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How Dirty Is Your Data?

With subpar data costing U.S. companies 12 percent of annual revenues, hygiene has become critical to the bottom line.

Data has been described as the very lifeblood of a company, but even that doesn’t completely describe it. Used to maximum effect, it enables a business to thrive. One executive called it “a game-changer.” Another said that data, when strategically leveraged, is “no longer a nice-to-have, it’s a must-have for any company that wants to compete.”

All true. Data allows organizations to put their best foot forward. It enhances their reputations and their ability to retain customers and employees. It informs employers’ ability to manage costs, whether by assessing workforce health risks or designing more effective employee benefits plans. Ultimately, it enhances their bottom line.

But the caveat about data is an enormous one: Not all of it is created equal. Dirty data — i.e., data that is outdated, redundant or inaccurate — hampers companies in the extreme. It has been estimated, in fact, that subpar data costs U.S. businesses the staggering total of $3.1 trillion annually. Broken down further, it can cost any given company 12 percent of its annual revenues.

Here are some other data-related insights, courtesy of ZoomInfo:

• 62 percent of businesses depend upon data pertaining to marketing or prospects that is between 20 percent and 40 percent incorrect;

• 10-25 percent of B2B database profiles contain inaccurate information;

• 40 percent of a company’s goals go unfulfilled because of dirty data.

Again, staggering. It is also indicative of just how high a priority data hygiene needs to be. It has been said, in fact, that good data can make a struggling company successful, and a successful company a powerhouse. Bad data, on the other hand, results in wasted time and lost opportunities, ineffective marketing strategies, poor engagement and (again) lost revenue.

Human error is the most common reason for dirty data — whether because of miscommunication or subpar training or even lousy handwriting. It is estimated that the error rate hovers around one percent when it comes to data entry, and from a dollars-and-cents standpoint the 1-10-100 rule comes into play, just as does with all matters of quality: That is, it costs $1 to correct an error upon entry, $10 to do so later and $100 if it is not corrected at all.

Another issue is data decay, which is constant. Of the contacts listed in a B2B or B2C database, 43 percent will change phone numbers every year, 37 percent will change email addresses, 34 percent will change job titles and 30 percent will change jobs. In addition, 34 percent of companies will change names. All of that will, of course, impact sales and marketing forces, and underscore the need for data hygiene.

Experts recommend following these steps to eliminate dirty data:

• Assess.  Falon Fatemi, founder and CEO of Node, the first AI-as-a-service platform, recommended via Forbes that every company needs to audit its internal and external systems. Doing so, she wrote, enables a business to conclude which input fields are needed, and which are not. The focus is narrowed, as a result, and the opportunity for error lessened.

But as mentioned on Geotab, the audit needs to go beyond that, to finding the sources of errors. It stands to reason that if trends can be spotted, it’s far easier to get out in front of the problem.

• Update. Start with standardization of entry procedures and rules. Fatemi, for instance, mentioned the importance of consistency when it comes to numbers, titles, street names, etc. All employees need to be on board with the approach, which will ensure the quality of the data entering the pipeline, and decrease the chances of duplication.

Next, tend to the data already in your system. Validate the entries that are in place. Consolidate duplicates. Update those entries that might have missing values and the like. Delete where necessary.

• Automate. Artificial intelligence and machine learning can do much of the heavy lifting for you, in any of the above scenarios. But as pointed out on, different problems require different algorithms, so there needs to be some savvy on the part of the company bringing this technology into the equation.

• Collaborate. Fatemi notes that sales and marketing teams tend to use different databases — that in effect, they have their own languages. As a result, it is best that those silos be torn down. According to ZoomInfo, alignment between these teams can lead to a revenue bump that almost defies the imagination — 209 percent.

The overall benefits of a data cleanup are obvious, and many. For starters, the lead-conversion rate, estimated to be 143-1 for those companies that do not do their due diligence in this area, shrinks to 68-1. For another, it is far easier to personalize a marketing campaign, and 79 percent of the organizations that do so surpass their revenue objectives. There is also the prospect of increased customer retention, and, again, a rosier bottom line.

In short, it is essential that a company take this step in this day and age. The amount of available data is only going to explode in the next few years; it’s a matter of organization, not to mention survival. As ZoomInfo social media manager Krysta Williams put it, the “spray-and-pray” marketing tactics of the past no longer work. Data needs to be recognized as the tool it is, and used in the best way possible.


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