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Analytics: The widening divide. An IBM/MIT Sloan study


I listened in to IBM’s call about their recent analytics study conducted with MIT Sloan – The Widening Divide (available from www.ibm.com/thewideningdivide). This is the second year for the analytics study and surveyed 4,500 people from 30 industries and 120 countries – a very broad view. Three key results:

  • The competitive advantage created by analytics is widening
  • Competitive analytics involves three competencies
  • There are two distinct paths to analytic sophistication

The survey results divide companies into three buckets – Aspirational companies just thinking about analytics, Experienced companies with some solid progress on analytics and Transformed companies, those with advanced capabilities and significant results. This year showed a slight increase in transformed companies but not much change over 2010 despite a lot more survey respondents. While these segments were self-assessed, the team did validate them with some clustering analysis and found that there was strong correlation between those in the segments (though Experienced companies split into two groups as we shall see).

The ability to create competitive advantage from analytics is way up from last year – from 37% who felt they were creating a competitive advantage from analytics in 2010 to 58%. And those that reported this competitive advantage were 2.2x more likely to be outperformers (up from 2.0x last year). More competitive advantage seen and more impact from it.

Transformed organizations were particularly likely to integrate analytics into their operations (over 70%) while Experienced organizations also said this (55%). Decision Management clearly being more important among Transformed organizations and these organizations were 3.4x more likely to be outperforming their peers.

Transformed organizations got their advantage by focusing on three areas:

  • Making decisions faster (72% say this is an area of intense focus v 49% for Experienced)
  • Enterprise risk (86% v 6%)
  • Customers (62% v 49%)

This is another pointer towards Decision Management in my mind – automating decisions (or partially automating them) with Decision Management Systems is key to making them faster. Decision Management Systems also have two key areas of focus – managing risk (rather than simply monitoring it) at the point it is acquired and improving customer treatment.

The study asked a lot of questions about analytic usage in different areas. High percentages used analytics in finance (budget, forecasts) as well as in monitoring operational processes. These dashboard-and reporting-oriented analytics were very widespread, even among laggards, and there was not much of gap between Experienced and Transformed organizations. In strategy and HR topics the Transformed organizations were getting close to 50% usage of analytics while everyone else was lagging behind. In terms of focus on customers Transformed don’t make 50% for anything except identifying prospects while Aspirational were down around 20% and Experienced around 30-35%. Clearly there is room to compete using analytics to target and manage customers. Personally I would like to have seen a very different set of areas of use as this list was far too focused on reporting/dashboard analytics for my taste.

Moving on, Transformed organizations have mastered three areas:

  • Manage the data
    Solid information foundation, standard data management practices and make insights accessible and available. For instances nearly 5x more likely to integrate data effectively and 4x more likely to make insights available
  • Understand the data
    Analytic skills are valued and developed, enabled by a robust set of tools and delivering action-oriented insights embedded in processes – a clear Decision Management reference
  • Act on the data
    Fact-driven leadership, analytics as a strategic asset and strategy and operations guided by insight. Need a data-oriented culture.

The most distinctive characteristics of Transformed organizations (those with the biggest difference in how much focus they give them relative to others) are:

  • Ability to analyze data
  • Ability to capture and aggregate data
  • Culture open to new ideas
  • Analytics as a core part of strategy and operations
  • Embed predictive analytics into processes (my emphasis)
  • Insight available to those who need them

This list was fascinating from a Decision Management standpoint. Embedding predictive analytics into processes means Decision Management Systems and these kinds of systems make insight available to those who need them even when those people are not themselves analytic (store staff, call center staff) or are not even people (websites, self-service applications). Decision Management Systems make analytics a core part of operation and a culture open to new ideas is critical for the kind of test and learn experimentation that is important for good Decision Management Systems. All good stuff.

Finally the study tried to see how people get from Aspirational to Transformed and found that Experienced companies, those on the journey, showed a strong bifurcation into two paths. One of these is focused on information management, on collaboration and on a top-down strategic approach to push analytics fairly evenly across the organization. A platform strategy if you like. The second path is more focused on developing analytic skills and tools in specific lines of business. Both can lead to transformation, they are just different. The collaborative organizations were trying to get broad improvement in customer engagement for instance while the specialized organizations were more focused on specific key performance indicators.

Interestingly the specialized group included many folks who found organizational change in their organizations difficult but technology change was easier. In contrast collaborative organizations struggled to deliver more advanced analytic capabilities, like predictive analytics, because they are spending too much time on a broad-based platform.

IBM recommends three steps:

  1. Assess your current analytic sophistication – know where you are
  2. Focus on improving your competencies – information foundation, analysis skills and tools, create a culture that ACTS on analytics
  3. Have an overall information agenda to tie things together

A good list and I especially liked the point about building a culture not of analysis but of acting on analysis. Those who know first don’t win, those who act first do!


Comments on this entry are closed.

  • Ravi Sodhi November 23, 2011, 4:09 pm

    Thanks for taking the time to review, clarify and share. I do think that for organizational transformation successes, we must build in a predilection or bias for the people and process enablers (you discuss in your book) over the tech enablers. We almost have to build informed collaborative decision ecosystems that follow a process from source data collection to IT across the business to the consumer and economy.

    I rememember Eliyahu Goldratt mentioning that business reengineering must take into account the assumptions behind the requirements new systems are automating. The trap is more data but faster that confounds decision making. What are the embedded business rules that we should no longer carry forth? We must improve our approach and strategies around the loading historical data. We must document the business case, secure resources to clean the data. How can we get better about collaboration around the data while driving data stewardship and ownership? Can we be better data managers? Can we also deploy an end to end testing infrastructure to ensure we dont just test process components in silos.

    As you suggest, we need to build different kind of information systems – Decision Management Systems!