Data governance for better data quality

Data quality and data governance go hand in hand, and it's virtually impossible to have one without the other. So, how does data governance impact data quality, and vice versa? Let's take a look.

Common data quality challenges

High-quality data allows business teams to work with regularly updated and simplified data to carry out their projects. For data to be high quality, it must comply with current data protection regulations (such as the GDPR).

Data quality isn’t always a guarantee, as several factors can impact quality:

• A large volume of data can be difficult to manage and monitor
• Information can easily become obsolete if it is not updated in all databases
• If the company does not appoint a data manager, the databases cannot be cleaned, and erroneous information can accumulate

The role of data governance

While data quality refers to the accuracy and reliability of the data itself, data governance is how the organization manages its data on a high level.

Data governance helps set up a system for measuring data quality through effective indicators. These indicators list and report all erroneous data present in a database and allow the implementation of corrective actions. Data governance requires that all the company's businesses take responsibility. They must establish data management rules and define the business manager and the missions to be carried out to maintain data quality.

Six indicators of data quality

There are several aspects of data that can indicate it is of a high quality, including:

Data governance: Improving data quality in 3 key steps

Involve your teams in defining data quality rules

Data quality management is done via data quality rules. This includes, for example, information about the format of an e-mail address. The business managers themselves must establish all these rules. The immediate involvement of the business units in drafting data quality rules allows for direct appropriation and more effective monitoring.

If data governance managers write these rules, they will probably not represent the realities of the field, and the business may not consider them.

The human factor

It is sometimes difficult to determine objectively if the data collected is qualitative, especially if you only use traditional factors. Don't neglect the human factor! An experienced employee who is used to using certain data inputs will be better able to tell you if they are satisfied or not.

You can use confidence indicators to capture these feeling factors. They guarantee a better representation of the data used within the teams and explain why they are neglected.

Implement machine learning

After defining the rules to be respected, you can couple them with machine learning. This will provide recommendations for implementing corrective actions to solve data quality problems.

Remember to get feedback from your employees. Department experts can also assign a confidence score to the data used. The algorithm in charge of evaluating data quality considers the results and verifies the data quality according to the rules in place. The tool also considers the use of data by different business lines.

Conclusion

Clearly, data governance is a critical piece of any data quality initiative, and the two concepts work together to drive success. To achieve your company's data quality goals on an ongoing basis, you'll need to ensure that these policies are regularly reviewed, updated, and enforced to ensure they're as effective as possible.

In doing so, you'll be well on your way to ensuring strong data quality across the entire organization—and having the business insights that matter when making key decisions.

Without an adequate data governance policy, data quality is difficult to achieve and even more difficult to maintain. Therefore, all the company's businesses and strategic decision-makers must work together to establish and follow data quality rules.