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Data Mining: The Fear Factor

Data mining is changing the way companies target customers and plan projects, but the tools and resources to do it are a big investment.
Alissa PonchioneOctober 23, 2013

Technology firm Ion Geophysical has been using big data to solve customer problems for 20 years. The company converts seismic wave data into graphs that help its clients know where to drill for oil. Although Ion has been at the forefront of big data for more than two decades, it only recently began using those tools in its business to shape its budgeting and capital-spending plans.

Ultimately, the oil and gas firm is using data mining “to look more organized” and to understand its potential oil and gas customers more wholly, CFO and Senior Vice President Greg Heinlein says. Big data will change the way Ion does business, he adds.

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While Ion is embracing the benefits of new technology , businesses that don’t may be left behind. According to a recent Deloitte study, after reputational risk, 53 percent of C-level executives listed technology enablers and disrupters as the biggest threat to their business models. Of those, data mining and analytics (44 percent) was chosen as one of the top five technology “threats.”

Data mining is only a threat to companies that fear the unknown, Heinlein says. “Organizations could do a better job of leveraging” data to interact with clients, says Henry Ristuccia, Deloitte’s global leader of governance, risk and compliance. However, even companies that are embracing data mining often find it too “complex and expensive,” Catherine Gluckstein, president of analytics startup SumAll, explains.


Indeed, data mining requires a significant upfront investment, Heinlein says. Ion is only in the “third of nine innings” with its data-mining practices, and the company still has a long way to go. Because Ion is still in the early stages of using big data, the finance chief can’t say for sure if data mining has helped the company avoid mistakes. However, Heinlin does have more information on hand when making investment decisions.

For example, when there’s a downturn in the global economy, exploration spending slows down because oil and gas companies cut their capital-expenditure budgets. “If we can see it coming earlier, we can reduce our investment decisions to lessen the impact of the industry downturn,” Heinlein explains.

The more a business uses data to inform decisions and provide context, the better choices it makes, Gluckstein says. Companies such as Facebook, Amazon, Target and Google are experts at using data mining to create thorough profiles of their customers.  For social media giant Facebook, big data is under-hyped, explains Brian Boland, vice president of ads product marketing at Facebook. The company is using its data on billions of users who provide detailed information about their likes and dislikes to provide advanced targeting practices for advertisers.

Facebook can mine through status updates, interests, page likes, and other user activities to create efficiencies for advertisers looking for their target audience. “We know the consumer very well from what they store on Facebook,” Boland says.

Ion, in contrast, uses multiple sources to build its data store. Sources include a customer-relationship-management system, earnings-call transcripts and analyst reports to glean information about its customers and prospects. During events or trade shows with clients, Ion executives already know a client intimately from the company’s research.

However, the results from data mining aren’t seen immediately. It’s a complex, long-term process for CFOs, who are tasked with prioritizing information-technology investment dollars and allocating some of that to data mining. For companies that have been around for a while, like Ion, it requires getting systems to “talk to each other.” Merging new systems with older, legacy systems is also complicated and expensive. “That takes time and money and resources,” Heinlein says. New companies have an advantage because they’re not wedded to legacy systems, he adds.

Training employees on the new software and hiring consultants to bridge platforms add time and expense to the process. For employees used to an older system, it can be difficult for them to engage with new, higher-level platforms that are better at mining for information than outdated ones.

Once the data is culled from systems, data should be translated into intelligent information that steers investments. That’s the “scary part” for most companies, Heinlein says. If companies don’t know what to do with the data, then data mining becomes an exercise in futility. But if a company can figure out the secret, one that Ion is trying to learn, it can see lucrative returns on investments in big projects, he says.

“It’s a big investment,” Heinlein explains. “You can’t spend $20 million [on data mining] and have nothing to show for it in the end.”

Image by Defense Advanced Research Projects Agency, via Wikimedia Commons

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