Navigating Big Data for Big Profits

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Mantha recommends setting parameters for data collection by identifying the right sources early on. "It could be a combination of internal data sources, which might include customer transactions, pipelines, or interactions that are logged in a CRM system," Mantha says. "Determine some metrics that you monitor on an ongoing basis." According to Mantha, having the key performance indicators (KPIs) in place will help companies identify the right data sources—the types of data sources that can help solve their problems.

Companies should also figure out what kinds of technology make sense for them. A company's top concern might be risk, or the health of its potential customers. In that case, a vendor like FirstRain, which delivers commerce analytics, might be a good fit. The vendor's solution can assess the likely outcomes from a signed deal. It analyzes the kinds of news, updates, and company changes that could affect an existing business relationship. "If you're a finance department and trying to find out the risk of a client, you want to see their structured data—for instance, how much money they have in the bank,” Penny Herscher, president and CEO of FirstRain, says. "[But you] also want to see what's being said about them." Running a Big Data analysis of the company across the Web can yield indications of pending bankruptcy, for instance.

Of course, things change. Mantha emphasizes that data collection is an ongoing process that can be adjusted over time. "As the business needs change, and newer data sources are integrated, and newer business groups or lines of businesses are brought in as stakeholders, the dynamics and qualities will change," Mantha says. "So this needs to be treated not as a one-time initiative, but as an ongoing program in which you continually enrich and enhance your data quality."

And enterprises should continually monitor the success of their data usage and implementation to ensure they're getting what they need out of it, Mantha says. There should be a constant feedback stream so that a company knows where it stands in relation to certain key metrics it has outlined. "If the data is not driving sales, go back to see if the insights were correct," Mantha says. "If they were correct, were they complete? And were there other data points that could have been integrated? Or, was the data quality really an issue in driving insights?"


Companies must always be aware of the risks involved in using data, Wu says. The consequences of a prescription matter a great deal. He points to weather forecasts as an example. Though weather predictions are fairly accurate, there's always the chance they'll be wrong. Knowing the chance for rain is 85 percent tomorrow justifies bringing an umbrella to work. But the stakes aren't high. If you bring an umbrella and it doesn't rain, you haven't sustained much of a loss—as you could with other kinds of faulty predictions. At the other end of the spectrum, our ability to predict earthquakes is weak, Wu points out, as we're able to predict them only about three seconds ahead of when they'll occur. Though, technically, that insight is predictive, as it sees into the future and determines the likelihood of an event taking place, it's not actionable. Three seconds isn’t enough warning.

Companies shouldn't use prescriptive analytics when there is significant room for error. It takes good judgment, of course, to determine when the payoffs outweigh the potential risks. Unfortunately, as in the earthquake example, it's not always possible to get a prescriptive read on a situation. There are certain limitations. For one thing, collecting hard data from the future is impossible. Wu states it rather bluntly: "The future is inaccessible." The closer something exists in time, the more likely it is that you can get a good prediction. The further ahead you have to look, the more open it is to errors.


Big Data adoption often becomes a change management issue, says Tanner, who notes that companies often steer clear of it. "Anytime a company wants to implement something that's more data-driven, there's a lot of resistance to it," Tanner says.

Like most initiatives that propose technology as a central asset, Big Data adoption can create conflicts among the various departments of an organization. "It's an interesting paradox," Tanner says. "People struggle to accept data, but people also aren’t willing to give it up." They feel they "own this relationship with the customer, and if [they] give it to you, you're going to screw it up."

To avoid such clashes, Mantha says, companies should make it clear from the outset which department owns the data. "Is it IT? Is it the individual practice owner? Is it the sales operations team?" he asks. Mantha suggests then putting the owner in charge of the data, having this person or department outline the business rules and how they should be applied to customers.

Tanner offers two key tips to company leaders, as they are the ones who must convince employees to get on board with data adoption and usage. The first: Give credit where credit is due. "Don't dehumanize the job," Tanner says. "Don’t attribute the success to the data, but to the person who does something with the data."

The second piece of advice: Remember that change can't just come from the top down. Big Data adoption requires more than executive support; it needs buy-in from everyone. He recalls the example of a holding company that simply did an informal presentation in which it convinced the various departments that shared data, if used across all departments, can lead to mutual growth. "It takes a grassroots movement," Tanner says.

Associate Editor Oren Smilansky can be reached at osmilansky@infotoday.com.

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