Data Quality A Thankless Task — Until A Crisis Arises

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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Sesame Street 2.0

Web 2.0 is hitting the preschool set, as children's TV favorite Sesame Street is now producing a weekly video podcast. Each video podcast is five minutes long and features content repurposed from the show’s broadcasts. The content is available as a download on the show’s Web site and via an RSS feed, as well as through iTunes (apparently to help keep the wee ones occupied and educated while they're being dragged around town on errands).

So even the Muppets are finding new ways of engaging their customers by distributing Web 2.0-type content through multiple channels. Hope they’re using a digital asset management system and a good solid taxonomy while they’re at it.

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