Why Bad Data Quality Is Bad for Business

 
 
By Dennis McCafferty  |  Posted 05-17-2016 Email
 
 
 
 
 
 
 
 
 
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    Why Bad Data Quality Is Bad for Business
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    Why Bad Data Quality Is Bad for Business

    Maintaining quality data is no simple feat, as employees manually input flawed data or issues emerge during the data migration and conversion process.
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    Expanding Universe
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    Expanding Universe

    95% of survey respondents expect the number of data sources and the volume of data in their organization to increase in the coming year, with 30% predicting that data volume will increase by 75% to nearly 300%.
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    Top Business-Value Drivers of Data
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    Top Business-Value Drivers of Data

    Increased revenues: 51%, Reduced costs: 49%, Less time spent reconciling data: 47%, Greater confidence in analytical systems: 46%, Enhanced customer satisfaction: 45%
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    Clear Misgivings
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    Clear Misgivings

    Just 40% of survey respondents are "very" confident in their organization's data quality management (DQM) practices or the quality of data within their own company.
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    Inflated Assessment
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    Inflated Assessment

    82% believe their organization's perception of its data quality is better than it actually is.
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    Strategic Liability
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    Strategic Liability

    94% believe that business value is lost as a result of poor data quality, and 29% said that 50% or more business value can be lost.
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    Problems Caused by Poor Data Quality
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    Problems Caused by Poor Data Quality

    Extra time to reconcile data: 45%, Additional costs: 44%, Lost revenue: 42%, Delays in deploying new systems: 40%, Bad decision-making: 39%
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    Likeliest Causes for Poor Data Quality
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    Likeliest Causes for Poor Data Quality

    Faulty data entry by employees: 58%, Data migration or conversion project issues: 47%, Mixed entries by multiple users: 44%, Changes to source systems: 44%, Systems errors: 43%
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    Tools of the Trade, Part I
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    Tools of the Trade, Part I

    62% said their company has a master data management (MDM) program in place to manage data quality, and 61% use DQM software on-premise.
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    Tools of the Trade, Part II
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    Tools of the Trade, Part II

    53% said their organization uses a DQM cloud service to manage data quality, and 46% said they "find errors using reports and then act."
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    Forward-Spinning
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    Forward-Spinning

    67% said their organization has a predictive analytics program either in place, or in the works.
 

With big data only expected to get bigger, IT professionals and other organizational leaders admit that they lack complete confidence in their company's data quality management (DQM) practices, according to a recent survey from Blazent. The accompanying report, titled "The State of Enterprise Data Quality: 2016," reveals that that vast majority of survey respondents believe that their organization's perception of data quality is better than it actually is. Problems are created when employees manually input flawed data, or when issues emerge during the data migration/conversion process. Such problems translate directly to lost business value, in the form of additional costs, lost revenues, bad decision-making and/or delays in deploying new systems. "As foundational as data quality is to an organization's success, a majority of IT execs are not confident in their data quality management practices," said Charlie Piper, CEO at Blazent. "The pace of change and seemingly never ending increase in the amount of data and data sources are significant drivers of this lack of confidence. And, critical business decisions are made without a complete and accurate picture. These findings further validate how crucial it is for IT and the C-suite to continue to prioritize data quality, employing an organization-wide streamlined process for data management." An estimated 200 C-level execs, senior IT pros and key business decision-makers took part in the research.

 
 
 
 
 
Dennis McCafferty is a freelance writer for Baseline Magazine.

 
 
 
 
 
 

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