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.
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.
The final phase in the maturity model is prescriptive analytics, automated decision making through machine learning and artificial intelligence. The highest-level example of these systems includes automated trading systems. The information moving through those systems moves so fast and in such a massive volume that it would be impossible for a human being to do the analysis. Instead, sophisticated algorithms make those decisions. Google Maps employs this type of intelligence when giving driving directions. If enough slowdowns exist in an area, Google Maps will automatically reroute drivers to an alternate route.
Organizations need to take a close look at the goals of key functional levels of a company – sales, finance, marketing, operations – and identify the data needed to support those goals. Then, an organization has to look at their data from technical and business perspectives.
From the technical perspective, the organization should ask questions like:
From a business perspective, the questions include:
After looking at all the technical and business factors, an organization can score the data to determine which data has priority for the organization’s success. This is a key part of data maturity: understanding key areas – finance, marketing, sales and operations – and prioritizing based on where the organization has the best data. If an organization hasn’t figured out “what was,” it’s hard to enhance with other data to figure out the “what could be.” If an organization can’t accurately capture sales information – across product or customer geographies – any additional data won’t improve how the organization can predict sales.
At the lower end of the maturity scale, an organization is hampered by a lag in visibility of what is happening in the business. As organizations become more mature in handling their data, “lag time” becomes tighter and transforms into not “real time,” but rather, “right time” data. “Right time” data is data that an organization can act on. If an organization can’t do anything actionable with their data, then it’s just noise.
An organization that tries to implement or build better data analytics may find detractors. Becoming better at analytics may turn into a larger change management issue for an organization.
While sales is usually where a company focuses its data-improvement efforts, sometimes a “champion” to support change works elsewhere in the company – in finance, marketing or operations. Organizations should look for these champions and develop better data analytics in areas where such a program can have traction and buy-in. When an organization demonstrates that it can add value by using data to make decisions in one area, they can push change into other areas.
Data maturity at an organization is viral. Once one part of the business buys in and builds the discipline and good habits of running as a data-driven organization, that discipline can be instilled into other parts of the organization.
The COVID-19 pandemic unleashed an unplanned stress test on most organizations and on how well they could pivot operations based on visibility into their data. It was a catalyzing event that forced organizations to examine their operations and make decisions much quicker than in the past.
Food service companies, like grocery stores and quick service restaurants, needed to make real-time staffing decisions based on demand shifts. Sellers of outdoor equipment of all types, but especially bikes, saw product fly off shelves and out of warehouses. When the pandemic lockdowns started around mid-March, if companies had to wait for month-end results to make decisions, it would have been too late for them to pivot effectively.
Getting good at using core data has become table stakes for successful companies. Whether because of this pandemic or some other black swan event in the future, if an organization can adjust its business based on what it sees in its data, and a competitor cannot, their ability to move faster will outcompete everyone.
Organizations from any type of industry can benefit from data maturity, although some industries have more experience than others in handling large quantities of data. Manufacturing and distribution companies have been using enterprise resource planning (ERP) systems for decades. Healthcare providers have ramped up their use of electronic medical record systems in recent years. As an organization grows into the middle market (around $100 million in annual revenue), it needs tighter visibility into its data throughout the organization.
That being said, innovation around data analytics in recent years has brought the price point for sophisticated tools down to where smaller organizations can benefit from certain dashboards and technologies. Even smaller organizations can focus efforts on strategies that provide the most value: “What should I sell? What actions should I take?”
An organization focusing on the wrong things will get left behind, whereas organizations that have the right indicators at the right time can make better tactical decisions. Organizations can't manage what they can't measure.
Data maturity is beyond a tool or an application, beyond building a dashboard or a data warehouse. Data maturity in an organization is like developing any skill. Organizations have to practice it and commit to it.