Those who are watching the data management industry have definitely seen an increase in the volume of posts, whitepapers, and webinars on the topic of Data Governance. This activity is partly due to the data privacy regulations that also have received a lot of air time, particularly GDPR that took effect last May. Companies are realizing that they need to get a handle on protecting and securing their user data in order to avoid serious fines as well as data breaches.

However, the management teams at these same companies are coming to a realization that they can’t easily manage and govern their data if they haven’t figured out what data they have, where it resides, and how it is used within the organization. So for many of them, it’s back to basics: know what you’ve got and what you’re doing with it, so that you can put the right controls in place and adapt as the business evolves.

The IDERA team has also been generating information to help customers grasp this concept to define and implement an enterprise data architecture as the foundation for data governance. It’s not a quick fix and can’t be done overnight – it will take time, but it will be well worth the effort to position your company for audit compliance and data governance programs.

First, make sure you know how data flows through your organization. This includes understanding who has permissions to access and modify it, as well as documenting how the data is used at various stages. The best way to document this is with business process models. Data architects need to collaborate with the business team to create business process models to define a consistent data governance strategy for the organization. In her webinar, Modeling Data Governance, Kim Brushaber discusses aspects for both business and technical considerations and shows examples of business process models that can help you kick off your data governance program.

In parallel with the process modeling, you should develop your data architecture aligned with those processes, and ensure you can trace the data lineage. You can also incorporate metadata and attachments into the models that identify sensitive data, assign compliance classifications, and identify stewards. In his webinar, The Model Enterprise: A Blueprint for Enterprise Data Governance, Ron Huizenga shares how rich data models can serve as the conduit to better data governance, security, and quality.

Now you can use this enterprise data architecture as your foundation for data governance programs. You can only govern the data you can effectively manage and track. Data governance programs are intended to establish oversight and control over enterprise information management processes and procedures. In his webinar, 5 Best Practices for Operationalizing Data Governance, David Loshin discusses the relationships between business policies and regulations, data policies, and five best practices to take advantage of tools and technologies to operationalize data governance.

Whether you’re designing a new data warehouse or need to reverse engineer an existing infrastructure, you need the right tools and the right methodology to build your enterprise architecture foundation and establish a data governance program. As I said, you can’t do all of this overnight, but if you put together an actionable plan to address these key areas, and work though the steps, you can make progress on the goal for effective data governance within your organization. Learn more about how to Fortify Your Data Governance with Enterprise Data Architecture with ER/Studio.

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