You know data quality is important for optimizing analytics results and speeding business insights. But have you thought about how data quality and IT governance go together? If not, this post is for you.
Defining Data Governance
Let’s start this discussion of the link between data quality and data governance by defining what data governance means.
To do that, we have to start by explaining the concept of IT governance.
IT governance refers to a set of policies that define how technological resources can be used and managed. At any large organization, establishing IT governance policies is essential to make sure that systems and resources are properly managed in a consistent way.
Data governance, as you might have inferred by now, is the subset of governance practices that relate to data. If your organization collects, monitors or analyzes data – as virtually every type of business does today – it should have data governance policies in place.
IT governance helps ensure consistency and compliance in the way data is collected, stored and accessed. Strong governance policies in the realm of data are especially important because data tends to be subject to several complex regulatory compliance requirements.
Using Data Quality to Facilitate Data Governance
So, where does data quality come into the picture? What does it have to do with data governance?
To put it simply, data quality is essential for data governance because ensuring data quality is the only way to be certain that your data governance policies are consistently followed and enforced.
After all, these policies – like any type of rule – are only worth anything when people actually adhere to them. And when it comes to regulating the way data is stored and managed, adherence to the rules is very difficult to enforce if your data is inconsistent or filled with errors.
If you lack strong data quality – and tools to help maximize data quality – your data governance efforts can be undercut in the following ways:
- Data governance policies will be followed more closely with some bodies of data than with others. For example, data governance may be ignored on legacy systems, where unsupported file formats or inconsistent data structures make data governance harder to enforce.
- Missing or inaccurate data within your databases makes it hard to identify which types of data are subject to which data governance rules. For instance, if errors within your data cause you to mistake personal customer information (which needs to be protected for privacy purposes) for non-private data, you will fail to enforce your data governance policies.
- Data governance audits, which are the only way you or outside authorities can determine whether data governance rules are being followed, become difficult and ineffective when the data being reviewed is filled with inconsistencies or errors.
Achieving Data Quality with Syncsort
Syncsort provides the Big Data integration solutions make data management a seamless process – no matter which types of systems you work with, and the data quality software to ensure you can trust your data in and out of the data lake. To learn more how Syncsort solutions like Trillium Data Quality products and DMX-h can simplify data governance, check out the Syncsort resource library today.