Four Ways to Ensure Quality Data
Companies have access to more data today than ever before, but if the quality is questionable, so are any inferences from it. As the old — very old — computer science saying goes: “Garbage in, garbage out.”
“In today’s market, the risk of having poor quality data increases as the volume of data an organization ingests increases,” said Dan Lynn, Crux Data senior vice president of product. “One bad dataset can have a detrimental impact on your business, with that risk multiplied every time someone new touches a dataset. Yet the risk of poor data quality is often accepted because this is just the status quo for data operations.”
Ensuring the quality of external data is especially hard because suppliers in the data industry all have unique schemas and languages for transmitting their specific data sets to their customers, according to Lynn.
But Lynn and other data experts pointed to the following ways for organizations to ensure the quality of their data:
Define your organization’s standards for data quality, Lynn recommended. The first step in improving data quality is to know the various data you possess. Each data supply has its own sources, formatting, processing and abilities — no two are the same.
Having a proven set of data quality measures based on supplier and business specifications allows for quality measurements, anomaly detections and data that’s analytics ready, enabling the organization to make better business decisions.
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Reimagine holistic data quality across several dimensions, added Amaresh Tripathy, Genpact global leader of analytics. “Ensure complete coverage of traditional data quality, including completeness, conformity, duplicates, activity and timeliness. Include data management process metrics that create and manage foundational data like the right first time, cycle time, new customer set up experience, and the product.”
It also establishes the downstream impact of data quality, Tripathy said. “For example, the number of incidents in business processes and their impact or rework due to poor quality of foundational data, compliance to regulations, and future readiness to support strategic initiatives like a CRM implementation or connected planning initiatives.”
Supply baseline visibility of the size and scale of data quality issues through an automated dashboard coupled with a central team, enabling an organization to identify and tag data quality issues, Tripathy said.
“Provide a starting point for decision-makers to prioritize investments in improving data quality,” Tripathy added. This includes educating leadership on the composition of the data universe per domain (e.g., volume, distribution across countries, number of attribute groups and their quality), creating a heat map of data quality issues and identifying what needs attention.
