3 Myths About Big Data
It’s been called one of the “most confusing technology buzzwords of the decade.” And with so many of the Fortune 500 front and center in the “big data” game, it’s no wonder the concept is often misunderstood by smaller to mid-size businesses.
Here are 3 myths for smaller to mid-size companies to keep in mind as they consider applying “big data” to their marketing strategy:
Myth #1: Big Data is for Big Business Only
The truth is big data is not just for big business or Big Brother. And while Oracle, IBM, Apple and the NSA certainly have more resources for data collection and analysis at their disposal than most organizations, big data is more about data-driven decision-making than any particular source or method. It’s not a thing; it’s an idea. For marketers, that idea is distilling complex customer and market data into meaningful insights to drive day-to-day decisions and long-term strategy. That’s something every business should be doing, large or small.
Myth #2: More Data is Better Data
The term “big data” does marketers no favors. It seems to suggest that more is always better, which is absurd. Before collecting data en masse, brands need to first understand what marketing challenge they’re trying to solve. Is it improving customer frequency? Increasing average order value? Driving new customer acquisition? If you don’t identify your objectives first, you can collect all the data you want and it probably won’t do you much good. But it will be expensive, resource-intensive and, I can almost guarantee, extremely frustrating.
Myth #3: Big Data = Complex Data
Don’t believe the hype. Sure, some data can be incredibly complex, such as the consumer databases we draw upon in building paid media prospect targeting campaigns. But an effective strategy for leveraging big data doesn’t necessarily mean drawing upon the most complex data available. Work with your team and marketing partners to inventory information already available: data from your CRM, marketing automation platform, site and social analytics, etc. Depending on the problem you’re looking to solve, you may find that the answer lies in some of the more familiar data you’re already collecting.