Data governance is a hot topic lately. Companies are trying to devise and implement governance programs to provide some structure around how they manage and protect their enterprise data. But before you can manage what you have in your data environment, you must know what the data is, what it means, and how it is used.
How do you figure that out? Model it!
A data architecture is a dynamic entity— you don’t just create a model once and then set it aside, never to be referenced again. The architecture must adapt to support the business needs and reflect the growth of the organization.
In his blog post, Creating a Data Culture Through Continuous Improvement, Ron Huizenga shared questions to consider for understanding your data and improving its quality, and also described how the models and data flows can provide trace-ability through data lineage diagrams.
Data modeling is all about data definition but has a much wider impact on the data of your organization. Quality data definition impacts how data is produced and directly impacts how the data is or will be used throughout an organization. That means that we must proactively govern the process of how we define data in order to establish a common understanding across the team.
In his whitepaper, Data Modeling is a Form of Data Governance, Bob Seiner provides insights on the three actions of governing data and relates them to data modeling. Organizations that do a good job of modeling their data can realize significant business value and improved data quality, and will have an easier time implementing data governance.