AI can help many types of businesses get more from their data. In 2021, one expert believes adoption of AI will take leaps forward.
TechRepublic's Karen Roby spoke with Ira Cohen, chief data scientist with Anodot, about the tools CIOs need to implement artificial intelligence (AI) at their companies. The following is an edited transcript of their conversation.
Karen Roby: As we're heading into 2021, CIOs need to have a checklist of some things to keep in mind when making decisions for this coming year, whether that be about hiring or projects to consider. Let's start with the talent that's needed at companies now, to pull off some of these AI projects. What do you think CIOs need to keep in mind?
Ira Cohen: As you said, 2020 was really special in all this disruption to so many businesses. And AI, actually, is now becoming even more important. The projects that maybe people talked about before have been accelerated now because the speed of movement to new paradigms, that has to be much faster. If you're talking about, for example, commerce, supply chains, need to move much faster. A lot of different projects that maybe before were slowly moving towards more e-commerce, and more shipment. I mean, you're getting your Amazon, but now, so many companies are sending what they're selling out, that you have to have a lot more automation and be a lot more mindful of the data, and be a lot more reactive to how things change constantly. Things are changing much faster, and AI is the perfect thing to manage all of that, if we talk about AI in a very, very global sense, because it has a capability of processing data very fast, giving you insights very fast of very high volumes of data, which is what's happening now, but that's what's needed.
What do you need to actually have in your company in order to actually be able to achieve these goals of these projects? The first order of business, and this is something that people and companies have been doing in the last few years is, put all your data together. Create these data lakes. Data lakes have been very popular, and growing at companies like Snowflake, and other types of companies that have grown tremendously in the last few years, because that's what they offer. But, now, to leverage those data lakes, you need data engineers that know how to pull data quickly out of them, and serve them to the data science team that can actually transform them with algorithms into meaningful insights.
Data engineers is something that is going to be required a lot more in the next year or so, because without those data pipelines, laying of data pipelines that will feed all these AI algorithms and projects, there is nothing. The AI doesn't work without data, at least the AI that we have today. And then comes the machine learning engineers. Today, data science has been something that has grown in the last few years. The data scientists are the ones that are developing the AI required for all these projects. But data scientists, a lot of what was hired was basically people that do analysis. They do kind of one-off projects.
And, now, because these things are starting to be more and more automated, you don't need just a data scientist who knows how to do a project well, and prototype something, but you need the engineers that will make it into products, even if they're internal products. It's not a project anymore, it's internal products that have to constantly work for the company to deliver the rate that they need to deliver. These two areas, the data engineers, and the machine learning engineers, and not just the scientist, these are probably the areas where we need to ... I believe, CIOs need to invest most in their companies.
