Which is your company Data Maturity level?

Data has proven to be a competitive differentiator in different business sectors. The performance of any organization is highly correlated to the maturity of its data, so it's important to know in which level you are in this process.

Thursday, September 2, 2021

What is Data Maturity?

Data maturity is a measure used to determine where a company's data quality, structure, distribution, security, and analytics are in their progress. A high level of maturity is reached when data has been deeply integrated and actively used in the organization's strategic decision-making process. The fact of having Big Data technology or using technological tools does not mean that a high level of maturity has been reached within a company.

You may be interested in: "Walmart vs. Soriana: who wins at the POS".

This process can be done from two different sides: defensive and offensive use of data. Defensive approaches include identifying cost savings and risk mitigation. Offensive approaches include identifying new customer trends, improving customer knowledge and developing new business relationships. 

What is a maturity model? (DMM)

These are models that are designed to provide a contextual and timely assessment based on a company's data maturity. The modeling of this process consists of understanding what the initial diagnosis is with respect to the real possibilities in multiple dimensions. In short, it is a quantifiable and objective look at the use of data in a company, line of business of a department or any area that is modeled, providing strategic planning to help improve the use of enterprise data.

Also read: "Geomarketing: What every retailer should know".

There are several key categories that help organizations benchmark their capabilities, identify their strengths and weaknesses, and leverage their data assets to improve business performance.

  • Data strategy: Data management, user communication, financing.
  • Data governance: Governance management, enterprise glossary, metadata management.
  • Data quality: Strategy and assessment, profiling, cleansing.
  • Data operations: Requirements definition, data lifecycle management, vendor management.
  • Platform and architecture: Data architectural approach, standards, data management and integration platforms.
  • Historical data and archiving: Measurement and analysis, process management, process quality assurance, risk management.

At PREDIK Data-Driven we help companies understand where they stand in their data maturity and develop solutions that enable them to get the most out of the information they control.

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