Deliver manageable components of data

This excerpt, from an article by Laura Brown, explains some misconceptions about data delivery.

One faulty assumption of constructing a data warehouse is that you need to import lots of data or an excess of data. After all, don't you want to give the users of the warehouse all the options they could possibly desire? Well importing too much data is a sure sign that your warehouse is set up to fail.

Take this advice from Laura Brown. She's written books on data warehousing, as well as articles like this one from InformIT (http://informit.com/content/articlex.asp?product_id={56C32CA2-3ACF-4B11-9AD7-2DE379C932B3}). This short excerpt explains some misconceptions about data delivery.

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It is better to deliver 70% of the required information in a given business area than to deliver 5% in 10 different areas, because the data becomes meaningful (that is, becomes intelligence) when it can be placed in context. Of course, 100% would be ideal, but the timeframes required to deliver 100% accuracy and completeness of data are never acceptable to the business. And with the shrinking shelf life of the value of data over time, 70% now can be much more valuable than 90% two months from now.

All information is not equally valuable, but requires the prioritization process to find out what's most critical and focus acquisition efforts there first. As the business picture shifts and changes with competition and trends, the priority of data delivery will change to reflect that activity.

When data can be isolated into manageable components and delivered rapidly, more business needs get met, and the sponsors are much more likely to sign on for another parcel of information to be delivered in a reasonable timeframe, just like the first.

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Read more of Laura Brown's advice over at InformIT (http://informit.com/content/articlex.asp?product_id={56C32CA2-3ACF-4B11-9AD7-2DE379C932B3}). Registration is required, but it's free.

For more information, check out searchCRM's Best Web Links on Data Warehousing.


This was first published in February 2002

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