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Al Nugent
Al Nugent
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Should I Hire a Data Scientist?

Before spending big bucks on data scientists, understand the depths of the big data problem. Identifying the issues can help extract maximum value from the coveted skill sets.

Contrary to prevailing sentiments, I counsel patience with regards to hiring a data scientist. Firstly, I mean no disrespect to my data science colleagues, and many will likely agree with me because no one likes to enter an environment where sub-optimal results or failure are probable. Yes, it’s likely you need to add one or even a few data scientists to your team, but not as your first step into the wide, wide world of big data. Give me a few minutes and hopefully you’ll see it my way.

Sure, big data is, well big. And it’s scary. Especially to those of you who have not been around large data sets most of your lives. So you need help understanding it.

To put the problem in perspective let’s look at a medical analogy. If you have some discomfort in your chest, you don’t run to a cardiac surgeon and ask for bypass surgery. First you try to understand the nature of the problem and then go through protocols designed to make the discomfort go away. Even in the case of extreme circumstances, one goes through a diagnostic process, perhaps in an emergency room, to determine the best way to get “better.” I would submit that if your business situation is extreme with respect to big data, there are probably bigger issues with which to deal and you might need a different kind of specialist. A lot is going to depend on the size of your company or organization.

If you are a midsized to large global enterprise with lots of consumer-type customers, the need for big data usage may be more acute than in smaller companies, and at some point you may find that you need data science expertise in house. Smaller companies need to think very hard about on staff data scientists as the costs may outweigh the benefits. In either case hiring decisions should follow strategy, planning, budgeting, etc. Knowing what you need to do ahead of time will help to frame implementation decisions down the road, including and especially hiring.

What if you don’t have the knowledge to craft a strategy?

In all fairness a data science perspective will be important in any well-conceived strategy or plan. Hire some consultants. (I know this seems self-serving, but the right consultants will work themselves out of a job. Others, not so much.) The short term expense will be amortized many times over as you march down the road towards extracting maximum value from your big data investments. Not every consulting firm will have the expertise necessary, although many will profess they do.

Like many other decisions we make: buyers beware. Network with colleagues, attend some industry conferences, find the right short-term help and the rest will fall into place. When the time is right to hire, hold on to your chairs. According to the media, it looks like data scientists may be getting paid better than cardiac surgeons; so don’t expect to get any bargains.

Another important point to consider is the nature of big data and big analysis problems. The real challenges may not lie in the interpretation of the data or with some other esoterica only handled by data scientists. One of the most challenging elements of big data environments is the efficiency and effectiveness of complex workflows. If your organization is not adept at handling large amounts of work that need to be decomposed, parallelized, and recomposed iteratively, then the number and quality of data scientists on staff is quite irrelevant. Like many other problems in the technology environment, there is a need for a thoughtful, balanced approach so you can achieve the most value with the least amount of waste.

There really isn’t any need to panic hire. Big data is here to stay. If we have proven anything it’s that as an industry we are insatiable when it comes to capacity, speed, bandwidth consumption, etc. That means you have time to strategize and plan about how to make the most effective use of the big data that matters to you. Remember the old saw: proper planning prevents poor performance. Never has it been truer than with big data.

Alan Nugent is a leading independent member of the Adaptive Computing Board of Directors. He is a senior executive with nearly 30 years of experience in managing and engineering the strategic direction for technology in global enterprises ranging in size from start-ups to $20 ... View Full Bio
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Becca L
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Becca L,
User Rank: Author
7/31/2014 | 8:25:02 AM
Re: Wait for the facts
Wonderful article, Alan, great analogy and some really excellent points. Employing a data scientist for data scientist sake is no way to tackle the big data and analytics challenge. And besides the hefty price tag of data scientist, there's also an emerging market for data scientist tools, those probably aren't cheap! Budget, plan, strategize...
Becca L
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Becca L,
User Rank: Author
7/31/2014 | 8:24:32 AM
Re: Stay calm
Interesting point, Kathy. change has to start somewhere! This reminds me, I've heard that before hiring a data scientist, it may be helpful to look at the current staff, and seeing if anyone on the team(s) can be trained up. that could be the middle ground that leads the charge.

KBurger
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KBurger,
User Rank: Author
7/30/2014 | 12:26:47 PM
Re: Stay calm
Yes, I'm sure there is a chicken/egg aspect to this. Part of a successful big data/analytics effort is becoming a "big data culture" -- it's not a project it's a journey, blah blah. Everyone's job changes somewhat as data factors into strategy and decision-making. So someone needs to lead the charge. Maybe not a data scientist, but someone who is a business or technology leader.
Greg MacSweeney
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Greg MacSweeney,
User Rank: Author
7/30/2014 | 7:02:09 AM
Stay calm
Well said. It seems as if every organization is scrambling to find a data scientist to head its data 'strategy.' But many companies don't seem to know what they want to do with all of the data that they have. Take a breath, figure out what you are trying to do, and then start to think about a data scientist....not the other way around.
Kelly22
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Kelly22,
User Rank: Author
7/29/2014 | 4:10:08 PM
Re: Wait for the facts
I like your medical analogy, Alan; it does a great job of illustrating this business dilemma. Obviously some organizations need in-house data expertise more than others, and execs should consider their business' specific structure and needs before taking on a data scientist. Short-term help could be the answer for many, especially when data scientists are so expensive!
Jonathan_Camhi
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Jonathan_Camhi,
User Rank: Author
7/29/2014 | 2:02:26 PM
Re: Wait for the facts
I've been hearing so much lately about efforts to make data more available to non-data specialists throughout the organization through better data visualization. I think part of that is being pushed by the points Alan brings up here.
KBurger
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KBurger,
User Rank: Author
7/11/2014 | 12:28:22 PM
Wait for the facts
Good reality check, Alan. Hopefully in a few years there will be enough critical mass of companies that have named data scientists (or comparable titles) that there will be hard evidence of the impact this function has on the business -- it will be possible to see if companies that have this function are actually better at making decisions, etc. That's what big data is all about, after all.
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