Businesses everywhere are racing to uncover more and more data, which they hope will give them a greater understanding of their customers and the world around them. By gathering more information about past and present purchases, marketers should be able to craft more intelligent strategies that will help increase profits.
There's a double-edged sword here, though. Companies are racing to collect data - more, more, more. And thanks to all the modern technology out there, there's plenty to be collected. But how reliable is all that information? Can companies sleep easy, trusting that they'll have the required levels of data quality?
Not necessarily. One of the world's biggest buzzwords right now is "open data" - citizens and government offices are putting heavy volumes of information out there for free public consumption, and companies are in many cases racing to capitalize. But if data is truly "open" and basically ungoverned, how can anyone trust that it's without errors? Misspellings? Outdated elements? Duplicate entries?
Embracing open data blindly feels like a mistake, but so too does eschewing it altogether. We'll need to find a middle ground.
What can be done?
When companies get deeply invested in "open" data, they need to be more vigilant about ensuring its accuracy and making sure it's a fit for their specific goals. According to Business 2 Community, it's a matter of devoting more time and manpower to data quality. Martin Doyle, who founded DQ Global in 2002, noted that some real investment may be necessary.
"The scale of error experienced proves that high-quality data is no accident and requires effort and commitment to achieve," Doyle stated. "As we become more connected, businesses and the public sector are realizing that investment is needed to make sure our data is fit for purpose. High profile failures should only cement our willingness to succeed. Your company data is an asset, ensuring it is accurate and up to date is not a just a requirement - it is a necessity."
Data leaders in corporate offices may need to craft specific standards that define what make consistent data - what formats will be used for certain elements, and how data clusters will be encoded in order to keep elements secure yet readable on command. They also need to check diligently for such issues as duplicate entries or pieces of data that are dependent upon a specific piece of software rather than self-sufficient. Data governance is hard work, but it's well worth it for companies that care about gaining a competitive advantage.
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