Data quality now trumps security in new governance initiatives

Enterprise Strategy Group (ESG) and Systems Management Provider Quest software today released their 2022 State of Data Governance and Empowerment Report, an annual study [subscription required] that identifies challenges and innovations in data governance, data management and data ops†

An important conclusion is that the pursuit of high-quality data has overtaken data security as the main motivator for data management initiatives. Forty-one percent of IT leaders agreed that their business decision-making is fundamentally dependent on reliable, high-quality data. At the same time, 45% of those surveyed say that data quality issues have the greatest negative impact on return on investment in data management efforts.

The findings are based on ESG’s survey of 220 business and IT professionals responsible for and/or familiar with data governance and empowerment strategies, investments and operations in their organizations. All organizations represented in the study have at least 1,000 employees and annual revenues of $100 million or more.

Use maximum data

While recognizing the importance of high-quality data, data management leaders are still struggling to improve their data and the ability to strategically leverage and maximize the use of data in practice, according to the research.

Security remains a major concern for all data managers. But the tide has shifted to maintaining high standards of data quality, first to get the right data in place — even before securing it in whatever storage method the enterprise chooses.

“We saw the convergence of data quality initiatives in data governance some time ago,” Heath Thompson, president and GM of Quest’s information systems management (ISM) business, told VentureBeat. “When I looked at this ESG report, it was interesting to see how important data quality has really become a central theme.”

Once you’ve laid the groundwork for data governance, Thompson said, there are a lot of operational things you can start to do, including building policy, whether it’s data security, data ops, or something else. “That’s how the industry is starting to do it now.”

Dataops is used in data quality initiatives

Dataops is a collaborative data management practice aimed at improving the communication, integration, and automation of data flows between data administrators and data consumers in an organization.

While the challenges of data visibility and observability differ across industries, the study overwhelmingly recognized data ops as the primary solution to advance data empowerment, Thompson said. Ninety percent of respondents agreed that strengthening data ops capabilities improves data quality, visibility and access issues across their business.

The greatest opportunities to improve data ops accuracy and efficiency lie in investing in automated technologies and deploying time-saving tools, such as metadata management. The survey found that only 37% of respondents describe their data ops processes as automated, and an equally small proportion report that they automatically catalog and map data today (36% and 35%), respectively.

“Reliable data and efficient data processing have never been more influential in determining the success or failure of business goals,” said Patrick Nichols, CEO of Quest, in a media advisory. “If people don’t have access to high-quality data and don’t have the confidence and guidance to use it appropriately, it’s virtually impossible for them to achieve the results they want.”

The report also revealed that business leaders struggle not only to understand their data, but also to locate and use it, Thompson said. Forty-two percent of respondents said that at least half of their data “dark data” – that is, retained by the organization, but unused, unmanageable, and untraceable. An influx of dark data and a lack of data visibility often leads to downstream bottlenecks, hampering the accuracy and effectiveness of operational data, Thompson said.

“We’re not talking about the dark web here,” Thompson said. “The shady data we deal with is all that data that is collected but never used by any company. Dataops automation is being used to solve this.”