Data discovery helps governance teams stay secure

Data governance is needed for organizations to meet compliance requirements

Sam Curcuruto
Sr. Product Marketing Manager, Data Discovery, OneTrust
May 18, 2023

Businesspeople talk in a sunlit office lobby

Data governance continues to evolve. As organizations grow their data landscape, they turn to data governance processes and technologies to meet compliance requirements and derive greater value from their data. This requires companies to move away from manual metadata management to an automated, smarter “data catalog.” Intelligent, automated data discovery for governance teams is essential to achieve data governance and data catalog objectives.

A data catalog enables various functions of an organization — such as marketing, sales, and even data science — to better understand their data and to address business goals more readily. For an organization to truly have a comprehensive and well-populated data catalog representing their data, their data discovery needs to evolve beyond just metadata.

Learn how data discovery and classification helps your business de-risk that digital information to help secure the enterprise with this infographic


Metadata only scratches the surface

It’s important to remember that you will never get a true understanding of your data when building a data catalog if you only look at the metadata level. Scanning a data asset or source just at the metadata level is essentially discovering the data at “face value.” The sheer volume and varieties of data that organizations must govern and a continually evolving data landscape often means that the actual data is very different from what the metadata states. This can be for several reasons:

  • Human or machine errors often lead to incorrect or poor-quality data being found in data sources. These common errors range from simple, such as surnames being held in a data column assigned to “first name,” to completely incorrect information tables.
  • Data drifts resulting in data mutations. When interactions take place with data over time, such as minor tweaks or major system upgrades, your data changes or drifts. Cloud migration is frequently a source of data drift. The changes caused by data drifts can impact your business’s processes in the long term and produce errors in predictive modeling. 

While metadata discovery does offer some benefits, in most cases, it is insufficient for teams to understand what data you have, its categories, and types – a pillar of data governance. Governance teams must also utilize intelligent data discovery tools to go beyond metadata to accurately classify and catalog data at a field level to understand their data better.


Understanding unstructured data is increasingly important

Cataloging only the metadata of unstructured data will give very little insight into what’s inside an actual file, for example handwriting in a PDF, text in an image, or content within a document. Deriving the content and context of unstructured data has historically been very difficult and error-prone, and for this reason, traditional data governance programs would focus on structured data only. 

However, ignoring unstructured data is also missing a significant part of your data. To meet your compliance objectives and to uncover valuable data in all data sources, you need to scan and classify unstructured data.

Unstructured data must be accounted for to have a truly comprehensive governance program in place. This can only be achieved through a data discovery tool that scans and classifies unstructured data sources.


Accurate data classification relies on scanning the actual data

Relying solely on metadata discovery does not allow proper classifications of data or a determination of the data’s sensitivity. For example, columns in databases will often contain a massive variance of data, resulting in great data sensitivity variations. Companies must be able to scan the actual data at the most granular, individual level. This allows governance teams to determine where their sensitive or restricted data is and eventually identify any that needs to be protected or have conditions placed on its use.

OneTrust Data Discovery provides governance teams with the power to find data assets, classify and enrich structured and unstructured data, classify and tag data to build a central data catalog and more.

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