Note: an edited version this story appeared on the Hortonworks blog on November 10, 2017.
Having a dedicated big data team is critical to achieving successful outcomes with the data you’re collecting. This post will discuss the elements of building a successful big data team and how this team operates within your organization.
Step One: Achieve Executive Buy-In
Ask any business school professor or seasoned project manager what one has to do first and they will all say the same thing: obtain executive buy-in. The most fundamental part of any business transformation is buy-in and enrollment from the top. A top down approach, and big approvals, are a key step in building a big data story in your organizations. Unless this step is done, don’t pass go, don’t collect $200: work until you have executive approval.
Step Two: Set Up a Center of Excellence
Building a center of excellence, a shared employee facility that develops resources and best practices, is the next step in this process. This CoE could be as small as one person, or it could be as big as you want. The CoE’s members should comprise a subject matter expert from each impacted group across the organization in order to adequately represent the way the transformation is happening. In this way, not only have you gotten buy-in from the executive level to start the overall transformation, but you have participation across the organization so all affected departments can feel heard and acknowledged.
Part of building a proper and maximally effective center of excellence is to encourage the naysayers. Platitudes are fantastic, and open mindedness is a great thing to strive for, but the reality is that everyone has their own agendas and different original points of view. In building the CoE, you want cheerleaders as well as skeptics in order to keep debate alive and valuable. Ironically enough, the naysayers end up leading the CoE often; they become most passionate over time because once their objections have been overcome, they understand why the transformation is so important.
Step Three: Building the Team
Once your center of excellence is in place, the bulk of building your big data team lies in finding individual employees to flesh out the team. This is about 75% art, 25% science. From the science perspective, you’ll want to screen workers through the job description, background requirements, and appropriate thresholds of experience. You will want workers with 7-12 years of experience in IT and some exposure to production Linux; data warehouse skills and experience are a very big plus. Unfortunately, this won’t get you all the way there: the industry at this current point in time doesn’t have skill set in body of work to make it easy to find workers just on those limited merits. The current H-1B situation is actively contributing to the dearth of objectively qualified candidates. It is akin to trying to find a needle in a haystack.
This is where the 75% art part comes in: you build your team from folks with personality and passion who also come from relevant backgrounds. How many candidates you interview are both willing to learn Hadoop but also have the passion to do so? The interview and subsequent in-person conversations is where you will find this passion and sense of opportunity. These soft skills have to be found in a face to face interview, and your interview process should dig into what the candidate’s exact experience translates into. You may also want to consider pushing candidates into a live demo where they perform a real world task, and then discuss how they solved problems you stage. Many times candidates are unsuccessful at completing a demo, but the real key is, can they explain why?
You will also find the best candidates are either toward the beginner of their careers or toward the end, and not necessarily in the middle. For more experienced resources, there is a familiarity with the “wild west” that is Big Data these days as it bears resemblance to IT 20 years ago. Things weren’t integrated, and staff had to do the heavy lifting: build it, script it, troubleshoot it. That innate ability to self-start is an asset. For younger resources, they are quick to adopt tech that can build scripts and automate, which is also a useful skill.
Leaders of these teams should be neutral parties in the organization with no self-driving interest other than to help the company overall change. If a leader does end up being from the department that is funding the project, that interest will often eclipse the greater good. The ideal leader is an employee who is not fully integrated, almost a third party.
Finding this talent is also a challenge. One way is through the Hortonworks University, a program that 12-15 colleges nationwide provide in order to establish Hadoop as a fully accredited part of their curriculum. Hortonworks pays the costs of the program incurred by the university so long as the school makes the courses part of the curriculum. You might also consider recruitment days at local universities, asking professors, who stands out? Who solves things? Provide internship and trial opportunities for those names you receive.
Word of mouth is also a proven way to find candidates. The raw truth is that the Big Data community is a small enough group in world that if you happen to be really good at Hadoop, then people know who you are.
The Last Word
Ultimately, a big data transformation is an enablement opportunity to get your entire organization to go learn. Over time, we can all get stale, but this transformation can be a driver of learning, a place to get hands dirty with something new, and an opportunity to create new subject matter experts. Don’t be afraid to use this rubric to build a successful team.