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For decades, data warehouses were extremely “brittle” in the face of new or changing business requirements. Once loaded, their data repositories could not be re-purposed without re-structuring the existing data for the new database design—a practice that requires long and expensive data conversion programming. Luckily, the new, hyper data modeling paradigms eliminate nearly all of this risk.

Ceregenics’ solution architects have traveled the world researching adaptive designs for large data repositories, and found not one, but two solutions to challenge of brittle data warehouses:

1) Hyper normalized data models utilize twice as many data tables as a standard warehouse designs but then make data transforms pattern-based so that your ETL teams need to program only a half dozen or so reusable transformation modules.

2) Hyper generalized data models can store the dimensional information of an entire enterprise data warehouse in less than six logical tables, allowing 80 percent of the data transforms to be generated from machine-readable business models.

Every new business analytics project stands at a cross roads where the development team must choose between slow, expensive standard data models and the new, agile hyper data models. Ceregenics’ knowledge of the new, adaptive design techniques will allow a company to rapidly implement and evolve its business intelligence applications with far less time, cost, and risk.