Innovation at the edges of the Big Data value-chain model has, on the one side, fostered sophisticated BI delivery, and on the other, led to flexible, affordable, and scalable data storage. But in the swampy middle, data delivery continues to play process-innovation catch-up, and is typically muddied with manual data pulls, costly process repetition, error-prone reporting, and unmeasurable data integrity.
Efficient data delivery requires both data unification and data preparation capabilities. Data unification is the collection and management of data from multiple sources. Data preparation enables stakeholders to formulate data—essentially the last-mile processing of unified data sets for business analytics consumption. When the two functions are paired in a data-management workflow environment, the data-driven enterprise enjoys efficient, repeatable, silo-breaking data-delivery capability that sets up analysts and data consumers to achieve greater insights.
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