This is the last article of a series that introduced and discusses a life-cycle model for business intelligence (BI) data.
In an earlier article we compared the raw material we process into finished goods to the raw data we process and consume as part of the BI process. The finished product that a factory turns out has a useful life of some number of years. At some point, one of two things happens. Either the product breaks down and is cheaper to scrap than to repair, or a new product makes the old one obsolete. Of course it doesn’t work out that way every time; just think of the thousands of ancient microwave ovens still humming away in break rooms across America.
The same thing that is true with products is true with data. At some point, the data become “stale.” Changing market conditions make the numbers obsolete. But some numbers stay relevant longer. Generally speaking, the higher the aggregation level of the number, the longer it stays relevant. For example, you’ll want to know your total sales and net profit for the whole company going back several years long after a specific product you may have sold back then is off the market. The temptation may be to just delete the old data. But in this age of cheap storage, I would suggest archiving it. You won’t need to use the old data on a daily basis and you probably won’t miss it much. But if you ever need to go back it to do historical research you can.
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