Analytics Maturity A0 (47" x 33") synthetic paper (EN)

Why the Analytics Maturity Canvas?

The Analytics maturity canvas helps you create a roadmap to help your business evolve step by step into a data-driven organization. With the analytics maturity canvas you can determine the analytical maturity of your business or the maturity level of your customers and identify data-driven applications that increase the maturity of your business, department, or customers.

What is Analytics Maturity Canvas?

Analytical Maturity Canvas is a visual collaboration tool that complements the Canvas data strategy. It allows you to compare and prioritize several data-driven use cases with regard to the required analytical maturity level. It is based on Gartner's Analytics Maturity Model and adds a fifth level of "Autonomous Analytics" to this model. Use the canvas with the Stattys Notes to structure your ideas, thoughts, and conversations about a roadmap for implementing a data strategy.

Note: the canvas contains trigger questions to help you fill it.

How to use Analytics Maturity Canvas?

Document the current status.
Begin your work on Analytics Maturity Canvas by documenting the current state of your organization on green Stattys Notes. Start with the analytic applications in the upper green area of the Canvas. The green area reflects the utilization of the company data and the boxes in the green area are arranged according to the analytical maturity level of the company. An analytic application is a solution for a particular data-driven use case. An example is a report of key business metrics from the various departments such as marketing, sales, and production to business management. For example, this application can be assigned to the Reporting box.

Then dedicate yourself to the lower yellow area. This section lists the analytical tools (for data refinement) that are available to the company and which the employees use in the applications (see top green area). It is important that there must be at least one corresponding tool in the company for each application. Conversely, there should be no tools without application - otherwise the company may incur unnecessary costs. In addition, the degree of ripeness of the tool should match that of the application. If the ripeness of the tool is higher than the maturity of the application, "shoot sparrows with cannons". If, on the other hand, the degree of maturity of the tool is lower than the degree of maturity of the tool, then it will not only overwhelm the tool, but also your employees.

Use green maps for existing tools and yellow maps for planned or in-progress, acquisition, or launch tools. The same applies to applications. Work on a step-by-step basis: from left to right, bottom-to-bottom, according to the increasing complexity of the tools and the increasing value-added potential of the applications.

Specify the TARGET state.
When you have completed the documentation, go to specifying the target state by identifying data-driven use cases (for example, using the Value Proposition or Canvas canvas) and fixing possible solutions to red Stattys Notes. Then locate the applications on the company's analytical maturity process (upper green area).

Also, think about which analytics tools you need for each application and also note the tools on red Stattys Notes in the lower yellow area, according to the analytical maturity level of each tool. If necessary, concretise the solution with the Canvas data strategy to find out which tools you need.

Derive a timetable.
Finally, before you derive a roadmap according to the analytic maturity level, check the canvas for consistency: are the mappings correct? Is there an appropriate tool for each application? And for every tool at least one application? Are the maturity levels of the application and the tool matched?

In order to identify the relevant applications and therefore also the necessary tools, you first focus on those boxes (maturity levels), which are mostly filled with red Stattys. Boxing with a majority of green Stattys probably offers little potential, as the opportunities to gain new insights and then optimize critical business processes have been largely exhausted.

On the other hand, if maturity levels are too high, there is a risk that the projects for implementing and implementing the applications will become tedious and complex, increasing their costs and risks, while the expected benefits will come late. So do not overcharge your business and employees by trying to take two or more (Analytics Maturity) levels at once. Rather, take quick little steps.

It should also be noted that the analytical maturity levels are based on each other and the applications are mutually dependent. You also need descriptive and diagnostic applications for Predictive Analytics applications, firstly to measure the accuracy and secondly, the usefulness of the predictions, and to identify patterns in the existing data when modeling the predictive models.

Links

More information can be found on Datentreiber.