We are in the upstream data quality software business, so I keep wondering why data quality processes are still run once in a while, rather than as a normal part of the data capture process. Why do most companies start worrying about data only when it’s already dirty, already in the database, and in use? How come it doesn’t occur to them that the quality of data needs to be addressed when it’s actually captured? Since many data quality issues can be addressed at the point of data capture, why don’t more companies use upstream processes to improve their data?
A recent Forrester paper titled It’s Time To Invest In Upstream Data Quality suggests that when companies realize short-term data cleanup ROI immediately, it’s hard to justify front-end investments that may take years.
At the same time, Forrester says, IT budget planning committees tend to avoid the existing data quality (DQ) products that allow integrating downstream data hygiene rules into front-end processes, justifying this by solutions’ cost and complexity.
The result? I&KM (Information and Knowledge Management) pros quickly reach diminishing return on data quality investments, requiring even more investments later on to catch up with missed opportunities like verifying customer contact information, standardizing product data, and eliminating duplicate records.
Read the paper to find out more.
f you are thinking about implementing an upstream data quality solution, or if you already have, chime in here and let us know your thoughts.
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