Just do the Math Experiment

August 9, 2019

It has been almost two years since I posted anything here.  I have been preoccupied with growing a data science team that started when we recognized an opportunity to accelerate product development for our portfolio companies.  Mercury Data Science, Inc. (“MDS”) now has 15 people and is growing fast.

MDS is partnering with innovative, high-growth companies to move more quickly and efficiently to develop data science products in areas as diverse as Consumer/Retail/Marketing, Biotechnology, and Industrial sectors.

To explain how we got here, let me back up and start with some observations (circa late 2017):

Data Science (“DS”; also known as AI but that seems kind of hype(y) unless you are talking about self-driving cars, etc.) was becoming part of almost every software company.  In fact, it was kind of a running joke that every company had suddenly slapped AI lipstick onto their pitch deck.  The truth was that:

DS was becoming more like “cloud” in that most companies actually did REQUIRE a DS strategy to be competitive because it can make a huge difference in product performance, efficiency or customer satisfaction, and

DS was becoming a permanent and evolving part of the technology expertise that a corporation has to keep developing. To be 100% clear: DS needs are not like ordering a website refresh but is an ongoing core competency more akin to software development needs and maybe more necessary than that for some companies.

Similarly, we saw some companies with big data assets and plans for moving forward in DS to create value with that data but their team needed to bring in the necessary DS expertise. These skills are quite different from their existing software expertise. Also, the exact DS skills they needed are often a bit undefined at first (until some DS work is actually done on the data) so the companies needed some help getting started.  A chicken and egg issue here.

Even companies that knew what skills they needed had to work hard to find top data scientists to work on their problems and, in the process, were losing time getting a product to the market.  And yes, this observation is even more true in 2019.

None of this is unique to early stage startups or to Mercury Fund’s portfolio of companies.  Even growth stage or established companies were looking for how to move faster on DS.

In late 2017, I met a terrific data scientist.  We started introducing her to Mercury Fund portfolio companies and, 100% of the time, they needed and wanted her help.  This was the Light Bulb “ON” moment. There was no way that one person could do all the work so we thought “wouldn’t it be cool to create a company that could recruit top DS talent and have them drop in and accelerate companies in the early stage by building their DS products, vision and capabilities? And, wouldn’t it also be cool to be able to take the growth stage companies with big data assets and more quickly build defensible competitive value from that data?”

So that is what we did – we created a service company that to help speed development of DS enabled products.  Our thinking was that, to reach critical mass and attract top talent, we should work with any company that has a need and a cool problem that our data scientists want to solve. Now, two years in with 15 outstanding data scientists on board, we are doing just that. Dropping in and accelerating product development for companies using DS. And, our client companies have launched numerous DS products resulting from our work.

That’s what I have been up to.

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