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Getting real ROI from data quality initiatives

Richard Jones

June 17, 2014

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Companies across all industries have a clear need to emphasize data quality. For anyone who collects information about their customers, it's important to focus on continually cleansing that knowledge and making sure it remains accurate - any untreated errors with consumer data could potentially lead to missent marketing messages, flawed sales pitches and other mistakes that are embarrassing and costly to fix.

Fixing data quality mistakes should be a key priority for any business - one that's worth investing time and money to get the job done right. But precisely how much? That's a difficult question to answer.

According to EPI-USE Systems Limited, many companies today are grappling with the question of how to extract maximum ROI from their data quality efforts. There's a breaking point somewhere - it's clearly worthwhile for businesses to invest in clean data, but if they invest too much, they may be overspending and not getting enough improvement in return.

To answer the ROI question, corporate data scientists need to have a clear picture of how much they stand to gain from accurate data. How many more sales can they complete if they have accurate addresses and emails for delivering marketing content? How many more dollars will that generate in revenue?

The news source noted that it's sadly quite common for companies to overextend themselves in their quest for greater data quality. The Standish Group has referred to this problem as "migrate headaches" - businesses' data officers are frequently working to move data across their many departments and overcome the "silo problem," but they often struggle to effectively allocate resources for this effort. A staggering 83 percent of of data migration projects costing over $1 million are at risk of overrunning in time and cost or failing outright, the organization found.

With better advance planning and smarter deployment of resources, companies can set themselves on the right track to improve their data quality ROI.

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