Every day last year, we created an average of 44.1 exabytes of data. That’s an unfathomably high number roughly equivalent to about 369 million Macbook Pros. With the volume of data out there, marketers have gotten savvier about how to process and utilize it to reach consumers. The problem is, most of that data is bad data.
How bad? Two Cork University Business School researchers, as well as the president of Data Quality Solutions, figured that out using the “Friday Afternoon Measurement method.” Participants, executives from a variety of sectors, assembled 10 to 15 critical data attributes for their departments’ last 100 units of work. Managers then went through each record, marking the obvious mistakes. Each error-free record gets a point; the total between 0 and 100 is that company’s Data Quality Score.
A score of 97 is the threshold for what’s considered acceptable. Out of 75 executives, only 3% had data that made the cut.
“I’m not at all surprised,” says Jay Marwaha, CEO of predictive behavioral analytics company SYNTASA. “Data is the battery that drives your car and still enterprises have huge issues with data quality.”
Dirty data costs the economy trillions of dollars a year, which we’ll get back to. The marketing and sales teams are particularly affected, given how frequently people tend to change companies.
For those B2B marketers, bad data results in bad leads. Examples include invalid email addresses, missing fields, duplicate data and improper formatting that requires manual correction. According to ANNUITAS research, 77.4% of B2B marketers cited lead quality as their biggest challenge.
An Integrate infographic illustrates how much time and money this can waste:
And that’s without factoring in the remuneration of the sales and marketing teams who wasted those 59 hours. This also snowballs into the customer experience. Poor data quality slows up the sales process, meaning that leads aren’t contacted quickly enough, or at all.
DemandGen’s 2017 B2B Buyer’s Survey Report found that 75% of buyers consider vendors’ content in decision-making. Almost as many (72%) factor vendors’ timely responses into their choices. Ultimately, bad data reduces ROI, which often results in a decreased marketing budget, exacerbating the cycle.
“Marketers understand the problem, but they don’t always truly respect the cost bad data imposes on the organization,” says Justin Gray, CEO of LeadMD. “Predictive marketing and artificial intelligence sound sexy. But if the data is so bad, you’re constantly adding new flat records that make it hard to understand who a prospective buyer is. That’s where the costs start to amplify.”
One of the dirtiest kinds of data is duplicate data, which Gray argues renders 50% of a marketing database useless.
Duplicate data takes up space in the database, causing the technology’s price to surge. Additionally, it creates confusion around lead generation, with multiple salespeople contacting (and annoying) the same prospective customer.
“Marketers approach to data is often buying lists of data, entering it into the system and hoping the data will work itself out,” says Gray. “Sales and marketing enter data into platforms and the system deems them equals. As marketers move from one company to the next, there are multiple companies associated with them. It creates a volume problem and people are also sending messages to inactive email addresses.”
Integrate analyzed leads from its platform over the course of a year. From September 2016 through August 2017, 55% of the company’s 3.64 million leads were good ones. Duplicate data, which clogs databases, was by far the biggest problem, accounting for one-third of the total leads.
Like anything else worthwhile, cleaning up your data is neither easy nor cheap. But it can be done. Identify your problems and scrub your data, consistently. Look at the user experience side and see how you can make it more difficult for users to commit errors, such as typos in fields.
Marwaha also prefers to clean his own house, rather than trust a maid, so to speak.
“We’ve improved data quality by using data management tools, as opposed to getting clean data from vendors,” he says. “Vendor data is useful, but very little of it. Platforms like HubSpot and Salesforce allow us to ‘de-dupe’ data. We also validate bounces against the data management platform, for example.”
Companies are increasingly hiring Chief Data Officers, which is a step in the right direction. But that’s not a perfect resolution, given the lack of industry-wide regulation Gray points out.
“You have to have an overall data governance strategy with someone positioned as a steward of that data, who can move the integration from a governance perspective,” he says. “But it hasn’t become standardized enough. It comes back to the carrot and the stick. Everyone realizes there’s a problem. At this point, maybe the only thing that can solve it is the stick: someone getting in trouble for not having data governance in place.”
Modern marketers have more data available than ever and use more tools than ever to integrate it into their businesses. However, most of that data is dirty, which is particularly problematic for B2B marketers, whose lead quality is negatively impacted.
Bad leads snowball into wasted time and money, and a compromised reputation. Data quality will likely never be 100% accurate—but that doesn’t mean it’s not worth trying.
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