According to a recent TDWI survey, 38% of organizations surveyed are practicing advanced analytics today, whereas 85% say they’ll be practicing it within three years. This dramatic increase in advanced analytics is driven by organizations’ need to understand constantly changing business environments, as well as to discover opportunities for cost reductions and new sales targets. There are different analytic methods users can choose as they move beyond basic OLAP-based methods and into advanced analytics. The majority, however, seem to be choosing SQL-based methods, because they know and trust SQL, plus they can leverage the SQL-based tools and skills they already have.
This trend is pushing SQL-based analytics to a new extreme. With “load and go” methods, users quickly load a few terabytes of raw operational data and go at it with ad hoc queries until the data reveals the answers they need. The ad hoc queries get more complex with each iteration by a business analyst or similar power user. This method doesn’t allow time and resources for data transformation, cleansing or remodeling, so users compensate with lots of WHERE clauses, table joins, and temporary tables (when necessary). SQL-based analysis at this advanced level is powerful, but it only succeeds when supported by an analytic database management system that can quickly execute extremely complex SQL statements run against multi-terabyte volumes of raw data, in a schema-neutral fashion that supports “load and go” practices.
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