Data Governance and Data Maturity

I have written previous blog posts on the topics of Data Governance and Data Maturity, and although these two topics are related, the former typically gets the most visibility within an organization.

As volumes of data grow, the need for precise, accurate, and scalable controls does too. This should really be captured within your data architecture and enterprise models. The data architecture should be an ongoing program that evolves with your business and explains the relationships of the business data. Even though data modeling is all about data definition, it has a much wider impact on the data of your organization, providing context with metadata.

Data governance is intended to provide and enforce processes and structure around how data is managed within an organization. It helps if the company has a strategy to establish a data culture that is focused on both understanding the data and improving its quality. It just isn’t possible to effectively govern your data if you don’t know how the data is being used or if it can be trusted for making important business decisions.

Implementing and enforcing a data governance program will be easier if the company already has some level of data and organizational maturity. Achieving organizational maturity is a journey requiring a balanced focus on both data and business processes. In this webcast, Data Maturity: A Balanced Approach, you can learn about a continuous improvement approach to achieve breakthrough results for data architecture and governance.

But how can you really tell if your organization is successfully implementing data governance and achieving data maturity? It may be helpful to conduct an independent assessment, but even then, there are multiple approaches. Robert Seiner of KIK Consulting & Educational Services devised a Data Governance Maturity Model that covers five levels of investment and achievement. George Firican of LightsOnData wrote several articles that highlighted key features of other data governance maturity models that have been introduced over the years. Each organization must evaluate what metrics and measurements they will apply against their governance implementation, and what success really looks like for them compared to industry best practices.

Learn how ER/Studio can help you get control of your valuable data assets, minimize redundancies and errors, and make your data more productive, so that you can guide your data governance and business processes with powerful features and industry best-practice strategies.

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