CBVA presents findings from collaborative research between the Sloan Management Review and IBM's Institute for Business Value. The point of departure for the work is a second annual survey with over 4,500 executive, manager and analyst respondents from 122 countries and 30 industries – 10 times the numbers of the typical BI/Analytic industry survey. The response base, heavy with MIT alumni, SMR subscribers, IBM customers, academics and subject matter experts, is probably more management-focused than the typical BI survey as well.
I like CBVA's definition of analytics, paraphrased to “the use of data and related business insights developed using statistical, contextual, quantitative, predictive, cognitive and other disciplines to drive fact-based planning, measurement, decision-making, execution, management and learning.”
Using a taxonomy established in last year's survey, the study assigns respondent companies to one of three levels of analytics sophistication: Aspirational, Experienced and Transformed. Aspirational companies are basic analytics users driven from spreadsheets that’ve yet to make the big analytics leap. Experienced companies rely on analytics to guide strategy as well as day to day activities and have invested in visualization and advanced modeling tools. Transformed organizations are experienced consumers who've committed to advanced analytic modeling and have also invested in an enterprise information architecture to source their initiatives.
The 2011 results point to the increasing use of analytics in business: 58% of this year's respondents cited competitive value for their companies from analytics, compared to just 37% last year. Tellingly, it's the Experienced and Transformed bases noting competitive advantage from analytics that have grown most significantly. Apparently, the analytics rich are getting richer.
The research identifies organizational factors as being critical predictors of whether companies can have success creating a competitive advantage with analytics. “In effect, the most advanced users of analytics typically have a strong data-oriented culture that supports and guides analytics use. Having the right combination of tools, data and people, while necessary, is usually not enough, according to the data. Without strong cultural commitments, the success of an analytics program can be easily shortchanged or derailed.”
What are the characteristics of a data-oriented organizational culture? The research cites three:
- Analytics is used as a strategic asset
- Management supports the use of analytics throughout the organization
- Insights are accessible to all who might benefit.
Indeed, in data-oriented organizations, there's generally a top-down mandate derived from the business strategy to leverage evidenced-based decision-making managed through analytics champions.
A data-oriented culture is a necessary but not sufficient prerequisite for organizations seeking to create competitive advantage from analytics. Companies that wish to excel at analytics must first establish strong information management competencies and also build deep analytics expertise.
Which capability should come first? CBVA suggests it depends on the type of organization. Collaborative organizations focus first on an enterprise information platform to support broad dissemination of analytics. Specialized organizations, on the other hand, are more functionally-oriented, first building deep analytical skills for specific business processes.
The findings summarized in CBVA study affirm OpenBI's experience in the BI/Analytics marketplace over the last 18 months. Our customers are increasingly evolving from data warehousing/BI-alone solutions to DW/BI/Analytics. And the ones that lead the way are the ones who've adopted an evidence-based or science of business strategic approach and have made the commitment/investments to becoming a data-oriented culture.
These transforming customers are well down the information management path with established data integration practices, and are now making the investments to turbo-charge their analytics expertise. For such companies, this year's retrospective OLAP and dashboards will become next year's prospective predictive models and simulations.