practitioners and occupational professionals to

harvest data in flat file, XML, WSDL, ODBC, CSV and other file formats to then build views of data on the

cloud that can be easily expressed in charts, visualizations and scorecards. In many cases this data may

be sourced from in-house business data repositories and administrative systems and enriched by third

party external sources such Consumer Data providers, Postcode databases, Googlemaps and

OpenStreetmap.org. In addition to structured file formats, documentary content can also be used with

the aid of natural language search tools. Thought-leading institutions and research centers like the

University of Sheffield are also working on capturing photographic content by providing tools to analyze

the imagery captured in photographs!

Actionable Insights are important to businesses because they drive decision making and improvement.

Gaining a richer perspective of how an organization operates – and the environment or market within

which the enterprise operates – gives managers the ability to see beyond the spreadsheets and

dashboards that give answer to questions they already know.

Operational analysis tools harvest, present and share actionable insights in ways that enable managers to

build new connections across their data, and third party data. This enables richer profiling of customer,

better ways of making use of on-the-job know-how, simpler sharing and load-balancing of resources,

fewer defects brought about by a clearer appreciation of process pipelines, better quality data, richer

profiling of customers and better personalization of customer offers to make interactions more

meaningful and valued by customers.

But you don’t need to spend a fortune to start using Actionable Insights. Here’s a brief summary of the

sorts of tools you can use. The type of tooling that’s right for your business will depend on the use case

you have in mind, the amount you’re prepared to spend, anticipated data volumes and the complexity of

your data environment.

And a few important notes…

These are only my opinions and examples that I know or have some experience of – I’m sure there

are many more out there if you research the topic online!!

The nature of summarizing genres of products is that it lends to generalizations and particularly

in this market, products are very different in their capabilities and broadness of functionality.

Therefore I’d encourage you to google the vendor sites in order to get a richer understanding of

capabilities and benefits.

Web Spreadsheets (Spreadsheet+)

The first true mashup tool and situational application was the spreadsheet and it continues to be a

powerful tool for data analytics. While SuperCalc and Lotus 123 set the early pace, Microsoft Excel is the

out and out champion spreadsheet tool these days. Using a spreadsheet app you can do a lot to integrate

data from different places and create impressive charts and graphs. There are limitations however.

Firstly, the amount of accessible data means that spreadsheets (and the desktop PC’s they run on) can

run out of puff and freeze on the user. Secondly, spreadsheets require a fair amount of manual formatting

and crunching – unless you’re a true expert, which takes some doing but the results can be impressive.

Google too has an online spreadsheet which is ‘okay’ but not great. A third pitfall, is the fact that

spreadsheets are difficult to share and construct as a professional business system. Version control can

become a real nightmare, and it’s not uncommon for users to change formulas (etc.) unless the

spreadsheet is completely locked down.

New online spreadsheet style applications like Smartsheet do making sharing and version control much

easier because they’re online tools. With more dashboard style features, apps like Smartsheet can be

very useful to build scorecards and GANT style charts of activity. The main challenge with all spreadsheet

systems is the challenge of sourcing and crunching the disparate data to make it ready for analysis. When

working with resources like location-based data and documentary data, the sheer volume of data makes

spreadsheet tools unsuitable. Nevertheless, when it comes to economy spreadsheets are hard to beat.

Microsoft has launched PowerPivot recently that works well with Excel to bring more functionality to the

data visualization and charting than comes out of the box with Excel – but it’s not at the same level of the

bigger ‘engines’ I describe later in this article.

Pros

Easy to Learn

Economy

Cons

Struggle to cope with big data volumes, particularly map resources etc.

Lots of manual work in harvesting and normalizing data

Lack richer data visualization tools

Difficult to share and manage versioning

In-Memory Guerrilla Business Intelligence Software Tools

Vendors like Tableau and QlikTech have led the charge for light-weight business intelligence in the

enterprise by using in-memory processing; crunching data in the browser environment rather than a back-

end database or data warehouse.

Nick-named ‘Guerilla BI’, these tools could repeatedly be applied across weak parts of the enterprise

information management environment and thereby circumvent the large, slow-to-move incumbent BI

players. Vendors grew quickly, tapping into a pent-up demand for accessible business intelligence tools

that business professionals could use to give them the ability to analyze data in smarter ways than

spreadsheets could offer.

These products offer highly interactive, drill-downable and visually stimulating views of data that make it

much easier for Users to understand scorecards and identify patterns. But the focused new entrants did

little more than slap lipstick on a pig and many companies found they needed something far better at

harvesting and organizing source data. This has led in recent years for the technology bundles supplied

by players like IBM, Microsoft, Oracle, Tibco and Encanvas to concentrate more on data discovery. Now

the ‘must-have’ capabilities also include data connectors, multi-threaded data access, extract, transform

and load (ETL) and data workflow tools so that data can be painlessly harvested, normalized and

transformed into structures that facilitate simple creation of charts, dashboards, alerts, maps,

visualizations and printable reports.

The level of competition in the BI market has grown exponentially. The light-weight install and

deployment benefits and in-memory features of the early guerilla players have by now been absorbed by

the larger BI platform vendors. At the same time, cloud and web-born technologies have opened doors

to a new tranche of more nimble platform competitors in the BI market including such companies as

Yellowfin, Birst, iDashboards, Jedox and Jaspersoft.

Pros

Low skills overhead

Prices for in-memory business intelligence products can be surprisingly affordable

Powerful data visualization, dashboarding and analysis features

Typically remove need for dedicated BI data repositories or expert staffing

Many of the guerilla platforms are web/cloud based and require no installation of IT

Cons

In-memory ‘exclusive’ products suffer when crunching large volumes of data

While the dashboarding and charting might be whizzy, the quality of data discovery and

integration tooling varies hugely across the range of vendor options – getting the right data in

the right structure can be the most challenging aspect of BI!

