by Angela Guess
A recent whitepaper by Jim Harris examines the role of data quality monitoring in data governance: “When the correlation between poor data quality and poor business performance isn’t measured in a tangible way, data quality can be misperceived as a technical activity performed for the sake of the data, instead of an enterprise-wide initiative performed to provide data-driven solutions for business problems.”
Harris continues, “The common mistake is taking a data-myopic approach to data quality metrics, i.e., creating metrics that reflect the quality of the data in isolation. Without understanding how the organization is using its data, and how data quality affects business results, data cannot be called a corporate asset. Data is an asset only if the organization can qualify and quantify its value by connecting its usage to business objectives.”
The paper goes on to discuss three main topics: “(1) Defining Data Quality – Examines the two most prevalent perspectives on defining data quality, since how data quality is defined has a significant impact on how data quality is measured. (2) The Role of Data Governance in Data Quality – Examines how data governance provides the framework for a proactive data quality program, ensuring that data is of sufficient quality to meet the current and evolving business needs of the organization. (3) The Role of Data Quality Monitoring in Data Governance – Examines how compliance metrics associated with data governance policies align data quality with business insight, providing the historically missing link between data quality and business performance.”
Click here to read the full whitepaper.
