first_img 8SHARESShareShareSharePrintMailGooglePinterestDiggRedditStumbleuponDeliciousBufferTumblr Credit unions today are increasingly aware of the mountain of valuable data accumulating in their core and other operational systems. With technology evolving at a rapid pace, opportunities to leverage this data are becoming not only more available but also more affordable than ever before.As a result, credit union decision makers are anxious to ramp up Big Data & Analytics initiatives. However, before plowing ahead with an investment in hardware, software, and services, it is important to consider the risk lurking within the data itself: dirty data.Dishing Up the DirtThe official title for dirty data is “poor data quality”.  However, that seems too clinical considering the mess that dirty data can make for an important Big Data & Analytics program. Yet, when program champions are warned of the dangers of dirty data, they are often skeptical. They point to the source systems from which they plan to pull data and note, correctly, that there appear to be few issues due any dirty data. In fact, they doubt that there is much a data quality issue since these systems run just fine. continue reading »last_img

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