5 Data Warehouse Design Mistakes to Avoid
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Data Shadow Systems
Mistake 3: Ignoring data shadow systems
It's easy to focus on an enterprise's transactional systems of record and BI, but not all the important information is located there. Key info is also located in "data shadow systems" dispersed throughout the organization. Spreadsheets in Excel contain many of the most useful reports and analytics. Groups of spreadsheets and customized databases make up data shadow systems, especially programs like Microsoft Access and other similar statistical databases. Business processes, like budgeting, forecasting and other reporting tasks are supported by data shadow systems. You will always need to use reporting from your enterprise application and BI, but it would be a grave mistake to ignore the reporting and analysis from data shadow systems.
A data warehouse lies at the base of any business intelligence (BI) implementation project. And if you are designing your data warehouse to help you visualize your company's most relevant data (as well as streamline workflows — which can help your company cut down on redundancies significantly), then you need to map out all the areas where there is a potential for your project to fail, before you begin building.
In this article, Himanshu Sareen, CEO at Icreon Tech, has identified five design mistakes that companies should avoid.
Related Topics : APC, Resellers, Data Replication, Extract Transform and Load, Structured Data Integration
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