This article was originally published on the CPD website here.

What is Data Maturity?

The degree to which an organization makes use of the data that it generates is referred to as data maturity. The more they do with their data, the more data mature they are, and as a result, the higher they rank on the maturity scale. Therefore, an organization that employs modern business intelligence and analytics tools to analyze its data may be deemed significantly more mature than an organization that depends only on spreadsheets to carry out reporting tasks can be considered far more mature. Many analysis and data pioneers who are now dominating in their professions are wonderful examples of extensive data maturity. The data-driven approach used by firms such as Airbnb, Uber, and Netflix have resulted in the designation of data companies rather than conventional rivals in the hospitality, transportation, and entertainment sectors being more appropriate terminology.

Business data maturity may be divided into five levels

1. Introduction To Business Because there are no formal BI & Analytics tools or standards in place to enable this, reporting is restricted to activities that are important for business operations. Spreadsheets are used as the main reporting tool, and reporting is confined to tasks that are critical for business operations. 2. Detailed Description BI & Analytics are still in the early phases of deployment and are used to generate reports on activity levels and trends. 3. Consideration BI & Analytics are used not just to report on what is occurring, but also to prepare for the future, via the use of tools such as scenario planning. 4. Predictive Analytics Analytics is used to anticipate what will happen in the next 5, 10, or even twenty years, as well as to identify the primary drivers of current and future trending patterns. 5. Prescriptive Guidelines Users will no longer be required to enter variables into the system in order to forecast future results. Instead, Machine Learning and Artificial Intelligence make it possible to discover concerns before they are ever taken into consideration by decision-makers.

Importance of data integrity

Every organization generates data, and it is a valuable resource. It comes from a variety of sources, including the web, our mobile phones, payment systems, surveys or social media. The use of data is becoming more important as a business asset for organizations, and it has even been referred to as the “money of the twenty-first century”. However, it is not merely possessing data that makes it an asset; it is what we do with it that determines its value. And it is at this point that the concept of data maturity is brought into play.

The Data maturity framework

The Data Science Maturity Framework is a 5-level framework that helps you understand the maturity of your organization’s data science capabilities. This framework was designed by Sameer Rahman (voted in the top 100 data scientists in the UK) and the Tesseract Academy. You can access it via the free frameworks section on our website. Also feel free to get in touch if you have any questions.  

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