Data jobs are “the sexiest jobs this century”. Data jobs are also among the hardest to positions to fill. The qualifications are so high that candidates have the leverage to “name their own price.”
Recently I was contacted by several recruiters seeking to fill a few mid-career to senior data related positions. The positions typically fell under the broad theme of innovation, change management, or capacity building to make an organization more data-enabled. The titles range from Director to VP or C-suite levels; some in business units, some in functional departments.
I myself am extremely thankful for where I am in my career. I am not looking. :-) The job descriptions and phone calls I received got me thinking though and raised some questions in my mind that I’d like to share. In particular, what does a “data person” look like? And, what should a data-enabled business look for? Does the common stereotype for data scientists hold up in a business environment?
First, we need to differentiate power users from tool builders. The advent of the term of “big data” and “data analytics” came with promises of fundamental changes in how business is done. While all of those claims are reasonable and path defining (although aspirational), the broad and unqualified use of those terms have muddled the boundaries between those who build innovative tools and those who use those tools.
There is an overlap between those two crowds, specifically when they come together to rethink and reinvent new business workflows for solving long-standing business problems using new tools. That’s why the two crowds need to be knowledgeable about each other’s terminology. But there is never a requirement that they be able to do each other’s jobs. As long as the power users know where to find the vendors and how to evaluate the vendors, the vendors are merely a replaceable piece for a data-enabled business today.
In an analogy, how could we require a surgeon to know how to put in a stent and know how to build the stent too? It’s unrealistic and unnecessary.
The perceived demand for a “modern data scientist” like the one pictured above is solely because many businesses are not certain what they need and think they should experiment themselves. Most organizations are not in that category. Many of your needs are common among your peer organizations. With so many startups popping up each month, it is very likely that some innovative problem solvers have identified your needs and developed a product to fill the gap.
I do research in data analytics and teach the subject in a computer science department; I built a feedback analytics company based on an invention in behavior analytics; and I have had a unique opportunity to observe how power users of Survature are building their own data-enabled business workflows within their organizations. From these perspectives, I want to reveal three costly misconceptions that organizations may have when hiring data talent.
Knowing how to run databases and write code is not the driver.
The real differentiator is whether you think like a hunter or a survivor. Let’s look at an example of when a business process has to be changed because of (anticipated) business results. Imagine the team thinks of themselves as a survivor. They do everything that their peers would do—just for the sake of doing those. They buy reports about the company’s aspiration peers, they corral industry benchmarks, they run 18 minute long surveys. The survivors then look for everything that is “wrong” and present detailed reports about all of the “perceived gaps” during an executive meeting. In the end, no one carrying out the data analytics is in danger of having made any mistakes, nor making any meaningful contributions to the business decisions. No risk, no reward, and hence no seat at the table. This is typical of descriptive analytics today. Would “knowing how to program” make any difference? No. It’s not a skill issue. It’s a mindset issue.
Let’s flip the analytical mindset to that of a hunter. There is no food on the table unless we score. Such clarity of purpose starts with every decision and every tweak or change on the table. They already know what can be done, but what are the reasons that back up those decisions, the hunters focus on capturing the information to back up the choices, know the consequence, know that they have to tread carefully. They take actions, they measure, they pivot. There is no real secret to the sauce here, but it is the first step towards taking effective actions.
The interface between you and the data analytics is a toolset that features fully automated data visualization and visual analytics. The challenge of adopting a new platform is almost non-existent as long as you have a data partner to help guide and facilitate your team on their data safari.
Instead of thinking “I might consider liberal arts majors”, you should want to hire liberal arts majors to join a data-enabled business team.
While liberal arts majors are known to be better at soft skills, based on what I have see, there is actually nothing soft about those “soft skills”. Since I was a Computer Science major and I teach many of them now, I can fill you in on a secret about computer science majors. Their world revolves around an axiomatic rule - the holy trinity of bigger, faster and cheaper. If my code runs 31% faster, then my world is linearly that much better. If my system is 15% cheaper, then my brain will emit that much more endorphins. Is that world beautifully simple or what?
The liberal arts majors don’t get that luxury of simplicity. They “argue”, argue with themselves, about the logic, the philosophy, the cause, etc. In a business setting, these traits are immensely useful. In most cases business strategy can be joked about as “a post-mortem summary of luck”. The key to developing a successful strategy is tied to finding the most crucial goals and knowing the order in which to approach those goals. This is a do-or-die issue for today’s business world. The caveat of course is to also avoid those who make decisions based primarily on feelings and emotions. Also avoid those who just want to win, win at all costs and right now. The awareness and desire to continuously become better, individually and collectively as an organization, is a must. In the end, it’s striking a well rounded mental approach that features objectivity, clarity, and having the heart of a champion.
Don’t buy into the data scientist stereotype.
You should encourage everyone on your team to be data-enabled. In a way, the diversity issues are in drastically better shape than one might assume.
Many advocates have spoken about diversity issues in the broad universe around “data”. Many have spoken in respect of industry, and more have spoken from the academe and public-service side. I have first hand knowledge from all sides. I know for a fact that diversity issues, regardless of reality or perception, do cause significant hesitations when it comes to the adoption new tools.
Looking at the Survature user base, our users are primarily professionals involved in corporate decision-cycles and need to use feedback analytics to understand which opinions matter and how those opinions matter. The users include directors / VP level professionals of Fortune 500, business unit heads in charge of manufacturing operations across multiple continents, hot internet retailers who have just bought their 2nd competitor in the market, great non-profits that are causing positive changes, the biggest A&D firms, one of the biggest youth sports events in the US with 40,000 attendees, and the biggest music festival on the planet—Bonnaroo, etc.
Survature’s user base consists of experienced business professionals in the 35-45 age group (only about 15% are millennials). They all tend to strike a great balance between life and work, they are making a difference and causing positive change. None majored in IT or related fields. They appreciate ease of data collection, transparent data management, and visual analytics that can make deep information more accessible. The business value they need is primarily in regard to their company’s team, product and customer and they don’t fit the stereotype of a data scientists.
My three suggestions are actually very related. When it comes to finding and building a team of power users, who will use those innovative data tools, you first need to make sure the problems they are solving are worth solving. But after that, it’s making sure the team has the right mindset, empowered with room for critical thinking, and involving everyone.
You do not need data scientists working for you as long as you are not making your own scalpel while doing surgery. Let your team focus on being great surgeons.