As mobile applications and social media apps become more prominent in every company's strategies, and old-style methods for direct marketing remain popular as well, data is flooding into the coffers of small and large businesses alike, and they're using it to tackle a variety of challenges. Analytics can help business leaders design better products, deliver smarter sales pitches and provide more reliable customer service.
None of these initiatives, though, are anywhere near 100 percent effective. Though companies set out every day to begin new data-driven initiatives, answering big questions they may have previously never thought possible, there are many instances in which they fall short. A common problem is one of data quality - the information they're using to solve these big business riddles is inaccurate, or incomplete, or fails to address the real question at hand.
Data quality issues can be big or small. It might be a major problem such as a database of thousands of entries in the wrong format, causing them all to be unreadable, or it might be something minor like a single misspelling or outdated address. But all data quality difficulties take a toll - as they add up, they make it more and more unlikely that businesses can find success with analytics.
Enterprise Apps Today recently explored this problem, emphasizing that it only grows more daunting as the amount of data out there continues to balloon. Pedro Cardoso, data governance consultant at BackOffice Associates, pointed to the inevitability of Moore's Law, which states that the amount of data in cyberspace is continually doubling. He said that as data keeps growing, it becomes more difficult for companies, their staffs and their technology to keep up.
"More than two out of three information-related projects fail to generate the expected business ROI and outcomes," Cardoso noted. "So what's going on here? There are many factors in any project, but the concept of a 'data first' strategy mitigates many of the issues at play, including failing to properly account for and incorporate the activities required to properly assess, improve and sustain data quality in order to deliver expected business benefits."
It's a simple concept, but it just might be one with a profound impact - put data first. Before you get into huge analytics projects and big sweeping changes, make it a preliminary step to ensure that you're working with high-quality data. From there, the rest will follow.
Cardoso delineated an effective three-step process for making this "data-first" ideal into a reality.
Assess where you stand
First, you need to figure out where you stand. Go through your business processes, understand them inside and out and know precisely where data plays into them. Know which pieces of information are the most relevant. Then, figure out how many quality errors you have in the data that matters most. How big of a problem are we tackling here?
Make key improvements
Next, it's time to address any problems with data quality you might have. This begins with fixing obvious mistakes like misspellings and outdated contact information. It also includes eliminating duplicate entries, fixing formatting mistakes and the like. Then, looking to the next level, you may also want to look into data enrichment strategies, bolstering the information you already have.
Seize long-term control
Finally, you want to keep your data quality strong for the future as well. Don't just fix mistakes once and move on - stay in control of your data for the long haul, ensuring that no errors seep into the process later on. The best data stewards are those who keep their information in tip-top shape for good.
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