Businesses Lack Confidence in Data Analytics

A Blazent study indicated the pace of adoption for more sophisticated algorithmic analysis is accelerating more quickly than originally anticipated.

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Despite a strong appetite for advanced analytics technologies, more than half of businesses have low confidence in their organization's data quality management practices, according to a 451 Research and Blazent survey of 200 C-level and senior IT leaders.

The study indicated 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.

"As the data shows, some enterprises are curating anywhere from 50 to 200 disparate data sources at any given time. With this immense volume of data, it's easy to understand that the curation and cleansing can be a rigorous process," Pradeep Bhanot, director of product marketing, told eWEEK. "This becomes even more complex as nearly 40 percent of enterprises are still collecting and cleansing data through a manual process. However, if properly curated, cleansed and leveraged, the avalanche of data is a good thing in that an organization has a holistic view of the company's data assets and can become extremely agile in making business critical decisions."

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

"By far the most concerning part of the data was the stark disconnect between enterprises wanting and planning to integrate machine learning and predictive analytics technologies, yet the current state of their DQM (data quality management) initiatives are nowhere near where they need to be in order to make the most use of advance technologies," Bhanot said. "What's more, the study revealed that 60 percent of execs lack confidence in their organizations' data quality, showing again the disconnect between where they are with DQM initiatives and where they need to be in order to benefit from the application of advanced analytics."

Nearly 45 percent of organizations are in a reactive mode—relying on reports to find data errors and 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.

"I hope to see organizations being able to apply the insights that machine learning and predictive analytics is able to show them today and fix issues in real time," Bhanot said. "I think the next evolution in DQM is the use of prescriptive analytics, which will apply the best available data using rules that the system has been taught over time."