A new study by 451 Research reveals that there is a significant lack of confidence in data quality management despite the desire by many organizations to implement more advanced data-reliant initiatives.

The study, which was commissioned by Blazent, a leading data quality management company, polled more than 200 C-level and senior IT leaders. Entitled The Role of Data Quality Management in Machine Learning and Predictive Analytics, the report revealed that while nearly two-thirds (67 percent) of respondents “have a strong appetite for advanced analytics technologies,” more than half (60 percent) have low confidence in their organization’s data quality management practices.

“Given IT’s reliance on data to perform these initiatives, until data quality processes are properly managed, businesses will continue to suffer the limitations of garbage in, garbage out results,” the report noted.

“Based on this new research, the pace of adoption for more sophisticated algorithmic analysis is accelerating more quickly than originally anticipated, with 22 percent of respondents already having a program in place and 42 percent planning to implement one in the next 12 months,” the study concluded. “However, to capitalize on this promise, the enterprise must move data quality management initiatives to the top of the list of both IT and C-suite priorities – a far cry from where it currently stands, with nearly 40 percent still relying on rudimentary manual data cleansing processes.”

Advanced analytic technologies that were once thought to be cutting edge are becoming more mainstream. Still, many organizations are still struggling to put the proper data management processes in place, said Carl Lehmann, research manager, Hybrid IT Architecture, Integration & Process Management at 451 Research. “As the research reveals, the promise of more advanced technologies like machine learning and predictive analytics will remain a unfulfilled until organizations have remedied their data quality management issues.”

Among the other key study findings:

• “In addition to the 67 percent of respondents who will use machine learning for predictive analytics, 66 percent will use it to enhance their recommendation engines and 59 percent for analysis segmentation.”

• “More than 50 percent aren’t sure if all of the data sources required for their business purposes had been integrated before the cleansing process.”

• “Nearly 45 percent of organizations are in a reactive mode – relying on reports to find data errors then hoping the proper corrective, often manual, action takes place.”

• “Nearly 10 percent of organizations have avoided data quality management completely, opting for a “hope for the best” approach.”

• “Half of respondents believe that the DQM practices put in place and therefore the quality of data used were only satisfactory/good enough – but admittedly not great. “

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