AI is a team sport: Here’s how you can build a winning squadAI is a team sport: Here’s how you can build a winning squad
Today, many companies still look to hire “data unicorns” — that is, supernatural all-in-one data wizards who possess the entire range of skills that the organization needs to reach their AI goals. Not only is this an expensive and unrealistic strategy, but upon mapping out what the business needs and the skills required to fill those needs, it probably doesn’t make sense either
November 17, 2021
Delivering on AI and data objectives isn't an easy endeavor, and even as global maturity around AI initiatives is gradually increasing, many companies still stumble at one of the first (and trickiest) pitfalls: knowing what, and who, to look for when building teams and staffing initiatives.
A lot of the early AI hype focused primarily on the data scientist role, often expecting it to cover the whole data skills spectrum and fill every data-related organizational need.
However, this approach is fundamentally flawed. Instead, one of the biggest things businesses must understand to address this challenge is that AI is, in fact, a team sport.
Imagine a world where players on the field (or court, or insert your favorite sports terrain here) are all simultaneously following their own individual interpretations of the game strategy. Each player is making only the moves that align with his or her own personal strengths without coordination with others. Let’s just say there would be a lot of missed passes, failed shots, and ultimately, frustrated players and coaches.
This analogy probably sounds a bit outrageous, because that’s just not how sports work — everyone knows you need people with a range of skill sets and that they need to communicate and work together to reach their common goal. Even if a team had the best player money could buy, (s)he couldn’t do it alone. At the same time, hiring additional players all with the exact same skills as the superstar doesn’t work either — and the same goes for AI initiatives.
Today, many companies still look to hire “data unicorns” — that is, supernatural all-in-one data wizards who possess the entire range of skills that the organization needs to reach their AI goals. They have deep knowledge of architecture and infrastructure, but they also are also skilled at building machine learning models. They can communicate requirements and needs to the business, but they also know how to push a model into production. Not only is this an expensive and unrealistic strategy, but upon mapping out what the business needs and the skills required to fill those needs, it probably doesn’t make sense either.
Before hiring, consider exactly what the needs of the business are and which types of data profiles would add the most value. Making this type of decision before creating a job posting will be beneficial in listing specific skills and honing interview questions.
Achieving the proper balance between all the different data profiles is critical to an efficient data practice at the organization overall. Hiring too much of one profile and not enough of another can cause bottlenecks in processes and frustration all around.
Ultimately, finding a good mix of data professionals that is the right balance for the business is key to staff retention and to building a team that can actually execute on the AI ambitions of the organization. A well-oiled machine means happier employees, with fewer people having to perform tasks outside of their skill or comfort zones.
However, even with the hiring and upskilling resources and practices at hand, building and empowering great AI teams still presents a myriad of challenges. Data science, machine learning, and AI platforms (like Dataiku) are a clear win for data teams and, when implemented the right way, can serve as a foundation for building a great team.
By embracing the different strengths and technical skill sets of various contributors and enabling them to consolidate their work in a governed and organized way, the right tools can ease these types of pains and allow teams to develop AI projects both faster and more effectively.
Florian Douetteau is a senior executive with extensive experience driving growth and operational excellence at disruptive technology companies. He is currently the co-founder and CEO of Dataiku, the world’s most advanced Enterprise AI platform. The company was recently named to Forbes’ Cloud 100 and AI 50 lists. It holds a valuation of 1.4B.
Florian started his career at Exalead, an innovative search engine technology company. There, he led an R&D team of 50 brilliant data geeks, until the company was bought by Dassault Systemes in 2010 for $150 million. Florian was then CTO at IsCool, a European leader in social gaming, where he managed game analytics and one of the biggest European cloud setup. Florian also served as freelance Lead Data Scientist in various companies, such as Criteo, the European Advertising leader.
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