Advancing and assessing analytics maturity: part 2

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In part 1 of my thoughts about analytics maturity, I deferred talking about issues related to the actual assessment of your organization’s level. Today I intend to detail some of the ways my peers and I are thinking about analytical maturity, comment on scales in use today, and address some of the innovative ways we have worked to preserve aspects of organizational complexity. And I’ll reemphasize a tip - “Get started now!” Almost all decisions at a departmental level should rely on facts generated from analytical analyses (including predictive modeling), and there are always things you can do to extend your progress.

First, let’s talk briefly about developmental stage labels.  Looking at any analytically-oriented organizational maturity chart it is immediately apparent that the levels are not defined using a uniform set of criteria. Usually classification systems shift definitional requirements when comparing lower levels to higher ones. Since SAS was the first to patent a five-tiered classification system called the Information Evolution Model), I extend that model by  proposing these five stages of development:

  • Immature (the lowest and Level 1)
  • Aware (Level 2)
  • Informed (Level 3)
  • Reliant (Level 4)
  • Innovative (Level 5)

Each level is based on the sophistication of analytics usage – pure and simple. These labels insure that each stage is separate and distinct from the others, and that organizational analytic maturity is uniformly emphasized (apart from what is introduced in books by my SAS colleague Evan Stubbs on  The Value of Business Analytics and Tom Davenport on Competing on Analytics.

The next important requirement of any classification system is to preserve the underlying variability that can be used to distinguish one organization from another, even if they are at the same maturity level. For instance, one business at an Informed level (L3) may have advanced tools, but no statisticians or programmers, while another may have these kinds of people but no analytics tools they can use. What SAS has recently invented is a set of corresponding metrics related to people, process and technology for each of the levels. My colleagues and I are using these metrics to score individual companies and even departments or divisions within larger companies. That way we can easily plot organizations on the larger scale as well as evaluate them to their peers. This scoring system supports the macro-level definitions while preserving the underlying variability that naturally exists from organization to organization.

Regardless of an organization’s level, the ensuing question is always “How do I move up to the next level?” The answer - there are always things you can do to advance your company, and the time to act is “Now!” Creating a fact-based culture must begin by infusing all business decisions with more informed or analytically-derived choices.  Usually we recommend changes to the underlying analytics infrastructure, which includes the people, process, and technology distinctions alluded to earlier and in my part 1 of this series on advancing and assessing analytics maturity. The focus needs to be on leveraging analytics resources from a variety of different infrastructure components. Therefore, if you do not have specific assets to leverage, then priority goes toward obtaining the necessary analytics infrastructure. People and technology acquisitions  are usually the first things to be considered, but it is critical to evaluate important business processes and data flow (i.e. data consumption needs) throughout the organization. And finally, always actively engage upper management in leading and supporting any overhaul/upgrade efforts.

Once a variety of analytics assets are in place, the next step in leveraging those assets is educational and marketing efforts. Teaching others about the benefits and advantages of analytics should be on-going, but it begins with simple things like lunch-and-learn seminars, weekly/monthly newsletters, and conversations among team members about how to better incorporate analytics into daily decision-making. Undertaking a quick analytics assessment is usually the first step in prioritizing current infrastructure and improving data throughput, and is a qualified service that we offer here at SAS.  We can give you a robust set of recommendations that will at least get you started.

There is not a one-size-fits-all approach to get you to the next level of analytics maturity. It may be necessary to leap-frog specific qualities and jump to a higher level of sophistication. But no matter where you are there are always things that can be done to improve analytics usage and information consumption at your organization. By applying constant effort and recording notable successes, your analytics maturity is only bound to increase over time.  It is a marathon race that is really about endurance over the long haul! And SAS is here to help!

marathon runner

Photo credit: amrufm //attribution: creative commons

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About Author

Phil Weiss

Analytics Systems Manager

Phil Weiss is an Advisory Solution Architect in Sales and Marketing Support. He was an accomplished application developer and statistical consultant for 10 years before joining SAS. His technical specialties are time series forecasting, high-performance analytics and distributed processing systems. Phil has written a book on the history of Lake Tahoe’s oldest licensed casino, the Cal-Neva Resort, a place once owned by Frank Sinatra.

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