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How To Approach Data Governance To Avoid Poor Data Quality

  • 2 wk ago

Everyone is interested in getting more data. Few consider that more data is not always better if the quality is low. Quality assurance is an enormous problem, plaguing numerous organizations. In fact, Gartner’s 2020 research showed that poor data quality causes an average of $12.8 million losses each year for those surveyed.

Risks associated with poor quality data can be nearly invisible as they can only become apparent after a considerable amount of time. However, it can often lead to lost business opportunities, increased operating costs and lower decision-making accuracy. Data governance is the only way to protect organizations from the appearance of low-quality data.

What Is Data Governance?

You would be hard-pressed to find one definition of data governance. Everyone understands the process slightly differently. However, the goal is always the same — to ensure that all data across the organization is of high quality and is processed according to rules and guidelines.

As a result, data governance not only mitigates numerous associated risks (such as the ones outlined above) but also provides a wide array of benefits. These can range from simple ones like increased decision-making confidence and better data use to the ability to meet regulatory requirements, increased profitability and staff productivity.

However, while immensely useful, proper data governance isn’t easy. It’s a multifaceted process that requires the involvement of everyone in the organization. While getting everyone on board with data governance might be a strenuous process, it’s a necessity as data governance works only when it is a universally accepted framework.

Data governance can be separated into several parts. More pieces can be added along the way, however, the foundational parts are:

People and organizational bodies. These will be the people who are directly responsible for the implementation of data governance. Usually, this includes responsibility for data quality assurance, adherence to governance guidelines and the creation of new rules.

Rules and rules of engagement. Rules define how data has to be handled. Access rights and methods, data ownership and management should be clearly stated. Data pipelines should also have owners, handling practices and crisis action plans assigned. Finally, adherence to the rules should be measured through evaluation processes such as minimum data quality requirements. In the end, the goal of all rules is to ensure data quality and security while improving the speed of changes.

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