Data Governance

Control your data from start to finish, from strategic to operational usage

Data is a valuable business asset and storing (and using) data comes with great responsibilities. That’s why organizations need an effective strategy for data governance. Organizations need to control their data. Controlling the data means improving data quality, knowledge of data leaks and mask data when needed to comply with privacy regulations.

What is data governance?

According to Gartner, data governance is “the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption and control of data and analytics”.

TechTarget has the following explanation: “Data governance is the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal data standards and policies that also control data usage.”

Summarized, Data Governance helps organisations to reach several important goals:

  • Increase confidence in decision making based on data
  • Improve data security
  • Control data by metadata
  • Optimize the effectiveness of (supervisory) staff
  • Show data issues from strategic, tactic and operational levels

Data governance plan

A good data governance plan contains the basics on how to process data in accordance with the law. It’s all about defining policy, roles and responsibilities for access to, manage, secure and use Personally Identifable Information (PII). In short, a data governance plan helps to make it easier to follow the rules.

A data governance plan includes:

  • How and where data is stored
  • Who has access to the data
  • Which actions can be take on the data

Test Data Management

As part of the data governance plan, you have to think about how you manage your test data. One of the basics for test data management and data governance is data knowledge. There has to be insight in data quality and knowledge about whether or not privacy sensitive data is stored and if so, where. Many organizations still use production data in QA, development or training environments, so these environments must be represented in the governance plan next to production environment.

Obtaining data knowledge often results in finding out that 1) there is too much data and 2) there is privacy sensitive data.


Too much data

Many organizations (still) use copies of production data for development and testing. This can cause serious storage problems. When your production data grows, your downstream environments grow at least twice as hard. Subsetting test data will fix this problem easily.


Privacy sensitive data

Using copies of production will also result in having privacy sensitive data or PII in development and test databases: something that’s really not allowed anymore nowadays. To make sure it may still be used for these purposes, you should mask the data.

Improve your Test Data Management today

Data Masking

Mask privacy sensitive data, generate synthetic data and use it for development and testing.

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Data Subsetting

Extract small reusable subsets from large complex databases and speed up your testing.

Data Provisioning

Monitor, automate and execute your test data from one central test data portal.

Data Discovery

Get new insights in data quality and find out where privacy sensitive information is stored.

Data Masking


Data Discovery