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The Analytics Maturity template is based (in part) on the Analytics Maturity model of [Gartner] (

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The Analytics Maturity model helps you to find out:

  1. What analytical maturity do my company, individual departments of my organization or my customers own?
  2. What are possible data-driven applications and their requirements for the company, the department or the customer regarding the analytical maturity level?
  3. What are the relevant use cases to take my company, departments or customers to the next analytical level?
  4. Which analytical tools do I need for the realization of the applications?

For more information, see Data Strategy Design.

By the way: You can order a printed version (DIN A0) from Stattys.

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Start working on the Analytics Maturity template by documenting the current state of your company, department or customer. Focus your attention on the following two aspects:

  1. Use cases: which analytics solutions or applications are already in use in your company and for what purposes? To do this, fill the boxes (application fields) in the upper green area (=utilization) from left to right with the appropriate cards.
  2. Tools: which analytical tools, procedures or software systems do you use for this? You answer this question in the lower yellow area (=refinement).

The boxes reflect the level of analytical maturity, and the analytical maturity level determines the complexity of the particular application or tool. The analytical maturity levels (yellow area below) are interdependent: to make predictive analytics (predictions), you first need diagnostic tools to identify patterns in the data. Correspondingly, companies (green area above) also undergo a maturing process (from left to right). As the complexity increases from bottom to top (yellow area), the value added from the data (green area) increases from left to right. Correspondingly, it is the goal of companies to continuously increase their maturity level by introducing appropriate tools and applications based on them. A detailed explanation of the individual maturity levels and maturity process steps can be found below. After you have documented the actual state, start by specifying the target state by refilling the green boxes at the top and the yellow boxes at the bottom - from left to right or from bottom to top. Use different colors for the cards:

  • Green: existing applications or tools
  • Yellow: planned or applications or tools which are in progress
  • Red: required or desired applications or tools

Pay attention to consistency when filling:

  1. There must be an appropriate analytics tool for every analytics application. For example, for a marketing KPI dashboard, you might need a (self-service) business intelligence tool.
  2. Conversely, there should be at least one analytical use case for an analytical tool. Otherwise, this means that the tool is not in use and therefore may cost unnecessary costs to your business.
  3. The maturity level of the analytical application must match the maturity level of the analytical tool. To make predictions, such as revenue forecasting, you need a predictive analytics tool.


  • Value Proposition: for example, to identify data-driven applications and solutions (value offerings) for customers offered as products or services.

  • Business Model: key activities such as marketing controlling require analytical applications (for example, KPI reporting). Also, customer relationships can be based on analytical applications such as chatbots. After all, the analytic solutions may be a key resource.

  • Data Strategy: to specify specific use cases and to determine the required analytic maturity level (i.e. the analytics tools).

  • Data Landscape: to identify possible use cases based on available data.


Descriptive analytics describe what happened. They deal with metrics and key performance indicators (KPIs), which measure the achievement of goals. A descriptive analysis provides a retrospective view of what happened in the company and in the market. Descriptive analytics typically use anonymized and aggregated data. The results of the analysis still require a high degree of interpretation of the numbers by the decision maker. Often a descriptive is followed by a diagnostic analysis, for example, to investigate the causes why a goal has not been reached. Metric models are for example value trees, balanced scorecards or the AARRR model.

Examples for tools:

  • Classical business intelligence solutions
  • Manually created reports in spreadsheets
  • (Online-)Analytics services
  • Reporting tools


Diagnostic analytics explain why something happened. It deals with patterns in the data, such as trends, correlations, outliers, etc. A diagnostic analysis provides an insight into the mechanisms of a company and a market. Diagnostic analytics is usually based on non-aggregated data. A decision maker uses the results of a diagnostic analysis to plan and adapt measures. In order to decide which metrics are worth a diagnostic analysis, descriptive analysis is often required

Examples for tools:

  • Business discovery applications
  • Self-service BI solutions
  • Statistical tools
  • Testing tools
  • Visualization tools
  • Analytical programming languages


Predictive analytics predict what will or could happen. A predictive analytics solution creates statistical or stochastic models to forecast values and their probability. A predictive analysis provides an outlook on upcoming developments. Predictive analytics is based on non-aggregated and often non-anonymized data. A decision maker evaluates options for action based on the forecasts and makes decisions accordingly. For modeling, diagnostic analysis are often used in advance to identify patterns and verify them with specialist or industry knowledge. Descriptive analysis validates the significance and usefulness of predictions in productive operation.

