Skip to main content

Data quality efforts can harness disparate processes

Keeping data quality strong is a high priority effort for companies, and there are a number of different programs that can help. According to a recent study by Bloor Research, organizations should incorporate a profiling step into their data management efforts, combining tools commonly considered part of an analytics program with cleansing processes.

According to Bloor, companies can improve their interaction with data by applying automated profiling functions during their early life cycle. This process should, according to the source, take into account both empirical data quality measures and the information's future business purpose.

The report suggested that companies can actively track data quality issues and their progress over time by applying an analytics-style dashboard to information cleansing efforts. This means running profiling processes alongside classic management efforts. This checking can also act to demonstrate compliance with regulations, according to Bloor.

Data quality processes are sometimes straightforward, but often require extra effort from IT staff. Workers can incorporate new techniques. According to eWeek, the moments leading up to a data migration are an important time for cleansing operations and assessments, especially if the information comes from legacy hardware.