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Data Quality: Data Capture Complexity Stymies the Collection of Quality Data
How good is the quality of data used by enterprises today? Not great, but then data quality has been a long-standing problem across enterprises. The problem is that poor quality data can distort the results of data analytics and decision making.
Only 40 percent of enterprise IT professionals today have high confidence in the quality of the data that they use, according to 451 Research. The flip side of this, of course, is that 60 percent of professionals question their data quality. Part of the problem is the wide range of sources from where enterprises are getting their data. More than half of organizations use more than 50 different data sources. The complexity involved in capturing data from such a wide range of sources can be daunting.
Carl Lehmann, Research Manager at 451 Research, said that “advanced analytic technologies, which were once thought to be cutting edge, are now moving towards mainstream adoption, but enterprises are still struggling to put the proper data management processes in place… the promise of more advanced technologies like machine learning and predictive analytics will remain a pipedream until organizations have remedied their data quality management issues.”
Gary Oliver, former CEO at Blazent, said that “while data scientists became one of the most coveted roles in IT this past year, the reality is that CIOs and IT leaders still carry the burden of maintaining the proper checks and balances for data quality, and it will be incumbent on them to solve this unwieldy problem as data volumes continue to escalate”.
