If you can't hire a data scientist or develop one internally, you can outsource. But what are the downsides?
"Whenever you outsource to a partner something that involves deep intelligence about the business, you're putting part of your brain outside your body," said Chuck Densinger, partner and chief customer intelligence officer at customer intelligence company Aginity in a phone interview. But even with contract and service arrangements, he said, "You're not building a capability. You're not institutionalizing it. And you're beholden to that partner."
IBM's Bhambhri somewhat echoed this opinion. "Fundamentally we believe you cannot just outsource the whole job," she said. But her rationale is less about worries a partnership will go sour than the idea that data scientists are emerging as "organizational change agents." These people, she said, not only extract insights by asking hard, data-intensive questions, they must communicate them back to the business owners, who'll need to take action. "You can't take this whole discipline and outsource it, because you need that bridge between IT and the line of business," she said.
Bhambhri and others also shared the opinion that it is a mistake to treat data science as a one-off project, given that business-relevant data is always changing. "This is an integral part to how you're gaining new insights," she said. For the same reason, she doesn't believe relying on outsiders makes sense, long-term.
Every expert said the real downside of outsourcing is that an outsider might not have the necessary domain knowledge to pose the right questions. Sometimes this can be as basic as different taxonomy, such as the definition of "new customer." For this reason, all the experts emphasized the need to work closely with the outside data scientist or analytics partner, making sure that both sides understand and agree about the assumptions used in the model.
"These models aren’t static, aren’t a black box," said Eric Hills, chief evangelist and senior VP at price optimization solutions company Zillant. The model’s parameters need to be understood by the client, he said, adding, "We bring them up to speed, and train them on the model."
"Recognize your organization’s culture and understand that analytics and data management needs to be an integral part of an organization’s culture," Pankaj Kulshreshtha, senior VP and business leader of smart decision services at business process management company Genpact. "Without it, a lot of your initiatives and investments may not yield the right results," he said in an email interview.
"Many of our clients are looking for a strategic partner," Accenture’s Mulani said. The work will typically result in new business models or intellectual property, "and you don’t want to leave that to be used by a competitor."
Like Accenture, IBM stresses its services include training and knowledge transfer. As Bhambhri put it: "When we say IBM helps companies with their big-data journey, it’s not just tools and technology. A big part is to train the organization so that when we leave, they have the right tools, technology and processes to do it themselves. A big part of our value-proposition is training."
And look for a partner with relevant domain expertise, not simply experience with big data or statistics, said Genpact’s Kulshreshtha. "Ensure that your analytics vendor has the experience providing custom solutions which can address your unique business situation," he said. "Most often, companies develop standard tools and technologies for solving analytics problems."
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