Some offerings lack rich data visualization tools like geo-mapping, calendar views, pipeline

analysis etc. without further integration with third party APIs

Enterprise Mashup Products

While the number of enterprise mashup products has dwindled due to the slow take-up of the technology,

the companies I’ve mentioned earlier in this article are still around and have diversified. Enterprise

mashup apps and platforms are useful to bring existing data and collections of third party applications

together on a ‘single page’. They enable analysts and professional roles that demand lots of different data

topics to organize new views of data very quickly. Quite sensibly, when the anticipated ‘killer app’ status

of Enterprise Mashups turned out to be a damp squid, vendors applied their very useful tools to more

pointy and specific purposes. Companies like Corizon have specialized into workflow centric business

disciplines that need to harvest data from many different places like contact centres, while others such as

Jackbe, Serena and Encanvas have transitioned successfully into Application-Lifecycle Management (ALM)

and operational analytics.

One of the biggest reasons why traditional business intelligence projects have failed to achieve their

targeted outcomes is a short-fall in skills to correctly configure and exploit the technology. Perhaps for

this reason, many of the tool-kits from the ‘enterprise mashup’ stable enable applications authoring with

reduced coding or no programming whatsoever together with copious amounts of self-service wizards to

enable end users to create their own data structures and views without involving IT. Examples include

companies like Encanvas and Interneer that offer platforms using dashboards, point-and-click, drag-and-

drop and drop-down selection wizard tools to completely remove the need for programming and complex

report authoring. In the confusing world that is enterprise IT, many of the platforms like Encanvas that

originated from the ‘Enterprise Mashup’ stable back in the early noughties are now complete Platform-

as-a-Service (PaaS) cloud-based systems competing head-to-head with new start-up mobile and cloud

Platform-as-a-Service newcomers like WorkXpress, AppArchitect, OutSystems and AgileApps Live from

Software AG.

Pros

Instant-on cloud based and require no installation of IT

More capable at delivering ‘data discovery’ and supporting ‘big data’ views

Low skills overhead

Surprisingly affordable price plans able to cater for ‘spreadsheet+’ thru to ‘BI lite’

Typically remove need for dedicated BI data repositories and expert staffing

Advanced platforms come with data discovery tooling as standard including data connectors

(for harvesting data), ETL, workflow and drag-and-drop mashup engines

Cons

More expensive than spreadsheet+ and some of the guerilla BI products

Generally smaller vendors with less financial strength than traditional BI vendors

Lack the installed base of existing customers enjoyed by BI platform vendors

Tools that demand new ways of working (even when for the better) can suffer slow adoption

rates

Legacy Business Intelligence Platforms

By far the most expensive option, traditional BI platforms are very sophisticated but along with that

sophistication comes with it complexity and the need for specialist systems and skills. Most legacy

systems operate a dedicated OLAP styled data warehouse that pre-processes views of data taken from

operational/transactional systems.

Can legacy BI platforms offer operational analytics that surface actionable insights? Yes – when applied

correctly with the right blend of technology, know-how and investment. The difference is that it just costs

a great deal more money and takes longer to get anywhere; and in today’s economy time-to-value is a

competitive edge few enterprises can ignore.

There are still many companies operating dedicated business intelligence repositories with dedicated

support teams, although quite how long this will last is anyone’s guess. It makes increasingly less sense

to use proprietary business intelligence and GIS tools that offer very little benefit over those products that

offer similar capabilities with less operating overheads. Nevertheless, if an organization has invested

hugely into its performance management and operational reporting systems, it’s difficult to justify writing

off this investment and starting over with new technologies that on the face of it offer little different, and

may have shortcomings in resilience, query performance and reporting functionality.

Some businesses simply don’t change their operating behaviors that often and changes are marginal. In

such cases, arguments to employ more nimble, self-service oriented operational analytical tools are

difficult to fight for (and may not be justified). Some of these platforms are SO integrated into operational

systems that they can provide all of the insights colleagues need given sufficient skill and investment. It’s

also understandably a politically awkward sell when those senior managers in IT that recommended the

business spend millions on the incumbent technology (for all the right reasons) find they have to explain

to their peers ‘that was then, this is now’.

The big IT platform players like IBM, SAP, Microsoft and Oracle continue to swallow up smaller business

intelligence tooling vendors to complete their enterprise software jigsaw puzzles in the ultimate ‘got, not

got’ top trumps game. Their solutions continue to be VERY HARD TO BEAT if you have the money to spend

on them. Each one of these vendors can be assured to provide an effective legacy-style BI platform and,

where buyer investments have reached their tipping point, justifying any new genre of technology

normally requires a change of management or a change in market conditions to necessitate a cost

reduction focus to finally topple BI platform empires.

Pros

Financial strength of vendors

Completeness of platform features and capabilities

Knowledge of - and close integration with - core operating platforms and workflows

Cons

Expensive – not just to buy and install but to employ dedicated systems and people

Slow to adapt to changing requirements (generally but there are exceptions!!)

Self-service capabilities can be less complete than smaller competitors though companies like

Microsoft are very strong in this area and IBM, SAP and Oracle are catching up quickly

I hope this article helps to surface some new ideas on the many and varied operational analytics solutions

open to businesses looking into ways of sourcing actionable insights. No matter how large your

organization and budget might be, THERE IS a solution for your business – and it’s well worth investigating

because the actionable insights gleaned can be truly game-changing. Please don’t take this article as

anything more than the opinion of the author and do please conduct your own research!!!

I.