To Make or Buy: Considerations When Kicking Off a Data Analytics Program

How to determine whether to build and in-house team or outsource.

self service big data
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It’s an age-old question – make or buy? In the last decade, that question has morphed into - hire or outsource? From IT to legal to human resources, it’s a question with which companies of all sizes grapple.

This conundrum is now extending to the burgeoning world of big data, as data collection and analytics continue to permeate nearly every facet of life and business imaginable. The technologies to collect, aggregate and assimilate data are plenty. Finding the people who can extract real world meaning from the data and convert it into useful, needle-moving outcomes is the paramount challenge of the day.

It’s a big job; so much so that industry analyst Gartner believes success will be dependent on either building or hiring a team of capable and talented data scientists. Which brings us full circle to the age-old question – build (an in-house team) or hire (an external team)?

With this quandary in mind, let’s review the factors companies should consider when determining the best course for establishing a data analytics program:

State of Current Resources:

Does your organization currently have any semblance of a data analytics team in place or would the corporation need to build one from scratch? Expanding a current team is easier than starting a new one, what with the challenges of finding the right people, dealing with learning curves, providing ongoing mentoring & training, etc. Consider whether your executives have the bandwidth to interview, manage and train. How far you would have to go to get where you want/need to be should be a factor in determining your route.

Level of Urgency:

Companies should evaluate just how quickly they need to have their data analytics program up and running, churning out business-impacting results. If there’s no great rush, building the internal capability and bringing in people who will be fully invested in your success may be the way to go. If there’s a more immediate need for results and actions, enlisting the services of a team ready to spring into action may be the better course.

Consider Where You Are:

Let’s face it - every company, no matter the industry, is impacted by their location when it comes to recruiting and retaining talent. Companies within a bustling metropolis populated by hungry, capable job seekers may find themselves with a wider, more diversified pool of data scientist applicants from which to choose. Additionally, there are some geographies that tend to have a larger supply of professionals with the requisite science & technology background. Such locations will be better suited to building local teams. They will also have a much easier time dealing with attrition and replacing a departed team member down the line.

Also, take into account the size, both today and potentially in the future, of your data analytics program and how many people you might need to meet your goals. If you’re program is modest, you might find an individual or two willing to move to an uncommon geography. But geography can become an obstacle if you need a more sizeable resource or expect to scale significantly in the future.

Organizational Culture:

The definition of “company” has changed dramatically. Factors like technology and globally available specialist suppliers means that many companies today are no longer strictly defined by the individuals who physically report to a specific location each day. These types of companies might have more receptivity to working with outsiders.

Conversely, internal teams at organizations accustomed to a more self-contained setting might feel resistant to the idea of working with an external partner. They may not have the requisite instincts when it comes to using technology to collaborate over long distances and possibly across time zones. Without buy-in from day-to-day managers, the chances for success are limited. Consider whether your organization not only has the willingness to work with outsiders, but the culture and skills to do so as well.

So there you have it. In the world of data analytics, there are a few different ways to take a bite at the apple and no one approach works for everyone. Taking an honest look at the culture of your organization and tying that to your resource needs for today and the foreseeable future, are the keys to finding the elusive answer to the age-old question. Or at least, the right answers for your organization.

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