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Integrating Data Governance and Enterprise Architecture

by Tony Mauro       • September 3, 2020

Aligning these practices for regulatory compliance and other benefits

Why should you integrate data governance (DG) and enterprise architecture (EA)? It’s time to think about EA beyond IT.

Two of the biggest challenges in creating a successful enterprise architecture initiative are: collecting accurate information on application ecosystems and maintaining the information as application ecosystems change.

Data governance provides time-sensitive, current-state architecture information with a high level of quality. It documents your data assets from end to end for business understanding and clear data lineage with traceability.

In the context of EA, data governance helps you understand what information you have; where it came from; if it’s secure; who’s accountable for it; who accessed it and in which systems and applications it’s located and moves between.

You can collect complete application ecosystem information; objectively identify connections/interfaces between applications, using data; provide accurate compliance assessments; and quickly identify security risks and other issues.

Data governance and EA also provide many of the same benefits of enterprise architecture or business process modeling projects: reducing risk, optimizing operations, and increasing the use of trusted data.

To better understand and align data governance and enterprise architecture, let’s look at data at rest and data in motion and why they both have to be documented.

  1. Documenting data at rest involves looking at where data is stored, such as in databases, data lakes, data warehouses and flat files. You must capture all of this information from the columns, fields and tables – and all the data overlaid on top of that. This means understanding not just the technical aspects of a data asset but also how the business uses that data asset.
  2. Documenting data in motion looks at how data flows between source and target systems and not just the data flows themselves but also how those data flows are structured in terms of metadata. We have to document how our systems interact, including the logical and physical data assets that flow into, out of and between them.

data governance and enterprise architecture

Automating Data Governance and Enterprise Architecture

If you have a data governance program and tooling in place, you’re able to document a lot of information that enterprise architects and process modelers usually spend months, if not years, collecting and keeping up to date.

So within a data governance repository, you’re capturing systems, environments, databases and data — both logical and physical. You’re also collecting information about how those systems are interconnected.

With all this information about the data landscape and the systems that use and store it, you’re automatically collecting your organization’s application architecture. Therefore you can drastically reduce the time to achieving value because your enterprise architecture will always be up to date because you’re managing the associated data properly.

If your organization also has an enterprise architecture practice and tooling, you can automate the current-state architecture, which is arguably the most expensive and time-intensive aspect of enterprise architecture to have at your fingertips.

In erwin’s 2020 State of Data Governance and Automation report, close to 70 percent of respondents said they spend an average of 10 or more hours per week on data-related activities, and most of that time is spent searching for and preparing data.

At the same time, it’s also critical to answer the executives’ questions. You can’t do impact analysis if you don’t understand the current-state architecture, and it’s not going to be delivered quick enough if it isn’t documented.

Data Governance and Enterprise Architecture for Regulatory Compliance

First and foremost, we can start to document the application inventory automatically because we are scanning systems and understanding the architecture itself. When you pre-populate your interface inventory, application lineage and data flows, you see clear-cut dependencies.

That makes regulatory compliance a fantastic use case for both data governance and EA. You can factor this use case into process and application architecture diagrams, looking at where this type of data goes and what sort of systems in touches.

With that information, you can start to classify information for such regulations as the European Union’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA) or any type of compliance data for an up-to-date regulatory compliance repository. Then all this information flows into processing controls and will ultimately deliver real-time, true impact analysis and traceability.

erwin for Data Governance and Enterprise Architecture

Using data governance and enterprise architecture in tandem will give you a data-driven architecture, reducing time to value and show true results to your executives.

You can better manage risk because of real-time data coming into the EA space. You can react quicker, answering questions for stakeholders that will ultimately drive business transformation. And you can reinforce the value of your role as an enterprise architect.

erwin Evolve is a full-featured, configurable set of enterprise architecture and business process modeling and analysis tools. It integrates with erwin’s data governance software, the .

With these unified capabilities, every enterprise stakeholder – enterprise architect, business analyst, developer, chief data officer, risk manager, and CEO – can discover, understand, govern and socialize data assets to realize greater value while mitigating data-related risks.

You can start a free trial of erwin Evolve here.

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