Secrets of successful data management: Data Warehouse Automation

Our last blog looked at the history of data management, and what we’ve learned along the way. Despite advancing technology and an appetite for information, there is widespread disappointment in BI development projects. It’s not for lack of effort, but rather that they simply don’t stand a chance to start with.

Insource has identified six secrets of successful data management, to help you avoid these pitfalls and build an insightful, efficient BI project from the beginning. They’re available to view as a webinar – here’s a taster.

Secret 2:
Use Data Warehouse Automation technology to build your Data Warehouse

Self-service BI tools offer faster and easier access to reporting and analytics from your operational data. But they’re only worth using if your data is structured properly. Otherwise, your tools may make assumptions about the way your information is put together, leading to unpredictable results – and unreliable output.

According to Gartner, less than one in ten self-service BI initiatives are watertight enough to prevent data inconsistencies and assumptions impacting their effectiveness. It’s vital to ensure that these tools are working on data that they can easily interpret.

A data warehouse can do this, by simplifying the specific data involved enabling you to get value from your self-service BI tools and thereby increase your chances of successful implementation.

With Data Warehouse Automation (DWA), you can build an effective data warehouse in less time, with less effort. The process of taking raw source data and transforming into organised, compatible reporting structures is generated automatically. Because it’s automated, it’s consistent, so there’s a much better chance of a successful outcome. But of course, this only applies if the original source data is decent in the first place.
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