Is your data warehouse still living in the 90s?

Is your data warehouse still living in the 90s?

Is your data warehouse still living in the 90s?

Traditional approaches to data warehousing have changed--has your data warehouse?

“Old habits die hard,” says Wayne Eckerson, a thought leader in the business intelligence and analytics field since the early 90s. “Too many companies rely on traditional hand-coded and labor intensive approaches to building a Data Warehouse.”

If you're responsible for building or maintaining a data warehouse, that probably rings true for you. And in this age of automation, in which robots are used for everything from milking cows to gathering customer data on a phone call, why is it that it's taken so long for automation to be incorporated into the data warehouse?

The data warehousing process historically has gone something like this: gather requirements from business users, design a data model to support those requirements, locate the data sources, and load the data into a star schema and develop Business Intelligence (BI) objects. Perfectly logical, right? Until the business team realizes that what was considered ideal during the requirements process isn’t so great now. Or that what would be much better this month is to see the rate of fulfillment instead of rate of conversion--but the fulfillment data hadn't earlier been deemed worthy of importing into the warehouse.

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The traditional, static data warehouse is as passé as Air Jordans, backwards baseball caps and the Macarena.

Today's modern data warehouse requires a level of flexibility to keep up with changing business needs. That's why we've dubbed the modern system the Data Discovery Hub, which is distinguished from traditional data warehouses by its agility, simplicity and flexibility, all of which are enabled by simple automation.

A key tenet of the Data Discovery Hub is this: import all your available data. Don't waste time and energy deciding which data should be brought in and which won't be useful.

Think of it this way: imagine you're going to move to a new house. Conventional wisdom requires that you painstakingly sort through every object you own before moving and anticipate whether or not you'll need it in your new home. This process is not only time-consuming but also runs the risk of leaving behind items that could be useful but weren't determined to be so at the time. And then you have to either rush to replace those items or simply do without.

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A data discovery hub works more like this: every item you own is packed up into neatly labeled boxes and automatically sorted for later use.

 



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