And how we can make it right. Data governance is top of mind for many of my customers, particularly in light of GDPR, CAA, COVID-19.
Data governance is top of mind for many of my customers, particularly in light of GDPR, CAA, COVID-19, and any number of other acronyms that speak to the increasing importance of data management when it comes to protecting user data.
Over the past several years, data catalogs have emerged as a powerful tool for data governance, and I couldn’t be happier. As companies digitize and their data operations democratize, it’s important for all elements of the data stack, from warehouses to business intelligence platforms, and now, catalogs, to participate in compliance best practices.
But are data catalogs all we need to build a robust data governance program?
Analogous to a physical library catalog, data catalogs serve as an inventory of metadata and give investors the information necessary to evaluate data accessibility, health, and location. Companies like Alation, Collibra, and Informatica tout solutions that not only keep tabs on your data, but also integrate with machine learning and automation to make data more discoverable, collaborative, and now, in compliance with organizational, industry-wide, or even government regulations.
Since data catalogs provide a single source of truth about a company’s data sources, it’s very easy to leverage data catalogs to manage the data in your pipelines. Data catalogs can be used to store metadata that gives stakeholders a better understanding of a specific source’s lineage, thereby instilling greater trust in the data itself. Additionally, data catalogs make it easy to keep track of where personally identifiable information (PII) can both be housed and sprawl downstream, as well as who in the organization has the permission to access it across the pipeline.
So, what type of data catalog makes the most sense for your organization? To make your life a little easier, I spoke with data teams in the field to learn about their data catalog solutions, breaking them down into three distinct categories: in-house, third-party, and open source.
The first step is to understand what is data governance. Data Governance is an overloaded term and means different things to different people. It has been helpful to define Data Governance based on the outcomes it is supposed to deliver. In my case, Data Governance is any task required for.
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