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| Thought Paper |
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Imperatives of Data Quality for Supply Chain Efficiency |
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| Abstract |
In the simplest form, data quality discusses the incompleteness or incorrectness of data.
On broader terms, data quality is achieved when a business uses data that is
comprehensive, consistent, relevant and timely. Low data quality requires huge efforts in
collation and verification of material availability leading to delay in effective decisionmaking
process.
According to a recent Gartner research, 25 percent of Fortune 1000 companies are
working with poor quality data. The Data Warehousing Institute (TDWI) estimated that
data quality problems cost U.S. businesses $600 billion each year. With regulatory
initiatives such as Sarbanes-Oxley and Basel II dictating that companies must provide
transparent data, it becomes imperative for organizations to ensure optimum data quality.
This paper highlights the key reasons that are responsible for data inconsistencies and
suggests solutions for improving data quality across an enterprise.
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