Achieve a data architecture that drives Data Governance

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Discover the components of data architecture to consider for comprehensive data governance.
A correct data architecture directly influences the success or failure of data governance efforts. Why? While we work with a correct data architecture we can make governance more efficient, but otherwise, when it is complex or poorly integrated, it prevents the application of standards to achieve reliable and secure data throughout the company.


Data architecture is essential in enabling data governance , designing and evangelizing data management architecture to support quality and privacy requirements. But all of this is only possible when organizations reach data management maturity, which involves a multifaceted organizational transformation with sustained commitments over time.



Furthermore, this data architecture must be supported by stockholder database that allow workflows to be managed in a way that helps with the discovery, definition, application, measurement and monitoring process. It is important not to fall into the temptation of selecting tools before having the objectives, strategy and processes for data governance in place.





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The role of the business unit in Data Governance





Components of data architecture
To achieve comprehensive data governance and a data architecture that supports it, you need to consider the full lifecycle of critical business data. The first step should be to identify what that data is and figure out where it is located. This is about clearly visualizing:


Traditional transactional and operational applications, systems, and processes that import or update data, consolidate, deliver, and consume data.


Cloud-based applications and platforms, social data, mobile devices, third-party data sources, sensor data, and Hadoop analytics environments.


Once you have clear visibility on this aspect, you need to focus on assessing and delivering the shared capabilities that should be available across the entire enterprise data architecture. It's about avoiding compartmentalization and information silos and making the right technology choices.