Imagine you are planning a dinner for friends. You pull together your guest list and agonize over the menu. You make countless stops at various stores and markets to get the best, freshest ingredients. You want your friends to remember this meal—and your culinary skills—so you follow the recipe meticulously.
Now, imagine you left your ingredients out for days, or even weeks, or didn't store them properly, or used the wrong tools to prepare them. What could have been a fantastic meal would be ruined and those friends might end up running for the hills.
Fresh, high-quality "ingredients" are just as critical in marketing. You may have the perfect campaign in mind and you might be doing all the right things—but the results aren't living up to expectations.
No one sets out to cook up a disaster. But, just as ingredients that have gone bad will ruin your dinner, poor data quality is often at the root of poor campaign performance.
Not having access to the right ingredients—or data—brings its own challenges. Having a fantastic recipe doesn't mean much if you have to go rooting through your pantry or freezer to find that key ingredient you were sure you had someplace. Data quality also means being able to find, retrieve and put information to work when and where it's needed.
The Bad-Data Culprits
Of course, you can take steps to recognize and address the data quality issue, so let's start with a look at the three biggest culprits behind poor data quality.
1. Past the Sell-By Date
Even the best data can go bad over time. Data erosion is inevitable, but that doesn't mean it should be ignored. According to Forrester's, Vendor Landscape: B2B Marketing Data Providers, Q3 2017, only 12% of marketers have confidence in the accuracy of their data. But that figure is just one data point associated with contact management.
The frustrating truth for B2B organizations is simple: Though firmographic data may change less frequently over time, demographic data management will always be a challenge. Job turnover alone—whether a key employees receives a promotion or moves to a different company—causes immediate usability issues within marketing databases.
Different types of data have different shelf lives, and it behooves marketers to develop a sense or system for keeping track of those dates. Names, for example, change far less frequently than employers and email addresses. On the other hand, personal phone numbers, thanks to cell phones and phone-number portability, often remain stable over time.
2. The Right Tool for the Job
A robust marketing stack, like a well-appointed kitchen, includes tools designed to meet many specific needs. In today's omnichannel world, the ways contact and account data is captured lack checks and balances for consistency and accuracy.
Websites, email, social, and paid campaigns are common mechanisms for data acquisition. With all of those data input channels, there's plenty of opportunity for human error—but that's not the biggest issue.
Even if a qualified prospect inputs his/her information correctly, that information flows through your marketing stack and related platforms. Unless all of those systems are perfectly integrated (and we all know the challenges associated with integrations), the data makes its way through each of them, being changed ever so slightly along the way.
The point is, without an active data management process in place, omnichannel campaigns will yield unstructured data points.
3. Too Many Cooks
Who hasn't had the experience of too many cooks spoiling the soup? The same thing can happen in marketing. Whether it's your customer service department, business development representatives, account managers, or fellow marketers, employees from across an organization may touch and rely on customer and prospect data. With every touch, business units can (unknowingly) hurt data quality.
There's no way to stop everyone from using data—in fact, it is there to be used. But the more people use it and append additional details to it or otherwise update it, the greater the chance that its quality will be diminished.
Some safeguards can be put in place. One is to restrict access to only those who need the information to do their jobs. Another is to put restrictions on what types of access people have so not everyone can tinker with the data whenever they want. Finally, it may make sense to inject some auditing capabilities into the workflow to identify what changes are being made by whom.
Who's the Top Chef?
Managing and maintaining data quality should be a priority for every part of an organization, but it is particularly important for three groups: Marketing, Sales, and Operations.
- Marketing leaders have a number of priorities and goals that depend on data quality. One priority—lead gen—provides a good case in point. Understanding total opportunity contribution, marketing-qualified leads, sales-accepted leads, and overall revenue targets will go a long way. Why? Because in today's performance-driven world, marketing leaders understand the better their data, the faster leads can flow through the sales funnel.
- For sales executives—and the reps they manage—poor data quality detracts from their most important resource: time. According to CSO Insights' 2016 Sales Enablement and Optimization Study, only about one-third of a sales rep's time is spent selling. Instead, reps are spending time "smell-testing suspect ingredients" and either manually updating records or, even worse, calling a company switchboard rather than dialing a prospect directly.
- An organization's operations team is tasked with keeping things running smoothly, and that's hard in an increasingly complex ecosystem. Forrester reports, for example, that 56% of organizations have implemented sales force automation solutions and 53% have enterprise marketing solutions in place. It's up to operations professionals to ensure these technologies are implemented properly and to manage the required integrations among systems. Data quality is key.
GIGO
GIGO is an old chestnut in the world of computing. It stands for "garbage in, garbage out." It is a catch-all term for the idea that data quality is important. But it misses an important nuance both in the world of cooking and marketing: No serious chef would ever start cooking with ingredients that had gone bad and no marketer would run a campaign with poor quality data. But, over time, for a range of reasons, what was once perfect can spoil.
To maintain data quality, it's important to understand how and by whom information is sourced and used. Likewise, it's critical to recognize when and where early signs of degradation are most likely to appear before the impact of data decay can be felt.
Keeping the cooking metaphor in mind can help keep everyone attuned to the importance of maintaining high data quality before something stinky is inadvertently served up.
