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“The best way to put distance between you and the crowd is to do an outstanding job with informa- tion.”
-- Bill Gates, Business at the Speed of Thought
I wish whiteboards well. I root for their compatriots, the colored markers. My clients have invested heavily in lined notepads, too, and that ubiquitous spreadsheet has always been a trusted friend. It's encouraging to be on good terms with these toolsets since they represent the preponderance of today's data quality solutions.
In a TDWI survey 86 percent of respondents reported that they determined the quality of their data via “manual analysis.”1 The conversation goes deep and broad. Most IT executives acknowledge that “dirty data” is an issue at their companies, many citing data quality as a planned initiative, its budgeting nevertheless delayed until a few years hence. Arguably the connection between the business impact of bad data—data that's incorrect, invalid, mean- ingless, or simply missing altogether—and the tactics for improving it is rarely made in a deliberate, ROI-driven way. While many people experience the pain caused by data quality issues, few actually claim the ownership needed to drive change.
This is nowhere more true than it is with customer data. As economic markets ebb and flow, savvy executives know that cost cutting will only go so far. Generating revenues from existing customers via cross-selling, up-selling, and new product mixes, and casting the net for new ones with integrated multi-channel marketing campaigns, are becoming priorities not just for marketing but for a range of business processes across the company. Executives are turning their heads once again to customer relationship management (CRM), relation- ship selling, and micro-marketing, not to mention innovative new uses for social network- ing and other Web 2.0 innovations. But regardless of the messaging vehicle, data quality is still the root cause of many marketing failures. Customer relationships hang in the balance.
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