2 posts tagged with "Ralph Kimball"

For two and a half decades or more, organisations have followed one of the two data warehousing principles of Inmon (CIF, GIF and DW2.0) and Kimball (Star Schema Fact models) for preparation of data. Both methods have intrinsic benefits but leave organisations with some challenges in accessing warehoused data. More recently Amazon S3 has provided data lake capability which similarly requires deep IT knowledge and a somewhat prescriptive data understanding to be of downstream value.

By taking the best of both data warehousing practices a new paradigm is possible however, one fundamental mindshift is required to bring the value of data closer to the surface for business self service, analytics and reporting. This mindshift is the breaking of the human paradigm of clustering data in a format that represents the data source (Transactions remain transactions, readings remain readings and functional records remain functional records) as this is the area that restricts data from being used in a more abstracted way.

For more than 25 years now, businesses have followed a pattern of mapping business transactions in their own context into a data warehouse. Realistically, this practice, driven by the ideals and methodologies of either Bill Inmon or Ralph Kimball, has to-date provided a great ability to re-format transactional data into a model that enables rapid retrieval and consistent enterprise reporting platforms. I believe that through this analytics evolution cycle, we have become a victim of our own design in that the value delivered from a data warehouse built in this manner is, by and large, not conducive to encouraging new questions to be asked of your business data. This stifled pattern of use leads us to, typically, answer questions from complex data warehouses for answers that we could just as well have built a static report from the operational system.

What’s the point you might ask, well, simplistically, if the business transaction from one area in your organisation looks like and apple and another looks like an orange, the reality is that it is difficult to compare or analyse the two together. There are a number of anomalies of which timeousness of information, granularity and the absence of common associative information are but a few that restrict us in doing this effectively. So, if we insist on making each warehouse entry look like the apple or orange from where it was sourced, the warehouse is, by design, failing to deliver much if any value other than migration of the reporting platform to a different system.