Examples for tools:

  • Predictive modelling tools
  • Data mining tools
  • Data science suites
  • Analyticall programming languages
  • Machine-Learning-as-a-Service


Prescriptive analytics recommend what should happen. They evaluate options for action based on predictions (predictive analytics), simulate different scenarios and make recommendations based on the simulation results. Prescriptive analysis still requires from the decision maker the decision for a course of action:

Examples for tools:

  • Modeling tools
  • Simulation tools
  • Data science suites
  • Analytical programming languages


Automated analytics decide autonomously what should happen. It uses prescriptive analytics and carries out the actions itself. A decision maker is no longer involved.

Examples for tools:

  • Marketing automation solutions
  • Service orchestration tools
  • Agent systems
  • Artificial intelligence systems


The company runs business applications without analyzing the data that comes from business operations. Examples of business applications include CRM and ERP systems, enterprise websites and apps, IT-driven production, and more.

Examples for applications:

  • CRM software
  • ERP systems
  • Corporate websites
  • e-Commerce websites
  • Mobile apps
  • IT supported production systems


The company calculates based on the data from the business applications and other sources metrics as well as key performance indicators (KPIs) and reports them for example weekly or monthly in business reportings (classic business intelligence).

Examples for applications:

  • Marketing KPI dashboards
  • Sales reports
  • Production statistics


The company analyzes the data, metrics and key performance indicators and looks for explanations and abnormalities to improve its business processes and business models. Therefore, employees use for example interactive dashboard applications (self-service business intelligence).

Examples for applications:

  • Ad hoc analysis for customer insights
  • A / B testing of website designs
  • Multivariate testing in online advertising
  • Correlation and trend analysis based on social media monitoring
  • Analysis of causes of defects in production
  • Customer segmentation


The company uses its data to predict the future of the company or the market. Therefore, it uses advanced analytics techniques (business analytics). Examples are sales forecasting, churn prediction, lead scoring or predictive maintenance.

Examples for applications:

  • Sales forecasts
  • Prediction of churns
  • Customer rating ("lead scoring")
  • Calculation of the customer lifetime value


The company is looking for optimization potentials within the data. It simulates possible measures and analyzes the outcome. Those measures that will probably generate the best outcome, will be recommended by the analytics solution.

Examples for applications:

  • Control of advertisements
  • Recommendation systems for products
  • Predictive maintenance
  • Churn avoidance


Machines autonomously take over the control and the continuous optimization of the processes. The employees of the company only have a controlling function.

Examples for applications:

  • Dynamic pricing
  • Autonomous vehicles or aircraft
  • Intelligent storage
  • High frequency stock trading


After you have completely documented the actual status and defined the target status sufficiently, you should carry out a final consistency check again (see checklist above). The next crucial step is to select the applications that will increase the analytical maturity of your business - without skipping a step to minimize risk, effort, cost, and project duration, resulting in a rapid return on investment, and to benefit from the analytical optimization potential in a timely manner.

Rather take many small and fast steps as a big and slow step. Anyone who has ever tried to take two or more steps at a time knows how exhausting it is and how dangerous that can be.

When selecting the relevant applications, the following points should be considered accordingly:

  1. Use cases in high density green card applications (i.e. existing applications and solutions) are likely to increase the company's efficiency and effectiveness only marginally. The potential for optimization, for example through business intelligence solutions, is already exhausted if a corresponding reporting system has already been introduced for all departments.
  2. Unoccupied fields of application or boxes with many red cards (i.e. missing applications), however, suggest a high value creation potential.
  3. However, it must be checked whether the corresponding analytical tools are available for this purpose in the company. For example, optimization applications require prescriptive analytics tools. If these tools are not yet available, they must first be procured and imported into the company. This usually requires a costly evaluation of the tools as well as lengthy training of the employees.
  4. Accordingly, care should be taken that no analytical maturity level is skipped. For example, if the company uses only descriptive analytics tools for reporting, and wants to use predictions, i.e. develop predictive analytics solutions, to take two levels in the maturity model, then first introduce diagnostic tools and identify use cases for direct benefit to use the tools and to gradually increase the degree of maturity in the company and in particular among the employees.

Finally, you select the use cases that take the maturity of the company to the next level. You can then use the Data Strategy screens to specify the respective use cases.

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