What is data maturity and why is assessing it important?

What is Data Maturity?

Data maturity provides information on how effectively and professionally a company handles data. A company with a high level of data maturity is characterized by the fact that it effectively collects, manages, analyses and uses consistent, high-quality data. Data maturity therefore refers to a company’s ability to treat data as a strategic resource and make data-based decisions. A high level of data maturity is important for digital transformation and is a key factor in a company’s success. Data maturity includes an understanding of:

• the data tools used
• the quality of the data
• the personal skills and knowledge of employees in dealing with the data
  the analysis technologies used

Why is determining data maturity so important?

The more advanced a company’s data maturity is, the better it can use data to improve processes, identify trends and make decisions. Determining and increasing data maturity offers important advantages:

• Effective data strategy: determining data maturity helps companies understand their current position in handling data and develop an effective strategy to improve and optimize their data processes.
• Identification of weaknesses: By determining Data Maturity, companies can identify weaknesses and bottlenecks in their data processes and take targeted measures to improve them.
• Increased efficiency: Higher data maturity enables more efficient use and processing of data, which in turn can lead to an increase in productivity and efficiency within the company.
• Competitive advantage: Companies with advanced data maturity are able to make data-based decisions faster and more accurately, which can give them a competitive advantage.
• Preparation for future requirements: In an increasingly digitized world, data management and data analysis are becoming more and more important. Determining data maturity helps companies to prepare for future requirements and successfully meet the challenges of digital transformation.

How to I establish the data maturity within my organisation?

Determining data maturity is complex and does not just follow one method. To determine your level of data maturity, you should first evaluate the data itself with regard to the following aspects:

• Accuracy: Is the data correct and error-free?
• Completeness: Is all the required data available?
• Consistency: Is the data consistent across different systems?
• Timeliness: Is the data up to date?
• Relevance: Is the data relevant to business needs?
• Reliability: Is the data trustworthy?

There are various data maturity models for assessing and optimizing the maturity of your data practices. Some of the most common data maturity models include

1. Gartner’s Data Maturity Model: Gartner has developed a Data Maturity Model that consists of five levels: Ad-hoc, Opportunistic, Systematic, Strategic and Transformational. Companies can use this model to assess their progress in terms of data management, data analysis and data governance.
2. CMMI Data Management Maturity (DMM) Model: The CMMI Data Management Maturity Model is a standardized model that helps organizations assess and improve their maturity in terms of data management practices. It comprises five maturity levels: Initial, Managed, Defined, Quantitatively Managed and Optimizing.
3. TDWI Data Maturity Model: TDWI (The Data Warehousing Institute) has developed a Data Maturity Model that helps organizations determine their maturity level in terms of data management, data integration and data analytics. The model consists of six levels: Unaware, Aware, Reactive, Proactive, Managed and Optimized.

Overview of Data Maturity Levels

Depending on the data maturity model used, the number and classification of the different levels of data maturity can vary. The exact assessment criteria and benchmarks also vary depending on the model. For the sake of simplicity, we explain data maturity in four levels below, which initially help companies to roughly classify themselves and their data management.

• Level 1: At this level, data processes are unstructured and uncoordinated. There is no clear data strategy or guidelines and data is not used systematically. Data is often stored in isolated silos and is difficult to access.
• Level 2: Companies at this level recognize the value of data and begin to use it in a more targeted way. Initial measures have been taken to improve data quality and integration, but a comprehensive data strategy and governance is still lacking.
• Level 3: At this level, data processes are already more systematic and better coordinated. There are clear data guidelines, standards and processes in place to ensure data quality. Companies actively use data for decision-making and process optimization.
• Level 4: Companies at this level have established an advanced data culture. Data is fully integrated and available in real time. Advanced analytics technologies such as artificial intelligence and machine learning are used to identify forecasts and trends. Data is used strategically in all business areas to achieve competitive advantages.

Conclusion: Data maturity helps to gain a competitive edge

Overall, determining and promoting data maturity is crucial for the long-term success of a company. Companies that can use and process their data effectively are able to make informed decisions, gain competitive advantage and strengthen their ability to innovate. Therefore, companies should actively look at improving their data maturity in order to be prepared for the future and realize their full potential. At ithinx, we support you in determining your maturity level and work with you to create a plan to optimize your company’s data maturity.