Operational Analytics: Best Software for Sourcing Actionable Insights

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 and PaaS 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!!!

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