How are data governance and data quality related?

By Jonathan Robson, Data Governance Lead at Precisely

Data quality is crucial. It allows businesses to better understand their data and ensure it’s accurate, consistent, and complete – giving business leaders the power to make critical decisions with confidence. But to truly deliver trusted data, it isn’t enough to focus purely on data quality. As a result, many organisations that are committed to improving data quality are also starting to invest in data governance; delivering frameworks to manage the data within their organisations, ensuring its security, maintaining compliance with important government regulations, and making sure it is available to the people who need it, when they need it.

To find out more, we recently partnered with Drexel University’s LeBow Center for Business Analytics on “The Data Professionals Speak: Trends in Data Governance and Data Quality Programmes”, a report based on a survey of over 800 data leaders. Below are some of the key findings from the research, which provided fascinating insights on the relationship between data quality and governance:

Data quality is a top concern for businesses

With increased pressures on data professionals to lead their organisations into a data-driven future, it’s no surprise that the survey showed many businesses are already aware of the benefits of good data quality and are actively seeking to improve this within their organisation – with 75% of respondents acknowledging that improving data quality and trust is their organisation’s “top concern”. Other key goals included the optimisation of data for operational efficiency (66%), the use of data to drive new business models (63%), and improved risk mitigation and regulatory compliance (53%).

Investments in data governance deliver improved data quality

Organisations that struggle with data quality issues can make improvements by adopting data governance. The research uncovered benefits regardless of whether businesses were building new programmes or maturing existing ones. The findings indicated that 66% of data and analytics professionals experienced improved data quality as a “leading benefit” when implementing data governance programs, a trend that rose to an incredible 83% for organisations that already had a mature data governance framework in place.

Furthermore, the existence of a data governance programme seemed to encourage the adoption of more rigorous processes for data quality measurement – with 54% of respondents with data governance frameworks already in place reporting that they have mechanisms in place to measure the quality of their data, compared to just 34% of respondents who don’t already have a data governance program implemented.

With one in three of the overall respondents admitting that they “don’t currently measure the quality of their data”, the implementation of a data governance strategy could provide a real opportunity for those seeking to better understand their data – while also improving the measurement of key business metrics.

Data governance programmes have a lack of cultural awareness

The report also shows that despite data governance programs receiving strong executive support, there is a very real challenge with overall organisational awareness and adoption – with 63% of respondents reporting this as their primary obstacle to success.

To help to address this, businesses are seeking to better support adoption with enhanced training programs. During the survey, 51% of respondents reported some form of training is already place, with a further 26% confirming that a formal program is being planned, and only 23% admitting that no training program has been developed at all.

The relationship between data governance and quality

Data governance and data quality exist in a symbiotic relationship. Each one is essential to the other, and organisations that intend to extract meaningful value from their data assets must be mindful of both. Both require ongoing efforts with neither being a “one and done” proposition.

In some respects, data quality almost functions as a component of data governance. After all, it is hard to imagine effective data governance without addressing data quality. The reverse is also true, though. It is difficult to achieve meaningful levels of data quality without an effective governance framework in place, something which is especially true at scale. For true success, it seems clear that data governance and data quality must be seen as two sides of the same coin.

Find out more about the key trends in data governance and data quality by reading the full report here.