by Angela Guess
Mark Brunelli recently reported, “Organizations need to focus on being more proactive when designing or updating data quality management strategies, according to IT industry consultants and technology professionals. Experts say organizations spend lots of valuable time cleaning up data quality problems in the data warehouse and business intelligence (BI) environment, when a more proactive strategy could save time, ensure more reliable information and ultimately help business workers make better decisions.”
Brunelli goes on to refer to several experts: “’You have to be proactive,’ said Suvendu Datta, a data warehouse team leader with a large insurance company. Datta’s team is in the process of implementing a new data warehouse, along with new data quality policies. ‘I think you should take care of data quality [in] the design phase.’ But experts warn that the decision to design a more proactive data quality management strategy comes with a special set of challenges. For one, it’s difficult to justify an investment in improving the data capture and validation processes that occur ‘upstream’ from the data warehouse, said Rob Karel, a data management analyst with Cambridge, Mass.-based Forrester Research Inc.”
He continues, “Upstream data quality projects can take years to perfect, Karel said, and the return on investment (ROI) may be difficult to measure. Instead, most organizations opt for data quality tools that are geared toward normalizing data in the warehouse. Such “downstream” tools are easier to justify because they produce tangible results more quickly. Yet while these downstream data quality tools do their jobs reasonably well, Karel thinks they’re no substitute for capturing data properly in the first place. By taking the time to engineer more effective data collection and governance techniques, he said, organizations will greatly reduce inefficiencies and increase the level of confidence in business information.”
