Executive Summary (This is a document excerpt)

Information & knowledge management (I&KM) professionals typically focus on cleansing "dirty data" downstream — like in the data warehouse — rather than on improving data capture and validation processes upstream. Why? When you can realize short-term data cleanup ROI immediately, it's hard to justify front-end investments that may take years. Ironically, many data quality (DQ) products can integrate downstream data hygiene rules into front-end processes, but IT budget planning committees avoid them due to cost and complexity. The result? I&KM 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. Break this cycle by identifying the optimal DQ solution downstream and auditing source systems that cause the most significant data issues upstream.

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