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Data Quality’s Dirty Little Secret

Few marketers would argue that dirty data is a widespread problem. Even fewer could tell you how much of a problem it actually is.

That’s because dirty data—incorrect, incomplete, and duplicate information about prospects and customers lurking in marketing-automation and CRM systems—is a lot like back pain, notes Jerry Rackley, chief analyst for Demand Metric, a global marketing research and advisory firm. Most organizations endure the pain associated with dirty data, but few do anything to alleviate the pain; many simply accept it as a fact of life in the era of analytics.

They shouldn’t, according a new survey on data quality that Rackley and his team conducted. The survey finds that organizations that are experiencing revenue growth are about three times more likely to have clean data than organizations with flat or declining revenue growth. Although excellent data hygiene cannot be said to result in higher revenues, the research indicates that clean sales and marketing data contributes to revenue growth while dirty data inhibits revenue growth.

“The business case for keeping data clean is compelling,” says Rackley, who undertook a survey that he describes as the “the state of the union of sales and marketing data quality [to better understand] what kind of relationship exists between data quality and something that matters.” Revenue certainly should matter to organizational leaders, the majority of whom do not appear to treat data quality as a matter of strategic importance; at least not yet.

Although 63% of survey respondents describe their sales and marketing data as dirty, more than half of them (55%) indicate that their companies have no formal data-cleaning process in place.

That’s disconcerting, but the survey findings also indicate that dirty data hardly qualifies as an insurmountable problem. Asked to identify why their organization lacks a formal data-cleansing process, the largest segment of survey respondents identified the following reasons:

1)      Data hygiene is not a priority (49%)

2)      A lack of data-cleansing skills (37%)

3)      Lack of knowledge about data-cleansing options (21%)

Only 11% of respondents indicated that their companies viewed data-cleansing as being too expensive. “The reasons organizations are not managing data hygiene does not have much to do with cost,” Rackley says. “Mostly, this inaction relates to a sense that data quality is not a big deal; that it’s an out-of-sight-out-of-mind problem.”

Data quality ought to be a front-of-mind problem now that the nature of the issue has been quantified.

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