Three Big Data Myths Debunked

Customer Experience
Customer Experience
With any new marketing buzzword, there is a lot of speculation and advice on what constitutes a good data strategy. Here are three dangerous myths to debunk about Big Data.

By this time, you have probably heard of a number of companies leveraging Big Data to drive innovation or increase efficiency. It's very compelling: After all, who wants "small data" now that bigger versions are available? Sure, the insights that can be pulled from big data are impressive, but before you dive in, you should be aware that big data also means big investments, and just like any investment, you should be conscious of the risks and potential return.

With any new marketing buzzword, there is a lot of speculation and advice on what constitutes a good data strategy. After working with some of the largest datasets online at Yahoo!, BlueKai, and now, WhitePages, and heard a number of myths about Big Data, I've picked three that are particularly dangerous:

Myth #1: Gather Data Now Even if You Don't See a Need for it Right Away

FALSE. Applying a Big Data strategy isn't about gathering as much data as possible. Rather, it's about applying data to solve existing issues. The value of the data comes from the business application.

Big Data didn't originate from companies who suddenly figured out how to apply huge troves of data, but rather it originated within companies looked to data to solve existing issues. Big Data arose from the necessity of analyzing trillions of data elements to solve problems like "Out of the thousands of possible ads I can serve, which ad should I serve to each individual?" or "How do I use all of my historical check-out information to predict whether someone is who they say they are when they are buying an expensive product?"

The first step in determining the potential return on a Big Data investment is to identify the specific business pain points currently constrained by data. Survey your company's departments and pinpoint which areas could benefit from additional information, but don't take a nosedive down a rabbit hole of trying to infuse data into every aspect of your business. Data without a purpose is just noise, and none of us need more noise in our business lives.

Myth #2: The More Data the Better

FALSE: The biggest issue when dealing with Big Data is not collecting or acquiring the data; it's managing the signal to noise ratio.

Don't let your eyes get bigger than your team's capacity for interest. Be selective in gathering and buying data that actually address key business issues.

Asking the right questions before jumping in can be the difference between being in possession of data you need, and being overwhelmed by extraneous information you don't. A study from Lyris, conducted by the Economist Intelligence Unit, found that when it comes to Big Aata, only 24 percent of marketers actually use it for actionable marketing insight, and 45 percent of those surveyed admitted that they lacked the capacity for analyzing big data sets.

Managing a high signal to noise ratio is a constant battle even without Big Data-don't make it exponentially worse.

Myth #3: After I Cut the Check for Big Data I'm Good to Go

FALSE: Big Data is anything but stagnant. Gathering and applying Big Data is an ongoing process, not a one-time investment.

As you learn how to apply Big Data for your business issues, you'll naturally find more ways to apply data, as well as more find more potential uses for additional data. This is normal. Once your organization begins seeing some of the returns from the initial investment, the pressure to "gather now and collect more" can increase. Don't be swayed by your own success, but rather continue to make sure that your ongoing Big Data strategy continues to be tied to business issues.

The truth is, there is no need to rush into Big Data without a clear idea of what return you expect to obtain. Big data isn't about stockpiling large volumes of data, it's about leveraging data on a grand scale to understand and fix business issues.