How to Build a High-Performance Data Science and AI Practice With Capgemini
Capgemini is a technology service and digital transformation consultancy based out of Paris, France serving clients across industries around the world. Alex Vayner is Capgemini’s brilliant Data Science and Artificial Intelligence Practice Leader. He’s spent the last three years building the company’s North American practice and helping clients navigate corporate transformation. We recently had the opportunity to sit down with Alex to capture his insights about this exciting and growing field.
Tell us a little bit about yourself and what you do.
I’ve been in the data science field pretty much my whole life. I went to graduate school at Georgia Tech—that’s how I ended up in Atlanta. I started as a coder and modeler writing mathematical and financial models for consulting companies and hedge funds. I slowly transitioned into being someone who helps companies acquire structure and exploit their data assets. My last few roles have been focused on building high-performance data science teams, capabilities, and functions.
At heart, I am a math geek. I came to America from the former Soviet Union where I was part of an advanced math school. When I got here, I realized I was a couple of years more advanced in mathematics than my peers, and it just sort of roller coastered from there. I feel like I’m a humanities person at heart and probably would have been a historian or an anthropologist in another life. It was through divine providence that I ended up being a mathematician, and I’m really enjoying the journey so far.
What is artificial intelligence?
There’s a lot of buzz and noise around AI. At its core, AI is computers mimicking and enhancing human behaviors. Oftentimes, that’s fundamentals like sight and hearing and other human senses. All of that data and information needs to be interpreted, and that’s where machine learning and deep learning comes in. At the end of the day, it’s all of those techniques under the umbrella that encompasses everything from robotic process automation to neural networks.
At Capgemini, I’m helping clients answer questions like: How do you use those techniques to understand more about your business, your customers, and gain insights? How do you drive an insight-driven transformation within your company? How do you make sure managers make decisions based on data and not their gut? How do you make sure that processes are automated? How can you ensure that KPIs are readily available and are part of the conversation at the executive level?
What have you learned over your career? Could you impart some wisdom on our readers?
There’s a lot of hype around data science, so do your due diligence. If you’re in the middle of your career and you’re focused on learning and growing, ask hard questions during interviews when you’re talking to prospective employers. They might say they’re doing cutting-edge data science. Ask them what kind of tools they’re using. They might say they’re industrializing solutions. Ask them what languages they’re using. Nuances like this can give you insight into how much is hype and how much is real.
If you’re a company, my suggestion would be experimentation. Before you declare huge ambitions and invest millions of dollars and years into building something, do frequent experimentations. If you’re on the IT side of things, reach out to the business partners early. Where we see a lot of efforts go into “the PoC graveyard” for data science is either the team building the solution didn’t partner with the business stakeholders from the early stages (so it doesn’t have a landing pad), or they had the business support but they didn’t talk to IT (so now they have a cool solution and they have champions in the business but they can’t industrialize it because they don’t understand how the systems work, how do you embed the solution into existing architecture). If you want to avoid “the PoC graveyard,” you have to bring those two teams together.
Sometimes those teams have tension. This is natural—that tension will always exist. That’s how you create something great and something new. Capgemini helps bridge the gap by creating partnerships that drive effective solutions from the early proof of concept stages to industrializing a solution that’s actually going to drive revenue for the company and maybe even transform and create new business lines, revenue streams, and business models.
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