The incremental movement toward real-time operation is the most influential trend today in data-driven IT disciplines such as business intelligence (BI), data warehousing (DW), and data integration (DI). From a technology viewpoint, collecting, processing, and delivering data is hard enough; doing it in real time requires effort that is downright Herculean. Thanks to the big data phenomenon, the volume of data continues to swell, exacerbating the situation.
When faced with the challenge of making BI/DW/DI solutions operate in real time, the temptation among technical personnel is to omit or reduce some of the best practices of data management in hopes that the simplification will speed up processing. Likewise, new practices around analytics with big data are somewhat thin on data management best practices such as data profiling, transformation, quality, and enhancement, as well as improvements to metadata and master data. The unfortunate consequence for many real-time and very large data sets is minimal quality, richness, and auditability, as compared to data found in the average data warehouse. Data sets delivered as the output of big data analytics can suffer similar issues.
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