Decoding the Data Analytics Process

If you’re anything at all like me (or not at all like me) you probably have a vague understanding of DATA (if you could picture someone screaming DATA into your ear every time you read this word, preferably with spittle flying, that would be great) but you don’t really really know what it is.

No problem, we can get through this. I promise.

So, DATA (did you do it? I’m counting on you to keep it up) is pretty nifty. Basically, it’s a lot of little bits of information that add up to allow you to gain an insight that is really interesting or really obvious or maybe completely unrelated and irrelevant.

Was that not clear enough for you? Okay, let me try again. Let’s say, for example, that we have the ability to track exactly how 4,000 university students use the internet every day of their lives.

That would allow us to track quite a bit of information such as: what they do, where they do it, how long they do it for, and where they spend money: you get the gist.

Now, based on that information we can learn a lot about their lives. For example, we could discover that before 8 am only 5% of the population could be reasonably described as “alive”, delivery pizza skyrockets at 12 am on Fridays, and that people usually pick up their pizzas wearing aqua marine t-shirts.

Some of this is good stuff. You can count on the fact that some enterprising student will walk around at midnight selling pizza at 20$ a slice and gouging students for all their worth.

Other information is pretty obvious but also useful. Students aren’t awake before 8 am? Well then I guess it wouldn’t be worth your time to solicit money at that time.

On the other hand, if you wanted to make a LOT of people VERY angry, boy have I got some good information for you (it has to do with students sleeping).

Now, that last bit of info about t-shirts is not very good to anyone. I mean, aqua-marine is a pretty cool color but we’re more focused on the pizza here.

The point of all of this is to show that DATA is out there and it can show you some really amazing things.

But, there’s a lot of it and in order to get to the good stuff you have to slice and dice and analyze. Doing so will allow you to focus on the good stuff and forget about the things that might not be SO applicable- like pretty clothes.

Focus on breaking up the data into easy to understand pieces and then work from there.

Catch you next time.

Go ahead and follow us on Twitter, Facebook, and Medium if you want to know more and don’t hesitate to reach out to me at zach@humanlytics.co if you have any questions.

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