While at CES representing my company 12 Labs, I noticed two very clear patterns. The first: The virtual/augmented reality economy is going to be as big as the app economy, if not larger. Even single-man VR startups had 10-minute waits to try out their demos. The second: There were surprisingly few artificial intelligence or data science companies giving demonstrations. This observation didn’t quite reconcile with what the brightest minds in tech — think Elon Musk, Bill Gates — have predicted about AI finally being here. So where are the companies who are going to bring AI onto the field? (Microsoft and Facebook can’t do it alone.)

In my view, these companies aren’t around yet because although the technology computational power is at a stage where AI can be harnessed for large-scale impact, data scientists are a rare find and are in high demand. They’re rare because data science requires a blend of various skills: statistical modelling, programming and business savviness. It’s hard enough to find people who are experts in any one of the above individually, let alone find someone who has mastered all three. 

How to Take Advantage of Data Science (Without Hiring a Data Scientist) 

There are two main approaches for how you can spice up your current projects with data science. One is to license data science technology from another company. The problem with this approach is that AI technology has not sufficiently matured to be generic enough for broad use, though there are a few startups doing great work in this field.

The second approach is what we adopted at 12 Labs. It was to build an in-house data science team. When we started, we didn’t have any data scientists. But within a year, we built several data science products that even established companies in the fitness space have not been able to build.

Here are the steps we took to build our from-scratch data science team:

1. Find an engineer who is a hustler with a good product sense. Aspiring product managers in your team might be a good fit, as frequently such people work on developing a good product sense while they are still working as an engineer.
2. Hire someone with a statistics background. I don’t mean a data scientist. You need a pure statistician because your engineer will need pointers from someone who is strongly grounded in statistics and machine learning. As I understand it, even a strong data scientist can’t tell you which approach or algorithm is the right one without trying several approaches first. So, if you have someone willing to try out several approaches (in this case, the engineer above), the statistician can point them in the right direction.
3. Use Python. There are many online courses about machine learning and data science in several other languages such as R, Octave, etc. However, Python has the most vibrant data science community, with a large number of open-source libraries. If you use Python, you’ll future-proof yourself against any closed platform traps.
4. Don’t wait for perfection. The quickest way to make your product perfect is to ship it. So, when we first launched our meal recommendation technology, it was far from being production grade. But — with the data we had — it was the best could do. We could have waited until we had collected more user data, but decided to ship sooner as users are often more tolerant with something that is novel and cool. Gradually, our meal recommendation technology has become state of the art.

Data science is here to stay and it is going to disrupt every field. It’s just a matter of time before your competitors adopt it, and then you’ll be left behind because technology usually favors the first to move on it. So, it’s imperative for startups to make a move sooner rather than later. Luckily, you can start even without a data scientist.