4 steps organizations can take to enhance data quality

One of the most important aspects of data management is ensuring data quality. Without this, capabilities such as machine learning and advanced analytics might yield faulty results.

What do organizations need to do to enhance the quality of their data? Here are some best practices, according to Michele Goetz, principal analyst at research firm Forrester Research.

Focus on data value

It’s all too easy to equate data quality with data cleansing, which has focused efforts on the wrong thing—data elements over data value.

di-stock-server-070618-1.jpg
Servers stand in a computer room at the Yahoo! Inc. Lockport Data Center in Lockport, New York, U.S., on Friday, Sept. 26, 2014. Yahoo Inc., a $40 billion Web portal, is expected to release third quarter earnings on Oct. 21. Photographer: Andrew Harrer/Bloomberg

“Today’s data use cases are about drawing together information that represents our business and our engagement with customers,” Goetz said. “Data quality professionals need to ensure their policies and business rules for data are always in context of the value data delivers in support of a decision, a great experience, or an automated process.”

If rules, services, and processes are too granularly focused on data elements and records, data quality lacks relevancy.

Link to processes

Don’t assume data quality only happens through data management processes and services running at the database and integration layers, Goetz said.

“Every action, interaction, and consumption of data improves data relevancy and value,” she said. “Operational processes are not only there to inform data quality relevancy and governance, they are also the points at which data quality services run.”

It is important to note that operational processes happen in business processes, transactional processes, and analytic processes. “We don’t always think of analytics as having processes, but the way data is searched, gathered, aggregated, and prepared is a process in context of finding insights,” Goetz said.

Responsibility over ownership

Ownership is a loaded term when the subject of data governance comes up, Goetz said, because it leads to finger pointing and centralization.

“In a business and system landscape that requires sharing, combining and recombining data to get the most value from it, ownership gives way to responsibility,” Goetz said. “Data quality is a team sport. Data quality is a cultural imperative.”

Trust and confidence

“Be clear about what is necessary qualitatively as well as quantitatively, to demonstrate data is ready to use,” Goetz said.
Data quality dashboards showing compliance with rules and standards is meaningless to the data consumer.

“Those help IT and stewards know they are processing data according to those thresholds,” Goetz said. “What matters to the business is that data just works. Metrics, including reports and dashboards, “need to demonstrate that data is working for the business.”

For reprint and licensing requests for this article, click here.