Data Analytics Maturity Model

If an organization wants to grow and improve it is important to know where it is currently standing and where it wants to be.

To help your organization with growing and improving by using data, Hendrikx ITC has a data maturity model mapping the data usage in an organization.

The data maturity model helps to describe the current situation of your organization and shows where your organization could strive for. The model provides insight into the organization’s current situation by working with 5 levels. It can also be seen as our roadmap to guide your organization to the next steps by making the data usage more efficient, targeted, and productive. It also includes the applying of Data Science, Machine Learning, and Artificial Intelligence.

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Data Analytics Maturity Model

Level 1 Descriptive data

In the first level, the data is only of a descriptive nature. The available data shows the current situation or what happened in the past. Some examples of level 1 data are bank account balances or performance insights.

Level 2 Diagnostic data

With level 2 data, data is not just of descriptive nature but also provides diagnostic information. This means that the data shows how it was created and why certain values are what they show. The data is being put in perspective this way.

Level 3 Predictive data

Predictive information becomes possible with level 3 data. It can be done simply by using trend analysis. However, it gets truly interesting when we apply Machine Learning to discover patterns. The data is predicting the future to us, allowing organizations to make well-founded decisions and changes. You could think of an algorithm telling us the number of cars that will have car trouble in the next month.

Level 4 Prescriptive data

By telling us what has to happen in order to optimize or reach a certain goal, prescriptive data is going even further than predictive data. Prescriptive data is where Artificial Intelligence and Machine Learning are heavily applied. A good example of prescriptive data is the expansion of capacity and proposed changes to the configuration in order to prevent congestion on a production line.

Level 5 Autonomous data

Autonomous data maturity can be seen as the ultimate end-goal of Data
Science and is yet to be achieved by many organizations. An example would be a system that automatically adjusts itself to perform optimally. This is done without the interference of operators, engineers, or managers. Several examples include a SON (Self-Organizing Network), self-employed production lines or systems, but also automatic stock management.

Classification of the levels

Level 1 and level 2 are data levels that primarily explain the past and current time, meaning they are reactive levels. Level 3 and level 4 provide input on the future, allowing organizations to adjust before things happen. These levels can be classified as pro-active. The last level is being adjusted automatically, meaning no more action is required and therefore classified as non-active. It is possible that parts of the same process are present at different data analytics maturity levels.

Additional advice

If your organization wants to advance to the next level, keep in mind that the foundation must be solid. Incorrect processing of raw data leads to dramatically reduced levels of accuracy at higher levels. At Hendrikx ITC we work with our clients on a daily basis to help them achieve the next data maturity level. Contact us for more information!