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Glossary: Data Maturity

What does data maturity mean?

Data maturity is the ability of a company to handle its data effectively and subsequently use it efficiently for internal or external processes. Good data maturity is crucial for the overall success of a company and an important point on an organization’s path to digital transformation.
Data maturity is therefore an indicator of how well a company collects, analyses and interprets its data. The better the database, the better and more informed decisions can be made.

Where is Data Maturity used?

Data maturity helps companies in any industry to understand how efficiently they are using their data and where there is still potential. Data maturity can be assessed using various models. Well-known models include the “Gartner Data Maturity Model”, the “Dell Data Maturity Model” and the “Snowplow Data Maturity Model”. Most of these models list five different levels at which a company can find itself. With the help of this assessment, an organization knows where it stands with its data preparation and can improve it if necessary.

Here is an overview of the various data maturity levels:

 

Data Maturity Stages 1

  1. Level: At the first level, the company is not yet aware of data. Data is mainly exchanged by e-mail and there is no data crossover between different departments. Decisions are not made on the basis of sound data.
  2. Level: There is a low level of awareness of data. However, these are mainly analyzed manually and are not deeply networked with processes. The data is not used across projects and only in a simple form. It is usually only stored offline or in separate systems.
  3. Stage: The organization now has a basic awareness of data and no longer stores it offline, but in a data warehouse, for example. There is an overview of the data and a basic understanding. There is an awareness of how data can be used effectively and the first processes in which data is automatically incorporated are emerging, often across departments.
  4. Stage: The company now has advanced data awareness and the data is used flexibly in processes, making them better and more effective. Decisions are now also data-based. In addition, the data is made available automatically, which contributes significantly to improving the company’s performance.
  5. Stage: In the final stage, there is now complete data awareness and the data is effectively and deeply integrated into processes. Advanced data science methods, artificial intelligence and BI & big data tools are used. These tools are used to create automated data analyses that play a key role in activities and decisions. In addition, the data is now stored in data lakes or data warehouse systems.

 

How do you determine your data maturity?

To determine your data maturity as a company, it is important to ask yourself the following questions as a first step:

  • Which components are available and how do they fit together?
  • What is the company’s “decision-making style”?
  • Do the teams work collaboratively or in isolation?
  • Do the teams have the right knowledge and training?
  • Are dashboards complicated or consistent?
  • Does the company prefer a “big reveal”?

(Source: Igelsböck A. What level of data maturity does your company have? Six important questions for B2B marketers. 31.05.2021. retrieved from “What level of data maturity does your company have? Six important questions for B2B marketers (marconomy.de) “)

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