Why Good Data Goes Bad

 
 
By Dennis McCafferty  |  Posted 01-27-2016 Email
 
 
 
 
 
 
 
 
 
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    Why Good Data Goes Bad
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    Why Good Data Goes Bad

    There is a disconnect between workers held accountable for data quality and those that are responsible for its capture and use.
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    Data Nation
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    Data Nation

    98% of survey respondents expect the volume of data within their organization to increase in the coming year, and nearly half say it will increase by at least 50%.
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    Business Value Drivers of Data
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    Business Value Drivers of Data

    Increased revenue: 51%, Reduced costs: 49%, Decrease in time spent reconciling data: 47%, Boosted confidence in analytical systems: 46%, Improved customer satisfaction: 45%
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    Fleeting Faith
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    Fleeting Faith

    Just 40% of survey respondents are "very" confident in their organization's data quality management (DQM).
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    Oversight Essentials
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    Oversight Essentials

    62% have formal Master Data Management (MDM) program technology, and 61% use DQM software on premise and 53% use a DQM cloud service.
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    Bottom-Line Burden
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    Bottom-Line Burden

    94% believe that business value is lost as a result of poor data quality, and nearly three out of 10 say 50% or more of business value is lost due to data quality issues.
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    Glaring Shortcomings, Part I
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    Glaring Shortcomings, Part I

    71% say their data's integrity must be addressed, and 68% say the same about its accuracy.
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    Glaring Shortcomings, Part II
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    Glaring Shortcomings, Part II

    58% say their organization must improve the consistency of its data, and 54% say the same about the data validity.
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    Causes of Poor Data Quality
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    Causes of Poor Data Quality

    Data entry by employees: 58%, Data migration or conversion projects: 47%, Mixed entries by multiple users: 44%, Changes to source systems: 44%, Systems errors: 43%
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    Machine Mania
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    Machine Mania

    44% say they will either initiate an Internet of things (IoT) program for the first time this year, or they will expand an existing IoT program.
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    Types of Machine Learning Programs in Place or Planned
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    Types of Machine Learning Programs in Place or Planned

    Predictive analytics: 67%, Recommender systems: 67%, Cluster analysis and segmentation: 59%
 

A minority of top execs express high confidence in their organization's data quality management (DQM), according to a recent survey from Blazent. The resulting report, titled "The State of Enterprise Data Quality: 2016 Perception, Reality and the Future of DQM," indicates that nearly all organizations will continue to see an increase in data volume. That's a good thing, since they believe data initiatives can increase revenues, reduce costs and improve customer satisfaction. But there are too many doubts about data integrity, accuracy and consistency to maximize the value of this asset. Issues with data migration and conversion projects are creating many of the difficulties. But the biggest problems are caused by the employees who conduct data entry. "There is a disconnect between those persons held accountable for data quality and those that are responsible for its capture and use," according to the report. "While the IT department is mainly held accountable, the originators of data (e.g., employees, cross-functional teams, others) are not responsible for data quality upon capture or entry. IT departments are burdened with the task of employing multiple cleansing technologies to compensate. Some of those means are rudimentary and manual in nature, and apparently oblivious to the originators or curators of data … The gap between those held accountable for data quality and those responsible for its capture and use is opaque and problematic. It leads to a lack of empathy between the two constituencies." An estimated 200 C-level, senior IT and key business-decision making execs took part in the research, which was conducted by 451 Research.

 
 
 
 
 
Dennis McCafferty is a freelance writer for Baseline Magazine.

 
 
 
 
 
 

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