There is plenty of locker room gossip—everybody seems to be doing it but you. And what’s worse, through all the claims, misinformation, and confusion, you get the sense that nobody really seems to know what they’re doing. It’s all-new, all-inticing, and very exciting. But this kind of adolescent curiosity can’t last forever: the truth is, without applied big data analytics, companies are forever alone on prom night.
Quick Sex Ed
The goal is to turn data into information, and information into insight. Consider this: you can have data without information, but you cannot have information without data. The data are the points, and the overarching information is what links them all together. Big data simply refers to the gigantic lines drawn from staggeringly big amounts of data points. Now that it’s possible to collect data in tons of ways—from ordering a coffee, to liking a business, to tracking your health, consumer data has become a really big deal. Such a big deal, actually, that it’s entered the realm of having a whole new vague moniker: “big data.”
Still wondering what people actually do with this information? Well, let me switch out of health class and into shop class for another metaphor: information has become the oil of the 21st century, and analytics is the engine running it.
The Four Types of Data
Descriptive
Descriptive data is the live stream of data that is literally unfolding as your business progresses onward. It is inherently accurate and all-comprehensive; it’s just the numbers. You might want to use descriptive data for effective visualization: real numbers make cool graphs and info charts.
Diagnostic
Diagnostic data lets you drill down to the root-cause effect. This kind of data controls for outside factors—it isolates the “meat” from any confounding information.
Predictive
This kind of data compares the numbers from your business to those of the past: predictive data uses historical patterns and time-tried business strategies to predict specific outcomes. Warning: this is pretty math-heavy. Decisions involved in predictive data are automated and use advanced algorithm technology.
Prescriptive
Prescriptive data are the recommended actions and strategies based on testing strategy outcomes. Once again, this is for the math nerds: it applies advanced analytical techniques to tailor specific recommendations.
Analyze This
Ok, now let’s go to chemistry class. Remember the sweeping conclusions you made about some questionable experiment results? If you paid any attention at all in kiddy chem, you know you can torture your data to make it say just about anything. It can be construed to tell you anything about your business or its customers. Shocker: some common sense is needed! Not all data is created equal, and you shouldn’t trust just any quantitative data you can get. Here, you’ve got to use something you didn’t learn in high school classes: your judgment. We’re not saying you should hand-piece together the data point by point; by all means, use the all-new high-tech algorithms! But… a little trust in human intuition never hurt anyone.
So, there’s a crash course on big data. Apologies if you were actually looking to learn more about the American electronic band of the same name.
