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Big Data? Don’t strategize, hypothesize.

Rare is the discussion I have with a direct marketer, agency practitioner, or supplier in which Big Data doesn’t come up. Targeted emails? You’ve got to mine the voice data from the call centers for customer intelligence that will raise open rates. Mobile marketing? Track your brand indexing on social media to determine the tenor and timing of your messages. Display ads? Track online buying behavior to customize and personalize messages and send transactions through the roof. Invariably, the talk gets around to corporate imperatives for more technology, how the futures of even the largest, most entrenched enterprises ride on clouds of mighty analytical power, and how marketers must learn to talk tech if they’re to be taken seriously in the boardroom when budget time rolls around.

But must they? According to a McKinsey survey of corporate directors conducted earlier this year, corporate honchos are asleep at the computer switch. More than half of boards have one technology-related discussion a year or less, directors said, and only 28% undertake forward-looking discussions about how technology will shape the futures of their industries. No matter what’s right or wrong with the situation, it’s clear there is a hype versus reality imbalance afoot in the marketplace, and that could be an opportunity for forward-thinking marketers

In two mere paragraphs of a recent article in the Harvard Business Review, McKinsey consultants Dominic Barton and David Court, present what appears to be a common sense prescription for marketers affected by Big Data Stress Disorder: Don’t strategize, hyphothesize

They gave an example of marketers at a company who started their analytics model -building process by listing the factors that affected sales volumes. By having their IT department focus on those areas first, they got their operation off the ground quicker and demanded less time from an overburdened tech department. If their model was imperfect, well, so what? They had a model, and tweaks could be made.

“We have found that such hypothesis-led modeling generates faster outcomes and also roots models in practical data relationships that are more broadly understood by managers,” wrote Barton and Court. “Companies should repeatedly ask, “What’s the least complex model that would improve our performance?”

One person who knows both marketers and analytics thinks the McKinsey brains hit the nail on the head. “The reality is Big Data is big distraction, especially for marketers,” observes SAS Global Customer Intelligence Director Wilson Raj. “When marketers step into analytics, they first need to step back and go on the customer journey. Ask what the customer wants to achieve.”

Raj offers up a simple plan for marketers to instantly get their feet wet in the data game. First, he advises, don’t think about analytical models; think about marketing goals. He suggests starting with three basic ones: customer discovery, segmentation models, and customer outreach. Take discovery first and then ask IT to analyze one aspect of a customer profile, such as creditworthiness or likelihood to buy. Then take the next step and enrich that with attitudinal discovery. “Start breaking these down into discreet marketing goals before you get to models,” Raj says. “Make some marketing assumptions and then run some segmentation models to see if they’re true.”

Bonding with IT and forging a common language takes time and effort, but pays off big in the long run. “Take an existing hypothesis into IT and let them tweak it and test it. That will go a long way toward making the IT people partners with the marketers,” says Raj. “Success can be jointly measured.”

Raj says that his most progressive customers are the ones that start small and start specific. “You don’t want to overwhelm IT with some big, amorphous problem,” he says, “and you certainly don’t want to go in and ask them, ‘How you gonna use Big Data to help me with my customers?’”

Leave that to the CEO and the board of directors–if they ever get around to it.

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