Big Data Strategy: Focus on the Signal

Posted by Laura Teller on Wed, Mar 27, 2013

Signal red box

Why we should stop talking about Big Data and start talking about Big Signal.

“Big Data” seems to be the phrase on everyone’s lips these days — the new Big Bandwagon for enterprise, government, investors, and entrepreneurs. But I would respectfully submit that the term may be missing the point… and those who continue to define the phenomenon in that way may miss the first wave of the real opportunity.

Big Data refers to the never-before-available amount of data that comes to us in computable form — digitized audit trails of the human experience, of machines’ activities, of natural phenomena, and much more. Every transaction, status update, online search, or cell-phone ping leaves data behind. Never before has it been more ubiquitous, cheap, or computable.

Pulling the Signals from the Noise

Just because there’s suddenly a glut of data doesn’t make it particularly interesting or valuable, at least in its raw form. It’s like raw crude oil — full of impurities, sludge, and gunk. Putting it unrefined into the machines of commerce and government will just gum up the works.

And yet, hidden in this unrefined, untamed flow of the world’s data is more purely predictive information than has ever been available before. This is what we call “Signals” — the valuable patterns, connections, and correlations that, if properly extracted, allow us to predict behavior and outcomes far more accurately than we could in the past. Signals are the data elements, patterns, and calculations that have, through scientific experimentation, been proven valuable in predicting a particular outcome. And it’s these Signals — not the Big Data where they’re hidden — that hold the real value.

The majority of Fortune 100 companies have already taken the critical first step of acquiring Big Data technology, which captures information. But that’s just the beginning. Now these companies have to develop the capabilities to continually identify and weed out the Signals from the noise and turn those Signals into something of value. They need to build a Signal refinery — turning the crude “oil” of Big Data into “gasoline” that can power better decision making throughout the entire organization.

Moving from Predictive Signals to Prescriptive Actions

Creating the refinery to identify, extract, and organize these Signals is only the beginning. For organizations to fully reap the rewards of the Big Data phenomenon, they need to transform these predictive insights into frontline, directed actions. Attrition risk scores need to be integrated with CRM systems; prioritized actions need to be fed to customer service reps; and strategic recommendations must be delivered in clear and intuitive formats, not opaque algorithms.

It’s a big task, but with a big payout. This combination of Signal refinement and prescriptive, directed actions will drive enormous new value for enterprises.

To illustrate this, let me provide one example. For one company, we created this Signal refinery, which we call a Signal Hub. We integrated more than 20 different data streams — internal and external — to develop a rich profile of 20 million customers’ journeys and touchpoints over time. This includes such metrics as what they’ve bought, the pattern of their purchases, what promotions they’ve responded to, whether they are price sensitive, and much more.

In all, we’ve identified over 1,000 predictive Signals for each customer, which are extracted, arrayed, stored, and continually updated as new data comes in. When marketers seek to do something, such as match customers against offers, assess who is fading and how best to arrest each customer’s attrition, or determine individualized pricing and promotions, our technology automatically selects and “fires” the right Signals into predictive models that generate individualized recommended actions.

This approach achieves true one-to-one marketing and has resulted in extraordinary improvements in response rates. For one promotion, we improved clickthrough rates by 20%, Web traffic by 25%, and the ability to match product offers to customers by 1.8X. And this is just the beginning of the performance gains. When marketers are given ready access to predictive intelligence about each of their customers, it changes everything.

Big Data is here, and things will never be the same. But let’s not focus just on the phenomenon; let’s explore how we can best exploit the opportunities it creates. And that means shifting our focus from Big Data to Big Signal and putting this predictive knowledge to work for us in ways that generate real results. 

If you'd like to learn more about Signals and Big Data, click below to download our whitepaper.



Laura Teller is the Chief Strategy Officer at Opera Solutions. She holds a Bachelor of Arts degree from Yale and an MBA from Harvard University, where she was a Baker Scholar.

Topics: Big Data, Data Science, Machine Learning, Signals