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Unfortunately, no company has completely clean data The old saying is that as soon as you make something idiot proof, the universe creates a bigger idiot While this is likely true, it s also a fact that many applications allow bad data into the system in the first place Having bad data then makes analysis much more difficult Here is a real world example: A company had a large presence in the city of Ann Arbor, Michigan One of the systems asked customers for their address and the input box for the city was a free-form text field People living in the city of Ann Arbor found (at last count) fourteen different ways to spell Ann Arbor These included Anne Arbor, AnnArbor, AnneArbor, Ann Arbour, and so forth The easy solution going forward would be to fix the application so that the city was chosen from a drop-down box Unfortunately, the owners of that application were busy and didn t see it as a priority Even if it had been fixed, however, this did nothing to handle the existing data that was incorrect At this point, the business could have gone in and cleaned up all the source data Instead, they chose to do the cleanup in their ETL process The source system could continue to hold misspellings of Ann Arbor, but the data in the warehouse would be consistent This was important because they wanted to analyze sales by city and when someone chose Ann Arbor, they wanted all sales for that area to come up without having to know to add in many misspellings of the city name The ETL process was written to handle all known misspellings of Ann Arbor Periodically, a new one would appear and the ETL would have to be updated Note that today, this would have been much easier thanks to the Fuzzy Lookup transformation in SSIS, but at the time the customer had to do everything manually Given that there are such data quality issues inside any organization, it s important for the business to come up with the rules for handling such issues It s appealing to be able to say that everything will be fixed in the warehouse, but sometimes the effort to do so outweighs the benefits Imagine a system in which bad records are flagged as unknown If the estimate to truly fix the data is 2000 man hours, and less than one-tenth of one percent of the data is bad, is it worth the cost to fix On the other hand, if 40 percent of the data is bad, fixing the data probably has a much higher priority Articles, whitepapers, and books can be written about data quality and the issues around it The fact is that most organizations have bad data and still allow bad data to flow into the system There are ways to ameliorate this but eliminating it entirely is often more costly than just living with some level of dirty data It s important for business users to understand why bad data is in the system and what options exist to fix it, and why it might be necessary to see some percentage of data listed as unknown. java upc-a Generate UPC-A barcode in Java class using Java UPC-A ...
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