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Rethinking Data Quality
Why companies should closely examine their approach to collecting, cleaning, and maintaining their data
For the rest of the December 2015 issue of CRM magazine please click here
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Amid all the hype about Big Data and how companies can tap into it to better connect with their customers, it has been easy to overlook the necessity of ensuring that the data itself is actually valid and up to par.

But the truth is that continually monitoring data to keep it at high quality is vital, because nothing is ever set in stone. "People move, change jobs; they have kids, remarry," Michael Wu, chief scientist at Lithium Technologies, points out. "The world changes." And with it, so does the data. "When you capture [a piece of information] at a given time and don't keep on collecting data to ensure that it is updated, it's actually very hard to make sure that it's accurate," he says.

And while certain customer particulars may be negligible, depending on the company's goals, having inaccurate information on file can potentially hurt an organization's bottom line.

Take, for instance, an insurance company that collects payments through invoices sent via mail. Sending an invoice to the wrong address will usually include multiple steps that require correction—and add up, points out Andy Hayler, founder of the Information Difference. "Reissuing an invoice, redoing a delivery—all of these steps have costs," Hayler says.

Similarly, if a company spends time and money crafting messages that never reach its intended audience, the result is wasted opportunity. Even worse, companies can send customers inconsistent or redundant messages via different channels, which can quickly become a source of annoyance.

Unfortunately, keeping data up to date, and cleaning it, is no small challenge. And Wu notes that regardless of the type of data being handled, cleaning is a tedious process that many organizations have struggled with.

And that process is only getting more difficult as data flows in from ever-increasing channels and at ever greater quantities, whether it's through email, social media pages (such as Twitter or Facebook), Web sites, or telephone interactions.

Add to that the fact that many companies have collected data in various systems—whether ERP or CRM or some other enterprise solution—that have traditionally been separate. To get a unified view of the customer, a single version of the truth, organizations must integrate their existing databases. Plus, they must consider collecting and analyzing new types of customer data, such as images from the likes of Instagram and Pinterest.

All of which is to say that keeping track of it all—making sure data is clean and up to date—can be overwhelming, leaving companies scratching their heads in search of solutions.

Companies that wish to connect with customers should be taking the appropriate steps to clean their data, but like most things that are worthwhile, seeing results will require an incremental, step-by-step approach.

DEFINE THE OBJECTIVES

Starting with a set of objectives is integral to a company's data maintenance process, never mind whether the company is a start-up or an established organization with large volumes of data. But depending on where it is in its development, or what it is trying to accomplish, a company will have a different set of priorities.

For this reason, determining what those priorities are is typically the first step. "Ultimately what it comes down to is starting not with the data, but with the key strategic questions," says Rishi Dave, chief marketing officer at Dun & Bradstreet. A company may turn to the data supplier with wishes to target a specific set of accounts, or to adjust its strategy to target a set of companies in a different vertical or size, but it's important that it has clearly defined goals from the start. "We tend to start first and foremost by looking at the strategy they're employing to drive growth, then work on finding the analytics, data, and processes to help them achieve those goals."

Thomas Schutz, senior vice president and general manager of Experian Data Quality, says that with the vendor's customers, the process is similar. "The first question we want to ask is what is your data quality strategy—what does it mean to you?" Schutz says. "One of the first steps [we take as a vendor] is to help establish that strategy."

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