how often are you engaged on a retainer basis, so you're not just doing a project, but you're available to answer questions [and] be an extension of [the] strategy organization."
Get Ready for Change
Outsourced data science projects and initiatives may be strategic or tactical, depending on the nature of the work and the mindset of the people hiring the help. A strategic engagement will involve an assessment of the business and what it is trying to achieve; an understanding of where the company is now, and what needs to happen when; and a concept of how the changes will affect the organization, its people, and processes.
Tactical engagements tend to address a specific problem, sometimes in isolation. Either way, the project will likely effect some level of change that should be comprehended and managed.
"The senior leaders need to understand the value that data scientists and analytics can provide, but we also need to have the broader community and managers at all levels understand the value, see the benefit, and [leverage] training for the take-up and use of the capabilities," said Martin Fleming, VP, chief analytics officer, and chief economist at IBM, in an interview.
Only 10% of the executives responding to the Forbes/EY survey recognize data analytics as one of their core competencies. Those companies share three traits:
- They're using data analytics in decision-making most of the time or all of the time.
- They've seen a significant shift in their organization's ability to meet competitive challenges.
- They consider themselves "advanced" or "leading" in their ability to apply data analytics to business issues and opportunities.
"Many companies have reached a reasonable point in terms of being able to produce analytics insights, but they're having trouble driving that into business processes," said EY's Hite.
Companies are also having trouble retaining the data science capabilities they have in house, either because the roles are not adequately supported or because the individuals hired are not challenged with work they consider interesting enough.
"We often find data scientists are not part of a larger team. They're sort of sole practitioners," said IBM's Fleming. "They either don't have the level of support they need, or they don't have the functionality that's necessary, so they struggle with effectiveness and career development."
[eBay recently contributed a big data development to the Apache Software Foundation. Read How EBay's Kylin Tool Makes Sense of Big Data.]
Similarly, players on the outsourced team may leave. Even if they don't, a lot of knowledge that could have been transferred doesn't get transferred because knowledge transfer wasn't part of the engagement.
"At some point, if customers realize their data science needs are increasing, they need to start hiring some of the skill sets internally," said IDC's Zaidi. "There has to be a stream of knowledge transfer, because when the project is done, the customer will have some of that knowledge inside."
Continuing to outsource can get very expensive and so can hiring underutilized specialists in-house. This is another reason why companies need to understand their goals, their own ability to address those goals, and the resources they require to meet those goals -- all of which are dynamic factors.
Stop Looking for a Silver Bullet
The savviest outsourcing resources have difficulty making a positive impact when the client organization is change-resistant and unclear about its goals. Tools can help facilitate good data science, but they're no replacement for the human side of data science, at least not yet.
"You need to have a combination of human capital and software. If you just use human capital, it will take much longer to deliver data science solutions. If you just use tools, there's no tool that is so cognitive it can provide business insight on its own, so you need human capital," said Zaidi.
Some of the large consulting firms are building platforms and other software capabilities that supplement their service offerings. For example, EY recently launched its Synapse analytics-as-a-service platform, which expands the company's managed services capabilities.
"Most IT organizations are struggling with traditional BI warehousing. Now we're throwing big data constructs at them, and the way data science wants to leverage the technology is different from BI/OLAP environments and processes," said EY's Hite. "Finding the right mix of skills is important, but you also need the constructs to make it [work]."
There is no shortage of open source and commercial tools available. The constant stream of innovation is making it difficult to keep up. Outsourcing partners are often brought in to assess the environment and to make technology recommendations based on a client's business objectives.
Tools are making it easier to operationalize data science, but the underlying data science must be sound in the first place.
Outsourcing partners are available in all shapes and sizes, but when it comes to data science, not all of them can solve the same problem equally well. Many outsourcing relationships are less successful than they could be because the client failed to consider its own objectives or the client organization resisted change.
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