3 Areas Where Data Quality Management Can Supercharge Data Governance

What forces are driving expanded adoption of data governance programs?

Data governance has become a primary concern of companies everywhere. And with good reason – businesses must have strong data governance and data quality management in order to mitigate risks as thoroughly as possible.

These risks are both inward facing and external. On the compliance front, businesses now face more regulations than ever before. Regulations like the EU’s GDPR are mandatory rather than elective – if companies do not comply with these and other new laws, they can face severe fines. To avoid these types of compliance costs, companies must have strong data governance practices. These practices begin with an understanding of how well the business is mitigating risk at every level.

Additionally, risk mitigation involves protection of brand reputation. Sometimes compliance issues may not break a law, but the exposure can so alienate customers that the company suffers severely whether in direct revenue loss or loss of customer loyalty. This risk mitigation also extends to cybersecurity and the prevention of security breaches. Companies must protect their data and their networks so if an attack is successful, they have response strategies in place to limit their exposure and provide immediate notification to anyone affected.

But data governance is not just about risk mitigation and prevention. It can be used offensively as well. It is now playing a larger role in businesses as companies recognize that the data used to fuel robust data governance can be leveraged to aid other parts of the organization as a tool that increases revenue and reduces costs.

Once companies have established core capabilities to achieve high data quality, that data can be used to supercharge data governance in three key areas.

Streamline compliance and reduce risk

Once companies have put data quality management processes in place, they will have an easier time measuring where they are and protecting themselves on a compliance front as well as reducing security risks. Data quality tools can reduce the amount of compliance-related fines organizations pay, as well as the costs associated with managing data governance with poorer data quality. The ability to track metrics and measurements for data quality and show improvements in those metrics strengthens the compliance and security posture of the organization and reduces risk. Sensitive data can be appropriately protected and governed, and compliance reporting can be streamlined.

Drive revenue

By having strong data quality management, companies can leverage their available data more effectively and with greater confidence to gain insights to bolster revenue in the business. Data governance can be the springboard for a better understanding of what data the organization has, the relationships between that data and mapping how it is and can be used, which will then lead to more effective AI models and analytics so the entire business makes sounder, more profitable decisions.

Reduce costs

Just as strong data quality can help companies reduce the cost of compliance, it can reduce costs on a broader spectrum across the business in general. For instance, data deduplication can lead to spending less on data storage or data management systems. Companies can understand what data they actually use and what they don’t, and then adjust where they store the data, taking advantage of cheaper platforms and archiving or deleting data that is outdated, redundant or no longer needed.

Conclusion

Organizations are working hard to put more effective data governance and data quality management programs in place. The most forward thinking of these programs focus not only on risk and compliance but also on putting data to use to drive the maximum value for the organization, using AI and analytics on all their data to fuel digital transformation and uncover new business opportunities.

Data quality tools have broad applicability in service of both of these important goals and can be applied to any kind of data to improve the organization’s understanding of both the risks and value associated with that data. It’s time to explore how data quality can supercharge your data governance program, both now and in the future.

Learn more about how your organization can achieve excellence in data governance, read our report: Fueling Enterprise Data Governance with Data Quality.