Garbage In, Garbage Out: How to Clean Up Your Big Data Marketing Act

A young programmer way back in 1963 is believed to have been the first one to put the computer catchphrase, "Garbage in, garbage out," into print. This term, and its acronym GIGO, had buzzed around the old data center for some time before that. But GIGO is just as applicable to big data marketing as it is to computer programming. Your analytics, and any marketing efforts built on those results are no better than the quality of your data to begin with.

How Bad Data Corrupts Your Big Data Marketing

Bad data includes any information that is:

• Missing
• Duplicated
• Outdated
• Corrupted
• Erroneous

Let's say you have a big data marketing database of 50,000 leads and customers. Pretend that just 10 percent of those names and contact information are entered incorrectly, entered more than once, or are old and outdated. That means that every time you send out an email broadcast to the "leads and customers" on that database, that 5,000 of them are wrong!

Not only is this inefficient and ineffective, it gives your brand a bad rep. Bad data leads to bad analytics. Bad analytics leads to bad results. Bad results mean poor and misguided decisions. For example, if you're banking your marketing messages on where a lead or customer is located, what if they moved? If your messages hinge on what title they hold within a company, what if they've been promoted, or moved to a new company?

Bad data skews your entire big data marketing perspective. It looks bad to send an email to Ms. Scott, VP of Marketing when she's actually been promoted to Ms. Scott, CMO. It looks even worse if Ms. Scott is actually a Mr. The more personalized your marketing messages are, the more GIGO becomes a problem.

Data Cleansing is Like Housecleaning: Best Done in Small Increments

Big data marketing If you clean a little each day, it doesn't get so bad. But if you wait for months or years to cleanse a database, it'll be a rough go.

Have you ever waited way too long to clean your house? As long as you're tidying up a little here and wiping up a little there, things don't get too bad. But if you let it get really dirty, it can take hours -- or days -- to clean it right. Data cleansing is this way, as well. Most big data marketing efforts can be righted again with a thorough housecleaning, but it takes regular maintenance or the entire database will be back to a state of disaster in no time at all.

The problem with big data marketing is that as soon as you begin collecting information, it starts going bad. People move. Companies go out of business or merge with other businesses. Contacts within a business get promoted, get fired, or quit work to start families or launch their own businesses. Data cleansing and quality control has to be an ongoing thing. Instead of approaching it like spring cleaning, tackle it like a hospital that cleans and disinfects continually. That's the only way to keep your big data marketing database from becoming infected with bad data.

Don't Worry, There is Hope

ReachForce helps marketers increase revenue contribution by solving some of their toughest data management problems. We understand the challenges of results-driven marketers and provide solutions to make initiatives like marketing automation, personalization, and predictive marketing better. Whether you have an acute pain to solve today or prefer to grow your capabilities over time, ReachForce can unify, clean and enrich prospect and customer lifecycle data in your business, and do it at your own pace.

To learn more about how ReachForce can help you optimize demand generation and your impact on revenue, get a free data assessment and get a demo today.

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