Why Data Quality is at the Heart of Data Governance and Intelligence

Poor data is the bane of many organizations, risking business, financial and physical health. Take this report on a letter from the European Systemic Risk Board as a stern example of the risk to financial systems:

If poor-quality data poses a risk to European securities and financial markets, already among the most regulated and federated, then other enterprises should be sitting up and taking note. For example, Gartner's research suggests that poor data quality costs organizations an average of $12.9 million per year.

With that in mind, it’s good to see that the same report expects 70% of organizations will rigorously track data quality levels via metrics in 2022, improving it by 60% to significantly reduce operational risks and costs.

But how does an enterprise ensure its data quality is good and that dark data, or unknown or unused data, is uncovered from silos and unofficial services? And how do governance and intelligence tools ensure it remains consistent across times of frantic growth and increasing demands to use data as a strategic and tactical asset?

Data quality is a top driver for data governance leaders

Data leaders are a talkative segment of the IT audience and their input into the 2022 State of Data Governance and Empowerment report highlights their focus on data governance to deliver results across key business areas, with the results showing:

• Improving data quality (41%)
• Improving data security (38%)
• Improving business analytics (31%)

Whatever the business need or ambition, data governance solutions are increasingly available, with automation and integration features to provide visibility, business context and governance guidance surrounding business data. They support the application of features such as data profiling, quality assessments and scoring to understand the quality and fitness of your organization’s data. They also aid in sharing visibility widely among stakeholders to ensure wide usage of data analysts and decision makers, and the ability for teams to pinpoint critical areas for needed data remediation and quality improvement.

While these tools exist to provide surgical expertise and as part of improving data analytics applications, there are plenty of challenges when it comes to delivering on data quality goals. Technically, the quality, availability and accuracy of the data can be varied at best. Many enterprises lack the technical skills to implement data quality improvement processes, and across the organization, there can be opposition to or unclear leadership for such efforts.

Clearly, data quality and governance efforts start at the top, but they must be a broad effort, engaging all users and stakeholders to ensure widespread data quality visibility, literacy and collaboration.

Data quality integration with data intelligence software, whose impact is clear across areas as diverse as the auto industry and property investment, is foundational to solid enterprise visibility. As automation takes over and data sources become more nuanced, all verticals and markets will find new ways to use their data, with all enterprises focused on discovering and leveraging their unique data value.

What enterprises need for good data quality

Data quality is a major issue for enterprises, with the volume, continued growth and various data sources all posing a challenge. Data governance solutions such as erwin offer the tools to ensure the quality of data to deliver that strategic advantage.

By providing visibility and automation, erwin helps organizations see and understand the quality of their data, and target areas to remediate to make data more trustworthy and reliable. Metadata-driven automation, leveraging data catalog information and data quality tools automate many tasks to reduce the workload for data quality engineers, IT and data governance teams.

With high-quality data, the business leadership has access to timely and ongoing business intelligence sources, with data governance now an ongoing part of that operation. This gives all business stakeholders added confidence in the data they are using to make decisions and drive business processes, and reduce costs and risks associated with the poor data quality most organizations today experience.

While data profiling and quality assessment are a key initial part of data governance efforts, data quality measurements must be ongoing as that quality will vary over time. Regular data quality scoring ensures that current data is reliable and can highlight problems as they emerge rather than after the fact, minimizing issues.

Big data will only get bigger, and faster. Analyzing it requires modern tools that can automate much of the processes, leaving a clean dashboard with glowing insights for the leadership to consider. Without good quality data, even those efforts will fail, which is why data governance should be leading these initiatives.

For more information, and the changing trends that this barometer on data governance provides, check out the full report “ESG Research Insights Paper: 2022 State of Data Governance” and prepare your business for the data-first future.