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what does big data that is actionable and timely actually look like?

Clever animal

Big data has been the tech industry’s favorite buzzword for the past few years — and for good reason. Every website you visit, social page you “like” or “follow,” and wearable tech that you update is collecting data about your behaviors. For some, your car is building a database for your insurance company about when, where and even why you speed. And that implanted medical device that is saving your life? Yep, it’s also helpingresearchers better understand human habits when it comes to following the advice of doctors (surprise: we aren’t very good at it).

Every day, we collectively create 2.5 quintillion bytes of data. Over the last two years, technology has enabled us to create 90% of the world’s data. Those two facts alone mean one thing: the amount of data we currently have is really, really big — and it’s growing really, really quickly.

So, when marketers started to use the term big data back in 2012, it was only because “big” was the only honest way to describe it. The data collected about users on Facebook or Twitter or Google was overwhelming — but every CMO wanted to get to the bottom of it, figure out a way to make it affect the bottom line. The answer was in there, amongst all that data, a lot of it seemingly junk. The problem was how to get it out.

That’s when data scientists emerged, and everyone from Apple to The New York Times was hiring one (or a whole team) to dig through the junk, sort it and make profitable predictions based on tangible datasets that would alert executives to emails that wouldn’t get click-thrus or iPhone color combinations people just wouldn’t buy (looking at you iPhone 5c).

And that was always the problem with big data — it was never very smart.To utilize the massive amounts of data you collected, you needed to sort, to filter, to silo the information not just in one way, but in a multitude of different ways that would ultimately shine light on who your core audience or customer really was — and then you could target those people, in ways that would engage them, and ultimately build your word of mouth proposition.

Break all of that down and here’s what you get: big data is worthless unless it is actionable. Worse yet, even if it is actionable, it is still worthless if it isn’t timely.

So, what does big data that is actionable and timely actually look like?Let’s take New York City’s big summer music festival, Governor’s Ball, as an example. In order to get the best headliners, you need to prove that you can pull in a crowd – even if the multi-day, multi-concert location is a mud pit (like it was in 2013).

9271520007_f013b95349_bPhoto: Flickr, Mike Cicchetti

You use big data you’ve already collected to advertise to audiences who you know like the big headliners: Kanye, Kendrick Lamar, Kings of Leon. Cool – you get the same people who came last year to come out this year, the ones who weren’t deterred by mounds of mud, even those found around the port-a-potties.

But how do you pull in a new attendees, the ones who last year decided to ditch the weekend concert series altogether for less muddy brunches in Manhattan’s West Village? You know, the people who don’t really think they missed out on much by choosing food and drink over Kanye’s performance?

What you need to do is segment your big data to let you see additional, less obvious interests – not just which performers your RSVPers like. You need to know other ways to reach them, engage them, get them to spread your word of mouth worth so that when you announce this year’s headliners, the word “mud” isn’t mentioned once.

Smart data does that segmenting. It buckets brand affinities and geo-locations, plus tons of other data points, so you can discover that 70% of people in the tri-state area who RSVPed last year are fans of Comedy Central shows. Better yet, 50% of them are fans of specific comedian-based shows on that media outlet.

Now, you can confidently target on social media outlets to those who like those shows, reach out to Comedy Central itself as a potential partner (with real world numbers backing why this is a great partnership for them), put some ads up on their streaming shows, and even pull in some of their talent to the concert-series itself.

Turns out, maybe your ideal audience didn’t ditch the concert series for a West Village brunch. More likely, they were probably binge watching Comedy Central that weekend instead. Now, you’re pulling them and their friends out to what will hopefully be a better-weathered event – this time with a more diverse audience, better advertisers and a good joke sketch by Katt Williams who will most definitely mention all the mud from last year.

And you’ll laugh, because that mud no longer affects the bottom line.

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Is it always the case that 90% of existing data has been created in the last two years?

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