More companies are creating data science capabilities to enable competitive advantages. Because data science talent is rare and the demand for such talent is high, organizations often work with outsourced partners to fill important skill gaps. Here are a few reasons to consider outsourcing. What can go right and wrong along the way?
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A great number of companies are investing in data science, but the results they're getting are mixed. Building internal capabilities can be time-consuming and expensive, especially since the limited pool of data scientists is in high demand. Outsourcing can speed an organization's path to developing a data science capability, but there are better and worse ways to approach the problem.
"The decision to outsource is always about what the core competency of your business is, and where you need the speed," said Tony Fross, VP and North American practice leader for digital advisory services at Capgemini Consulting. "If you don't have the resources or the ability to focus on it, sometimes outsourcing is a faster way to stand up a capability."
A recent survey by Forbes Insights and Ernst & Young (EY) revealed that most of the 564 executive respondents from large global enterprises still do not have an effective business strategy for competing in a digital, analytics-enabled world.
"Roughly 70% said that data science and advanced analytics are in the early stages of development in their organization," said John Hite, director, analytic architect, and go to market leader for the Global Analytics Center of Excellence at EY. "They said they had critical talent shortfalls, inconsistent skills and expertise across the organization."
Unfortunately, data science projects and initiatives can fail simply because organizational leaders don't think hard enough about what the business is trying to accomplish. They also need to consider what resources, if any, are already in place, and how the project or initiative will affect people, processes, technology, and decision-making.
Have a Goal in Mind
Businesses are building their data science capabilities with the goal of driving positive business outcomes. However, success must be defined more specifically, and the results of the effort must be measurable.
"A lot of times, the client feels like the faster they launch a project, the faster they'll achieve the outcome without defining first what needs to be achieved," said Ali Zaidi, research manager at IDC.
Goal-setting, particularly at a departmental level, business unit level, or for a one-off project may actually work against a company's best interests, especially when the strategic goals of the organization have not been contemplated.
"The first conversation [shouldn't] just focus on the fire that needs to be put out, but the key challenges faced at the top level of the organization," said Eric Druker, a principal in the strategic innovation group at Booz Allen Hamilton. "You also need to understand how analysis is currently done, in stove pipes, or whether data is being shared across the organization. You also need a coherent strategy for linking subject matter experts to data scientists."
Even if the business problem is well-defined, the data science team, whether wholly or partially outsourced, needs to work backward from the goal to understand how the planned change will impact end-users, business processes, and decision-making processes.
For example, an EY client built a customer churn model that was capable of identifying which customers would defect in two weeks. Unfortunately, the marketing and sales teams needed 4- to 6-week lead times to take appropriate action, so the model had to be re-tuned.
"Starting with the end-user and how the [business] process is going to change can sometimes be overlooked," said EY's Hite. "Even if you get that right, do the end-users have the skills required, and are they incented to take the action you want?"
One company built a predictive model capable of identifying the customers who were likely to pay late. However, the customer service representatives tasked with sending payment reminders to those customers were compensated on customer satisfaction levels, not whether customers were paying their bills, Hite said.
Choose Your Partner Wisely
The growing demand for data science and data scientists is creating a market ripe for consultant organizations that now include the big consulting firms, systems integrators, traditional tech vendors, boutique firms, startups, and firms focused on specific vertical industries. One option is extending the relationship with a current service provider, assuming that provider actually has the level of expertise the organization requires.
"[If] you have a trusted partner relationship, you have everything contract-wise you need. Speed is paramount," said Capgemini's Fross. "You also need to consider who will give you the best resources immediately."
Different parts of an organization may be outsourcing different data science projects or initiatives to different parties to achieve different goals. Sometimes the lack of orchestration among the various operating units can have an adverse effect on the enterprise.
"Data science is a cultural change in the way we make decisions. Firms that come in to solve an ad hoc problem miss all these great opportunities to understand the context for decision-making and how decision-makers use data," said Booz Allen Hamilton's Druker. "[If you're working on an ad hoc basis,] it creates an impression that progress is being made. But because it's firefighting, it may inhibit the movement on a data science capability down the road."
Some organizations choose to work with outsourcing partners who specialize in a particular industry or who have consultants with specialized business domain knowledge. Others are looking for expertise that that is best found outside one's own industry.
"People in your own industry will be laggards in the same way you are," said Capgemini's Fross. "If you want to understand customer context, you want to consider someone from a retailer, because they know context better than anyone. If you're a pharma company and you're trying to get your act together around data and MDM (master data management), you probably want to look at something from financial services."
Regardless of which types of firms are on the short list, companies should put more effort into due diligence than they often do.
"As I’m talking to the vendors, I'm asking them about recurring business," said Jennifer Bellisent, principal analyst at Forrester Research, in an interview. "How many of these projects are one-offs? And
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Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include ... View Full Bio
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