Data quality gets only lip service by too many senior execs

Data quality gets only lip service by too many senior execs

Data quality gets only lip service by too many senior execs
When it comes to maintaining data quality in the enterprise, too many corporate executives have a laissez-faire attitude which ultimately has a negative impact on the business.

That is the conclusion of a new study from 451 Research and Blazent, a Burlingame, CA-based provider of IT data intelligence. The study, the “2016 State of Enterprise Data Quality” report reveals that less than half (40 percent) of C-level executives and data scientists are ‘very confident’ in their organization’s data quality, with the majority (94 percent) recognizing the impact that poor data quality can have on business outcomes.

The report is based on a survey of 200 C-level and senior IT leaders from companies with at least $500 million in annual revenue. It notes that the impact of this attitude disconnect around data quality can affect a number of areas, including lost revenue (cited by 42 percent) and bad decision-making (cited by 39 percent).

“Too often, IT leaders become enamored by the concept of big data without questioning its quality or validity. This report reveals the cost of this oversight and the overall impact it has on the business,” said Carl Lehmann, research manager, Hybrid IT Architecture, Integration & Process Management at 451 Research.

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When asked about the root causes for poor data quality, nearly half (47 percent) cited data migration as a leading cause. In large enterprises, more often than not, IT still bears the burden of keeping data clean (79 percent) despite the introduction of data scientists (26 percent).

The study confirmed the findings of some other recent studies, which point to a problem of expensive data scientists being poorly used for data project initiatives. In cases where data scientists are involved, one-third report spending up to 90 percent of their time “cleansing” raw data. This trend has been identified by some analysts as using data scientists as ‘janitors.

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