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Analytics Filter Out the Noise

Leo_SadovyIn this special guest feature, Leo Sadovy of SAS highlights how big data analytics can act as a filter and how it can improve our daily lives. Leo Sadovy handles marketing for Mid-Market Analytics, Supply Chain, Manufacturing and Performance Management at SAS, and is the author of a biweekly blog, the “Value Alley”, where he covers a variety of strategic, operational, financial and analytic-focused business management topics. Before joining SAS, he spent seven years as Vice-President of Finance for Business Operations for a North American division of Fujitsu, managing a team focused on commercial operations, customer and alliance partnerships, strategic planning, process management, and continuous improvement. During his 13-year tenure at Fujitsu, Leo developed and implemented the ROI model and processes used in all internal investment decisions. He has an MBA in Finance and a Bachelor’s degree in Marketing.

Information overload is not just a recent phenomenon. From the time Gutenberg fired up his printing press, we’ve been gradually getting snowed under by information. Today, more books are printed in a month than can be read in a lifetime.

And that’s just books – every day we create approximately 3 exabytes of data (that’s 3 million terabytes for those of you keeping score using last year’s counting system). Every second, 3 million emails are sent, 50,000 tweets are tweeted, and 2 hours of cat videos are uploaded. Every. Single. Second. In the time it took to read this far, you just missed 3,000 cat videos. If you’ve only got 100 unread emails you’re still an amateur. So how do we cope?  We do what nature does – we filter.

On your drive into work today, how many mail boxes did you pass? Can you remember even one of them? How many cars did you encounter, and what were their make, model, color and license number? What color is the carpet in your office hallway? Right now, what do you hear? Alright, close your eyes and sit still for fifteen seconds, now what do you hear? How could that garbage truck have been so loud and yet you hadn’t noticed it?

Our brains filter for the significant, the novelties, and what we pay attention to. A police siren, a deer bounding across the road, a mailbox painted like a giraffe, our baby’s cry, that dark green, 2009 Dodge Ram pickup truck with the “As a matter of fact, I do own the road” bumper sticker that cut you off. Discarding forever the insignificant bulk of the rest of the raw sensory input.

The early printers had to deal with information overload themselves by forcing publishers to act as an upstream filter for quality, and to take on the risk of overproduction. Printers learned early on that they couldn’t just print a thousand copies of everything that came their way or they’d end up sitting on more unsellable than sellable inventory. But the lines between information producers and consumers have blurred. Every device owner is a potential content creator. The filters have moved far downstream, even to the point of falling into our laps.

There are a variety of filters. Human filters include the editors that select articles for websites. Machine-learning filters analyze what you click on to suggest things you might be interested in buying or borrowing – books, movies, clothing. Note the word analyze – analytics is the key to filtering information in our big data world. And given the circumstances, with what was exabytes soon to be measured in yottabytes (a terabyte of terabytes), there are no other options. Analytics is going to become an integral part of our personal, social, and commercial lives as a matter of survival, teasing out the significant from the noise, the patterns from the petabytes. Behind the scenes, yes, but that’s how all good filters work, whether in your brain or your news feed.

What are some of the ways analytics can act as a filter?

  • Improving the customer experience. Instead of social media being a threat, some companies use it as a resource to discover emerging problems before they threaten the brand’s reputation. Text analytics serves as an editor, reading millions of blog posts, tweets and service call notes to spot brewing issues.
  • Reducing credit card fraud. By combing through millions of transactions banks can stop fraudulent purchases before they happen by comparing the pending purchase to patterns of the shopper and similar shoppers.
  • Save lives. By pouring through millions of data points, health care providers are finding ways to stop hospital-acquired infections, create customized health care plans and better understand the best way to treat illness and injury.

Without analytics, for all intents and purposes with that much data, all of it will become nothing but noise, data without information, the truly significant indiscernible from the trivial. The complaint, however, is not with the data or with information overload, it’s with the filters, the broken filters. Wherever you find a broken filter there also you will find an application for analytics, pointing you towards the oases of information amidst the desert of data.

 

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