Which capabilities should a company build for itself, and which should it outsource to a vendor? Since the outsourcing era began about 30 years ago, this question has been pondered by generations of leaders. Some companies use specialized partners for everything, going as far as to say that only people with the potential to become the CEO should be employed. They believe that every specialist should be hired from outside because requirements continuously change, and it’s always easier to find a partner than reskill, rehire and lay off their own people. Other companies err on the side of insourcing, believing that it helps them to do things less expensively and protect their IP.


However, as companies undertake new types of projects that require new skills, it’s worth revisiting the question, as the costs and benefits of each approach may present different trade-offs than in the past. Such is the case with analytics, which requires different skills for each sub-specialty, including data landscape, functional and advanced data science, big data and cloud infrastructure, and analytics storytelling.


As with other capabilities, when considering either building or outsourcing analytics, there are pitfalls at the extremes. Companies that outsource too much may end up with limited institutional knowledge of their systems and of the analytical models that drive their business. However, companies that do everything themselves may spend two or three times as much time and effort as specialists would, and may fall behind their peers in such fast-changing fields as data management and analytics.


So what types of analytics projects should companies do themselves, and which should they outsource? The following requirements can make DIY projects particularly costly, lengthy and risky:

  • New and highly specialized skills: Cloud architecture, big data and advanced data science are all skills that take time and experience to develop. Moreover, people develop them better when they work on multiple such projects. While Google, Amazon and many consulting firms can give their practitioners a variety of assignments and lots of support, an individual medtech company is not likely to. Even someone who has done a similar project once somewhere else may not have sufficient depth of expertise.
  • Heavy domain skills: Some heavy domain skills, such as supply chain, sales deployment, pricing analytics and incentive compensation, are also in short supply in the medtech sector, and it doesn’t make sense to develop such expertise for a need that occurs every few years. Specialized vendors who do such projects for the whole industry or multiple industries can better train and retain such expertise and talent.
  • Assets or accelerators: Many efforts would be cost prohibitive to start from scratch. Big data stacks for a given set of problems have been developed many times in the industry, and to develop another from the ground up would take months or years, even if the skills were available. Many vendors with such assets amortize their cost over multiple projects, so each client can dramatically accelerate its implementation for a fraction of the full cost of the asset.

Of course, for a company with a large department that has the necessary specialized or domain skills, as well as accelerators, it may make sense to do these kinds of projects in-house. But in most cases we’ve seen, doing it themselves took a lot longer and cost a lot more. When it comes down to it, think of it as if you were a homeowner: It’s OK to mow your lawn or to paint a room and, if you are a carpenter, to make your own furniture. But changing a furnace or rewiring the house is best left to specialists.