Are data science and data engineering technologies mature enough

Are Data Science and Data Engineering Technologies Mature Enough?

On August 31, 2021, Mike Murchison – CEO of Ada, joined us for a Fireside Chat. Ada is a no-code AI-powered platform that empowers brands worldwide to provide personalized experiences at scale. Recognized by Forbes 30 Under 30 and EY’s Entrepreneur of the Year program, Mike is also a Fellow at Creative Destruction Lab and volunteer for VentureKids, a program for Canada’s underserved youth.

Kima: Considering the state of data science and machine learning today, are you finding technologies that feel mature enough? How much room is there for them to evolve and meet the needs of businesses?

Mike: There are mature technologies and there are many that are in the process of maturing that are I think have high potential. If you look at Cohere’s APIs for example or OpenAI’s APIs, there’s some exciting indications of the future of an generative models and natural language generation more broadly.

The most important point for me is that every company will be an ML company in the next 10 years. The idea of there being ML companies today is really a temporary banner or label that we’re putting on businesses in the same way that being a web company or a digital company was temporary.

Every company is a software company or web company today. If you’re not, you’re not really a successful company with a digital presence. If digital isn’t core part of what you do, you’re probably not succeeding. This is likely true in most industries; I don’t want to make too much of a blanketed statement.

Infostrux Mike Murchinson quote 2

I think the idea that ML is a specialized skillset and there’s a handful of companies that provide this technology is something that will melt away pretty quickly. What we will be left with is a recognition that ML is an inherent part of all business and for that reason, it needs to be viewed through the lens of how does this technology solve the problems better than the current tools I’m using.

Kima: It’s important to decide what’s the problem you’re trying to solve. You’re not trying to solve technical problems; you’re trying to solve business and customer problems.

Mike: That’s exactly that’s right, and where it’s very dangerous is this tendency to focus too much on the tool and not enough on the problem in those environments. That’s the downside to them. So it’s important that with the increasing number of ML tools at our disposal are also considered alongside other tools that are not ML in nature. I believe the percentage that’ll be ML tools that we all either invent in our case, or deploy from elsewhere, will grow over time.

That’s what I mean where I say every company will be an ML company.

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