Data Maturity

Data Maturity is the key to becoming a Data Driven Organization. There are many factors to judge it.

‘Maturity refers to stability level of an entity or a process or a product.’

Maturity is not about how many stages of process has passed through, it’s about how stable the product is after implementation.

You must have heard people referring to some individual that he is a mature person so let him take his own decision. Same way, decision can be mature or immature which depend on the result from those decisions.

In this era where Data has become the most powerful input for any organization, well it has been since its start. But now technologies are so MATURE that insight from data analytics are taken very seriously and million-dollar or sometimes billion-dollar deals are taken based on Data Maturity.

Everyone remembers the acquisition of WhatsApp right? Multi-billion-dollar deal just based on how many users is using it and what is the potential growth on a simple mobile application.

Four Stage of Data Maturity

• Discovery: First stage is where users use adhoc reports and dashboard for data discovery, but decisions are still taken based on data collected from other ways.
• Users: Second stage is where users start using data in their daily operation activities. This stage is where organizations turn towards data for standard process, saving a lot of time by automation.
• Leaders: Third stage is where data started showing its real value where decision maker starts using data for their daily decision making. As mentioned above, now a days million or sometimes billion-dollar decisions are made based on data.
• Innovation: Forth stage is the most exciting stage where user start innovating new thing using data. Referring to WhatsApp example, we can see a bunch of developers built a small mobile app to connect people which has changed our lives for good.

Different Data Maturity Models

• Data Management Maturity Level
• Data Ethics Maturity Level
• Data Governance Maturity Level
• Data Architecture Maturity Level
• Data Modelling Maturity Level
• Data Security and Privacy Maturity Level
• Data Integration Maturity Level
• Data Analysis and Analytics Maturity Level
• Metadata, Master Data, and Reference Data Maturity Level
• Data Storage Maturity Level
• Big Data Maturity Level
• Data Science Practice Maturity Level
• Data Liberation Maturity Level

Note: All above terms are explained in this book as separate topics.