Traditional data models are a major cause of delivery delays and poor outcomes for DW/BI systems. The old-school paradigms of Inmon or Kimball both lead to data warehouses that are extremely brittle in the face of new or changing business requirements. With these out-dated modeling techniques, design changes require far too much re-programming and data conversions, wasting the time and money that you have.  Luckily, the new, hyper data modeling paradigms eliminate enormous DW/BI re-engineering costs.

Having traveled the world to research adaptive designs for large data repositories, Ceregenics’ solution architects unconvered not one, but two solutions to the challenge of brittle data warehouses:

1) Hyper normalized data models utilize twice as many data tables as 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 one of the new, agile hyper data modeling techniques. Ceregenics’ knowledge of the new, adaptive design methods will allow a company to rapidly implement and evolve its business intelligence applications with far less time, cost, and risk.

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ADW Set - front covers 150 pxh transCeregenics wrote the book on agile business intelligence techniques–in fact all three of them.  No other consulting company can offer you the breadth and depth of the solutions for EDW and big data development challenges. Use the “Contact Us” option above to request a conversation with one of our solution architects.