What does Data Maturity mean?
Data maturity is a company’s ability to deal effectively with its data and subsequently use it efficiently for internal or external processes. Good data maturity is crucial to the overall success of a company and an important point on an organization’s path to digital transformation.
Data maturity is consequently an indicator of how well a company collects, analyzes and interprets its data. The better the database, the better and more informed decisions can be made.
Where is Data Maturity being used?
Data maturity helps companies in any industry understand how efficiently they are using their data and where there is still potential. Data maturity can be estimated using various models. Well-known models include the “Gartner-Data-Maturity-Model”, the “Dell-Data-Maturity-Model” or the “Snowplow-Data-Maturity-Model”. The majority of these models list five different levels at which an organization can find itself. Using this assessment, an organization understands its level of data preparation and can improve it if necessary.
Here is an overview of the different levels:
- Level: At the first level, there is still no awareness of data within the company. Data is exchanged mainly via e-mail and there is no data sharing between different departments. Decisions are not made based on well-founded data.
- Level: There is a minor awareness of data. However, it is mainly analyzed manually and is not deeply networked into processes. Data is not used throughout projects and only in a simple form. In most cases, they are only stored offline or in separate systems.
- Level: Now the organization already has a basic awareness of data and no longer stores it offline, for example using a data warehouse. There is an overall view of the data and a basic understanding. The organization is aware of how data can be used effectively and the first processes are being created in which data is automatically incorporated, often across departments.
- Level: The company now already has advanced data awareness and the data is used flexibly within processes, which are thereby improved and become more effective. Decisions are now also data-based. In addition, the data is provided automatically, which contributes significantly to improving the company’s performance.
- Level: The final stage is where full data awareness is now in place and data is effectively and deeply incorporated into processes. Advanced data science techniques are used, as well as Artificial Intelligence and BI & Big Data tools. These tools are used to create automated data analytics that are significantly incorporated into activities and decisions. Moreover, the data is now stored using data lakes or data warehouse systems.
How does one determine its data maturity?
To determine its data maturity as a company, as a first step it is important to ask yourself the following questions:
- Which building blocks are in place and how do they fit together?
- What is the “decision-making style” of the company?
- Do the teams work collaboratively or in isolation?
- Do the teams have the right knowledge and training?
- Are dashboards complicated or consistent?
- Die wirtschaftliche Bedeutung Ihrer Daten | Data Maturity Rechner | Splunk
- Digitales in Zahlen (bmf.gv.at)
- Gartner data governance maturity model | LightsOnData
- The Snowplow Data Maturity Model | Snowplow
- Was ist Data Maturity? (bigdata-insider.de)
- Was ist Data Maturity? » Einfach erklärt! (bimanu.de)
- Welche Datenreife hat Ihr Unternehmen? Sechs wichtige Fragen für B2B-Vermarkter (marconomy.de)