What is data maturity? Data maturity is a measurement that demonstrates the level at which a company makes the most out of their data. Data maturity is the progression toward higher data utilization and capabilities. To achieve a high level of data maturity, an organization data must be firmly embedded throughout the business and fully integrated into all decision-making and activities. Decisions are based on data, not intuition or feelings or hunches. At the final stage of data maturity, an organization is said to be data-driven. People within the organization can do self-service analytics and machine learning. At this point, introducing new data sources is not difficult. It’s automated. Data is now a competitive advantage for the company.
Stages and Models
How does an organization get from the beginning stages to data maturity? There are several data maturity models out there. The book Fundamentals of Data Engineering by Joe Reis and Matt Housley published by O’Reilly. The authors have a simplified model of three stages only.
The company goals in the first stage of data maturity may be loosely defined or nonexistent. In this stage many requests for data are ad hoc. Data architecture and infrastructure are likely in the early stages of development. The data engineer may be involved in many roles such as data scientist or software engineer. The data engineer will need to work with key stakeholders and executive management. They will need to define the right data architecture within the confines of the business goals. They will need to identify and audit data that will support those business goals.
In the next stage, stage two, scaling with data, the organization now has some formal data policies and practices. The objective now is to create scalable data architectures with the eventual goal of being data-driven. The organization will be developing DevOps and DataOps practices. DataOps aims to improve the release and quality of data products. DataOps is built on business logic and metrics. It mixes people, processes and technology with high levels of communication and collaboration that needs to start with upper management that creates a culture of motivated people working together in a team for a purpose. This cerates a culture of continuous learning and growth.
At stage three, the company is data driven. The automated pipelines and systems created by data engineers allow people within the company to do self-server analytics and machine learning projects.