By Rob Karel.
Quality data in the enterprise is like breathable air — you don't truly appreciate it until it's gone. Many companies don't even bother to ask whether the customer, product, asset, or any other data it captures is actually complete, valid, and useful. Other companies leave the responsibility of standardizing, cleansing, and aggregating data from source systems to their IT developers, perhaps leveraging transformation capabilities within extract, transform, and load (ETL) tools to automate this hygiene process.
Then there are those companies that have felt enough data quality-induced pain such as wasted marketing costs or low call center productivity, and have invested in data quality software that allows for the advanced definition and maintenance of rules to standardize, cleanse, enrich, match, and merge. Once an investment in data quality software is made, companies hopefully have invested also in staffing at least a handful of data quality stewards or business analysts. These data quality professionals (DQPs) can translate requirements and perspectives of quality from the business stakeholders to technical requirements that can be implemented within the DQ software.
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