The IT department is usually held responsible for maintaining quality data, but those entering the data are not. “Data quality responsibility, for the most part, is not assigned to those directly engaged in its capture,” according to a survey by 451 Research on enterprise data quality.
When perceptions of the two groups — those “accountable” and those “responsible” — are misaligned, data quality suffers, the report said, and IT departments must employ multiple cleansing technologies to compensate, even as the volume of data grows.
Clean data is important for business. Sixty-five percent of respondents said that nearly half of business value can be lost to poor data quality, and 29 percent thought the consequences would be were worse.
Bad data comes from many sources, but the biggest cause is improper data entry by employees. Data migration or conversion projects come in second, followed by mixed entries by multiple users, changes to source systems, systems errors, data entry by customers and external data.
Problems caused by poor data quality are numerous. It forces staff to spend extra time reconciling data, contributes to extra costs, lost revenue delays in deploying a new systems and bad decision making.
Despite recognition of the problem, respondents’ confidence in their organization’s data quality management (DQM) practices is not high. Less than half (40 percent) of respondents to the survey were very confident that all the data sources required for their purposes had been aggregated prior to cleansing.