Big Data vs. Actionable Data: 2 Steps for Capturing the Information You Need
We've all heard the phrase "Big Data" and, as marketers, you may be struggling to incorporate a solution for managing it. But are you trying to solve the right problem? Is big data really what you need, or is it a catchy phrase better used by those who think they need to capture every keystroke for day-to-day business activities? Will big data make marketers successful in 2013, or would you be better off focusing on capturing the right data and making it actionable?
Let's face it, big data scares most marketers. The thought of hiring the talent, managing a huge database and purchasing specialized tools sends CMOs into their offices poring over spreadsheets trying to figure out how to cover the cost. Is it all really necessary?
Predictive analytics is one of the challenges associated with big data. The obstacle with predictive analytics is not the availability of well-crafted, easy to use modeling tools; it's the limited availability of skilled analytics professionals. While large enterprises routinely have in-house analytics teams, mid-tier companies can rarely afford to fund these positions. Moreover, in certain geographic areas, it's almost impossible to find people with the requisite skills.
Most mid-tier marketers need to capture the right information and make it work for their needs. Sophisticated Web analytics tools are available to collect every keystroke, but most of that information is anonymous and/or not particularly useful in helping marketers be more effective. What marketers really need to know are their acquisition and retention rates, what offers consumers are responding to, what subject matter (or content) they are interested in, and if and when they purchase products.
My advice to marketers is this: "Don't try to boil the ocean." Think about the data you need to drive your marketing programs and make yourself relevant and timely. Focus your efforts there and avoid the latest catchy phrase or term used to define something you might not even need.