Why Organizations Struggle With Data Quality

 
 
By Dennis McCafferty  |  Posted 03-31-2015 Email
 
 
 
 
 
 
 
 
 
  • Previous
    Tough Task
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    Tough Task

    92% of survey respondents found at least some aspects of managing data to be challenging, and 50% said they struggle with fixing data quality issues before they negatively impact business.
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    Slippery Slope
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    Slippery Slope

    26% of data maintained by organizations is inaccurate, according to survey results.
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    Elite Standard
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    Elite Standard

    Just one out of four organizations are considered "optimized" when it comes to data quality, with a chief data officer role in place, a platform approach to profiling, monitoring and visualizing data, and the oversight of data as part of standard operations.
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    Lower Bar
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    Lower Bar

    Nearly one-half are considered either "reactive" or "unaware," meaning–at best–there are no data-specific roles in place, and that there are only tactical data fixes within department silos.
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    DIY Data
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    DIY Data

    51% of survey respondents said there is some centralization of data management at their organization, but many departments adopt their own strategy. An additional 12% said all departments adopt their own strategy.
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    Central Focus
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    Central Focus

    Only 35% of respondents said data quality is reviewed and maintained centrally by a single director.
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    Who 'Owns' the Central Data Quality Strategy?
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    Who 'Owns' the Central Data Quality Strategy?

    Chief data officer: 29%, CIO or CTO: 23%, Data governance officer: 13%, CFO: 12%, Chief marketing officer: 11%
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    Mixed Message
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    Mixed Message

    While 93% of survey respondents said their organizations make a proactive effort to discover data quality issues companywide, 57% said such issues are detected when they are reported by employees, customers or prospects–a reactive approach.
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    Top Reasons for Data Inaccuracies
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    Top Reasons for Data Inaccuracies

    Human error: 61%, Lack of internal communications among departments: 35%, Inadequate data strategy: 28%
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    Most Common Purposes of Data Quality Tools
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    Most Common Purposes of Data Quality Tools

    Data profiles: 43%, Monitoring and audit: 42%, Standardization: 33%, Data cleanses: 33%, Data matches and linkage: 31%
 

A significant amount of data within organizations is inaccurate, and many executives admit that they frequently can't fix data quality issues before they negatively impact business, according to a recent survey from Experian Data Quality. One problem: Most companies haven't yet adopted a platform approach to profiling, monitoring and visualizing data. Nor do they track data quality as part of their standard operations. And few have put in place centralized and standardized data management practices, allowing individual departments to come up with their own strategies. As a result, companies fall short of expectations in developing data-driven, actionable insights. "Most organizations are at lower levels of data quality sophistication at this stage," according to the report, titled "Create Your Ideal Data Quality Strategy." "But as investment continues and the chief data officer continues to become more popular, organizations will inevitably advance their strategies into more central functions. The people, processes and technology around data need to operate in a more coordinated fashion to ensure consistency and usability across the business." More than 1,200 global C-level execs, vice presidents, directors, managers and administrative staff took part in the research, which was conducted by Dynamic Markets.

 
 
 
 
 
Dennis McCafferty is a freelance writer for Baseline Magazine.

 
 
 
 
 
 

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