Furthermore, as more and more organizations are going through digital transformations, analytics capabilities are getting more sophisticated. The complexity of data quality management is growing while the risk and impact of bad data quality is also rising.
Consider a high-level meeting to review company performance: if you learn that two reports compiled from supposedly the same set of data reflect two different revenue figures, no one can know which figures are accurate, which could cause important decisions to be postponed while the “truth” is investigated.
One of the causes of data quality issues is in source data that is housed in a patchwork of operational systems and enterprise applications. Each of these data sources can have scattered or misplaced values, outdated and duplicate records, and inconsistent (or undefined) data standards and formats across customers, products, transactions, financials and more.
Data quality problems can also arise when an enterprise consolidates data during a merger or acquisition. But perhaps the largest contributor to data quality issues is that the data are being entered, edited, maintained, manipulated and reported on by people.