Leveraging your data goes beyond implementing tools. It is a journey your organization will take to understand what is important to measure and how you use that information to drive improved decision making.
This article is the first in a series that will walk you through that journey. Step one is setting a baseline of where your organization is and an understanding of why moving forward will provide value in the future.
Maturity overview
Data maturity and organizational transformation go hand in hand; an organization that gets better at harnessing data will see transformation in its people, processes and overall business results. The word mature can mean “having reached the most advanced stage in a process.” Data analytics maturity happens when an organization advances how they leverage key information to make critical business decisions.
Below is Baker Tilly’s Data Analytics maturity model. It shows four phases all organizations will go through in their analytical journey.
All organizations start with a view of their data that shows “what happened.” In this phase, users are viewing events that have already transpired. Their ability to act on those events is minimal, but it does give them insights into what to do going forward. Descriptive analytics is a fundamental component of any business, but has limited effectiveness in today’s quickly changing world.
Once an organization knows what happened, they need to move deeper into diagnostic analytics. They need to understand “why did it happen?” Are there trends in the data that are presenting themselves and need to be addressed? As an example, can the organization see that a specific customer, product or territory is performing well? The analysis at this phase must provide a level of detail that allows the end user to draw connections to events and present that information quickly enough to make actionable decisions.
Moving further down the maturity model, organizations get into predictive analytics, understanding “what will happen?” This type of insight requires the detailed and timely information created in the diagnostic phase. The tools and methods at this phase will use advanced analytical technologies to both predict and group given outcomes, leveraging both historical data sets as well as current information. Examples would include online shopping carts that suggest additional products or forecasting solutions that show cash flow projections based on current sales and historical payment patterns.
