I’ve been in the Data Science space for a number of years now, I first got interested in AI/Machine Learning in 2009 and have a background typical of a number of people in my field – I come from Physics and Mathematics.
One trend I’ve run into both at Corporates and Startups is that there are many challenges to deploying Data Science in a bureaucratic organisation – or delivering Enterprise Intelligence. Running into this problem led me to be interested in building data products.
One of the first people I saw building AI startups was Bradford Cross – and he’s been writing lately about his predictions for the 2017 in the Machine Learning startups space.
I agree with his precis that we’ll begin to see successful vertically-oriented AI startups solving full-stack industry problems that require subject matter expertise, unique data, and a product that uses AI to deliver its core value proposition.
At Elevate Direct we’re working on this working on the problem of sourcing and hiring contractors – so one of the fundamental problems that companies have which is hiring the best contractor talent out there.
So what are some of the reasons that it can be hard to deploy Data Science internally at a corporate organisation? I think a number of the patterns are related to other patterns we see in terms of software.
I think fundamentally a lot of these anti-patterns come from not knowing how to handle risk correctly. I like the idea in that RedMonk article that big outsourcing is a bit like CDOs in finance. Bundling the risk into one big lump doesn’t make the risk go away.
I learn this day after day working on building data products and tools at Elevate. Being honest about the risks and working hard to de-risk projects and drive down that risk in an agile way is the best we can do.
Finally, I think we’re just getting started building Data Products and deploying data science. It’ll be interesting what we see what other anti-patterns emerge as we grow up as an industry. This is also one of the reasons I’ve joined a startup and why I’m very excited to work on an end-to-end Data Product, which is solving a real-business problem.
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