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How a Lack of Data Quality Hurts Business

Mar 20, 2017

How a Lack of Data Quality Hurts Business

How a Lack of Data Quality Hurts BusinessHow a Lack of Data Quality Hurts Business

Many managers don’t trust their data to make important business decisions. Instead, they rely on “gut feelings.” Find out how IT execs can improve data quality.

Fleeting FaithFleeting Faith

Just 44% of the data management professionals surveyed said they trust their organization’s data to make important business decisions.

Just a HunchJust a Hunch

52% said they rely on “gut feelings” to make decisions based on data.

Room for ImprovementRoom for Improvement

Only 39% of the survey respondents said their organization is proactive regarding data quality, or is achieving optimized data quality.

Disjointed ProcessDisjointed Process

Just 25% said their organization has centralized control of data quality with a single director, while 56% said there is some centralization, but many departments adopt their own data quality strategies.

Emerging RolesEmerging Roles

51% of C-level executives surveyed said they plan to hire a chief data officer to support centralized data management, and 43% said they plan to hire data protection officers.

Tech-DrivenTech-Driven

62% of the survey respondents said that IT has the most influence on the handling of data.

Biggest Business Drivers of Data QualityBiggest Business Drivers of Data Quality

Increased revenue: 50%,
Better customer service: 50%,
Reduced risk: 37%,
Enhanced marketing efforts: 35%,
New revenue streams: 35%

Consumer FocusConsumer Focus

43% of the survey respondents said they need a single view of customers to increase retention and loyalty, and 40% said they need this to improve strategic decision making.

Improved EffortImproved Effort

33% said human error leads to a lack of customer contact data accuracy, but that’s down from 56% who said this a year ago.

Data Management Projects for Next YearData Management Projects for Next Year

Data cleansing: 33%,
Data integration: 31%,
Data migration: 28%,
Data preparation: 26%,
Data enrichment: 26%

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