| Data Science tasks need a rare combination of statistics, hacking, database, business, presentation, and other skills, but finding versatile ("unicorn") data scientists with all the required skills is hard. Which approach is better: [304 votes total] | |
| Individual: Seek and Train versatile Data Scientists that have all (or most) of needed skills (135) |  44.4% |
| Not sure (33) |  10.9% |
| Team: Build a data science team where each member mainly focuses on one skill (136) |  44.7% |
The poll results were evenly split between supporters of team and individual approaches, but interesting and counter-intuitive differences emerged in regional analysis.
The respondents from the US, traditionally considered more individualistic (as epitomized by "life, liberty, and pursuit of happiness" in US constitution) were actually more in favor of a team approach, while both European and Asian respondents were for individual approach: Europe. Canadian respondents were quite different from the US and most strongly in favor of the team approach, while Australia/NZ voters, although few in number, were the most individualistic.
Here is full analysis and comments of KDnuggets Poll: ata Science Skills: Individual vs Team Approach.
Here is the breakdown by regions. Bar height corresponds to the number of respondents.
| Region | % Individual approach | % Not sure | % Team approach |
| US (147) | 41% |
12% |
47% |
| Europe (76) | 51% |
12% |
37% |
| Asia (41) | 51% |
2% |
46% |
| Latin America (12) | 42% |
17% |
42% |
| Africa and Middle East (12) | 33% |
8% |
58% |
| Canada (11) | 18% |
9% |
73% |
| Australia/NZ (5) | 80% |
20% |
0% |
Comments
C. Magaret, via Disqus
Let's go back in time about 20 years, when there was a crazy new trend that took the world by storm called the World Wide Web. Some people said it was going to change the world, others said not so much, some said it was moving much too fast, and some cynics declared it as just a passing fad.
Back in the bronze age of the web, the professional and amateur practitioners who brought the web to life were jack-of-all-trade technologists that went by the term "webmaster". To be a proper webmaster, you needed a (then) arcane mix of skills: system administration, network administration, programming, interface design, and graphic design, to name a few. Webmasters typically worked alone, or among a small cadre of webmasters, to accomplish an organization's web-based goals.
Today, the web is ubiquitous. It plays a huge role in almost everyone's life, has become a large part of essentially every organization on the planet, and there are massive numbers of businesses whose sole purpose is to monetize the web. The web has brought in databases, it spurs the development of new technologies, it pushes the envelope for interactive user-interface design, and certain essential services have been commoditized to the point of triviality. The scope and maturity of today's web is so big that having a jack-of-all-trades webmaster is no longer practical. For people working in web-based enterprises, each of those webmastery skills I mentioned above (and more) have been parcelled out as discrete disciplines to specialists. While an interface designer might know a thing or two about how SOAP works -- and would be a better interface designer for it -- they're not going to be working on the back-end programming with an API. They'll leave that to the developer.
Simply put, the web has outgrown the webmaster. Sure, you might find an old-school webmaster working for a small organization somewhere, but a proper web effort requires more than just a small team of generalists, for both efficiency and quality of effort. They need to have that team of specialists.
The above comes to mind w/this argument about generalist vs. specialist data scientists. If "data science" as a field is expected to evolve and mature to an industrial scale, then "data science" will outgrow the data scientist, and of course you will see specialization, typically in the form of project teams, and I'm seeing this already in large data-driven organizations. People have natural skill mixes, and they gravitate to those responsibilities that cater to their strengths, and for a large organization practicing in data science, having a project team composed of people with distinct responsibilities will result in a more-efficient workflow and a better end product, and it's easier to manage. It's a more natural and disciplined way of doing business.
Sam Steingold
I don't think either option is realistic. It is very hard to combine all 3 skills (math/stat, hacking, business). I think the best approach is to get an excellent math/stat person and then fix his/her deficiencies by adding people to the team as necessary. I.e., a data team should be built around someone with a strong math/stat background.
Kathryn Buttler, Data Science Team or Individual?
I agree that it's very rare to find the unicorns, but I also believe that you need individuals that are much more rounded than being experts in one domain. When recruiting, I'm looking for individuals who can fill the holes in the team, but if I'm running low on computational skills, I would never hire a programmer without the curiosity and creativity of a miner, or without the ability to pickup business knowledge and interpret the outcomes of their analysis, or the ability to distill their insights into powerful presentations. I think it depends on the size of your team, whether you have the luxury of hiring domain specialists or whether you need to make every FTE count and look for more all-rounders, with the occasional domain expert.
Graham Mullier, option 4 - both!
I voted for 'individual' but really wanted a fourth option, for recruiting and training multi-skilled individuals, augmented by a broader team to cover all aspects in more depth.
That's what I'm doing right now with the new Data Sciences function I'm setting up in Syngenta. I'm lucky enough to have a small team who can operate well as individual data scientists with the right skills and domain knowledge. They are part of a larger group who can handle and store datasets, integrate them, build ontologies, build data visualisations etc. It should help scale their expertise and spread the benefits wider.
David Gillman, Team vs Individual
Big Company needs a team. SMB needs one or two individuals.
Sarcastically - a "Team" is a necessary element in any project where blame must be shared or assigned to others.
George R, Team vs Individual
The team approach is healthier for an organization's sustainable efforts. Finding that true Data Analytics rock start is difficult and expensive. Even if you hire that exceptional individual with the entire skill set, she will likely not stay around. The team approach allows faster access to the various components needed to properly attack data problems. If a team member moves out, it is much easier to find somebody who is really good a that missing component.
Gary Howorth, Individual vs Team
Ah but your assuming that the team talks to each other and "asks the right questions" Only in text books i'm afraid . Been in the data business for 30 years, so speaking from experience. the tools have got better, but thats only 10-20% of the skills required. I answered individual but as the previous commenter said not many of them of around especially those that have the right multi disciplinary skills- its a special skill. so in the absence of that yes you need a team. you might argue for a very small team of 2-3 people But having the skills reside in 10 individuals say doesnt do it for me. Its about the way you pull together various disparate components thats important ie data analysis plus context plus experience in the field
Richard D Avila, Data Science Team or Individual?
I wrote my first "analytic" in April 2000. It was a geo-clustering algorithm on a spherical surface. Did the math, did the coding, ran the data,did the validation but had NO clue to what the results meant against 1.5M records. We had an operations analyst who understood what was being shown and good things followed. Analytical reasoning/domain knowledge/domain context can only be gained by truly working in the said domain. I truly agree that the three key competencies of data analytics are: analysis/domain knowledge, math, computation. To be an effective "data scientist" a single individual needs two of three. Of my time in data analytics (April 2000), I have met only one person that had all three areas that could work on a real world problem. The technical dimensions of this discipline is vast - think of all the mathematics, algorithms, and computational technology expertise that is required.
Where one would also fit domain expertise is beyond me. I would view someone who claims they can do all three as either naive or arrogant. Two traits that can doom a data analytics project or systems development. Just because you can imagine a unicorn does not mean that they can exist in this the physical world that we live in. Data analytics done right almost always requires a team.