It is generally accepted in many industries that the next frontier in competition will revolve around analytics. Companies that figure out how to leverage analytics will improve business performance by reducing costs, improving customer satisfaction, and understanding more clearly who their customers are and what they want to buy. Companies that know how to act on information developed through analytics will be the winners in tomorrow’s market.  Most of the organizations that we work with understand the importance of analytics and have a pretty good idea of where and how analytics can be useful; the challenge is developing an effective applied analytics capability that allows organizations to act.

There is a plethora of information available regarding analytics; it seems that every week (sometimes every day) notifications arrive in our e-mail announcing another analytics conference. What is missing is a framework that organizes a complex, n-dimensional issue into a succinct problem statement that allows the development of a conceptually comprehensible solution—one that translates analytics into action. In addition, most of the information being provided regarding analytics centers on three topics—tools, data, and data governance (we can refer to these three items as an “analytics core”). Although an analytics core is a critical base for developing an analytics culture, it alone will not solve the issue. We’ve observed many organizations with a reasonably solid analytics core that still can’t execute (apply) analytics effectively.

Nolan uses an analytics maturity framework when assisting our clients with applied analytics. Analytics maturity, in addition to the analytics core, takes into account the people, processes, and technology that surround the core to make applied analytics work – it helps institutionalize an analytics culture.  I will briefly touch on the key components of the framework that underlie an analytics culture.

Five main components of the analytics maturity model surround the analytics core and provide a basis for developing an analytics culture:

  • Establishing Measures
  • Reporting Measures
  • Translating Business Questions
  • Prioritizing and Executing Interventions
  • Confirming Business Value
  • Maintain/Refresh Analytics Infrastructure

Most companies conduct these activities, but they are often done informally and inconsistently across the organization. We believe that  to develop a sustainable analytics culture, these activities should be formalized and performed consistently across the organization; everyone should speak the same language. A brief description of each framework component is provided below:


  • Establishing Measures: Understand which measures are important to the organization.  Do they tie into strategic and tactical plans? Have targets been established, and does the organization understand what they are aiming for? If not, analytics initiatives can become intellectually stimulating exercises that don’t produce business value. If you can’t articulate the “so what,” why do it?
  • Reporting Measures: If measures are defined, are they actively reported on? Do people understand trends and know where there may be promising analytics-based questions to pursue? Not all analytics initiatives will be based on established measures, but some should be, and the organization should be keenly aware of where they stand with key measures and targets.
  • Translating Business Questions: The process of translating a business question into an analytics question should be defined, including service-level agreements. Stakeholders often tell us that they are frustrated with the time and effort associated with executing analytics-based initiatives: a business question is asked; an analyst runs some algorithms; the original requestor, who has waited a week for a results, finds that the analyst didn’t understand the question (or the requestor didn’t understand what they were looking for); and the process starts all over again. Analytics-based organizations know how to ensure that business questions are appropriately translated and provide service-level agreements (SLAs) between requestor and analyst so that all stakeholders know what to expect regarding cycle times, etc. They make it easy to leverage analytics capabilities.
  • Prioritizing and Executing Interventions: Once results are produced from analytics-based initiatives, are the results actually used to drive change? We see too many organizations discuss the interesting insights they’ve achieved through analytics; we don’t as often hear how the results are used. The process of understanding the results, developing interventions, and tracking the progress of the interventions should be formalized. Reviewing reports, asking for more information, and having intellectually stimulating conversations about results don’t improve business performance. Action does.
  • Confirming Business Value: The actual value received via the interventions should be tracked to allow the organization to understand where their investments are paying off, inject accountability into the process, and determine how the analytics process should be updated/improved. If it was decided that an intervention could increase customer satisfaction or reduce cost, the result should be measured on the back end. Understanding the actual result can help shape future initiatives, and drive other interventions. Analytics isn’t magic: like anything else, it takes rigor and accountability to produce results.
  • Maintain/Refresh Analytics Infrastructure: I haven’t touched on setting up the actual analytics infrastructure because my comments here center on what must be done on an ongoing basis to move toward an analytics culture. Improving the ability to effectively leverage analytics is an iterative process; some level of capability should be set up, the components outlined above should be defined and executed, and the experience should be used to reassess the analytics core, which will change as your organization and the industry mature.  As people become more familiar with analytics, the way they interact with the process will evolve and their needs will change. Organizations that really want to compete on analytics should expect to assess and refresh their infrastructure on a regular basis.

Obviously, developing a solid analytics core is critical to establishing an analytics culture—but it isn’t enough. Organizations must make sure that the people, processes, and technology surrounding their analytics core are well defined. People have to know what questions to ask (measures), how to ask them (translation), and what to do with the answers (interventions) once they find them. Stakeholders must  confirm that the actual results (business value) match what was expected of the answer (intervention) when it was designed. When these elements come together, you will have a winning analytics strategy and the enabling capabilities to realize the true business benefits of analytics.