The Data Scientist Unicorn

Squark recently hosted a fireside chat with esteemed AI industry expert (and Squark advisor) Dr. Tom Davenport and Squark Co-CEO/Co-Founder Judah Phillips, “What Executives Need to Know About AI and ML in 2022 with Tom Davenport.”

Among the many points discussed was how the understanding of executing data science has evolved over the past decade. As Dr. Davenport points out, “We realize that data science is much more than we initially thought it was, which was using data to create machine learning models. We now know we have all these steps related to the data… and data scientists are unlikely to be able to do all of those things. There is no data scientist unicorn.”

The steps for executing a machine learning data science project include preparing, improving, and modeling the data. Then there are the MLOps tasks; deploying the model into production, monitoring it to make sure it doesn’t drift and maintaining lifecycle updates due to changing parameters or conditions. If you rely on an internal team, you’ll have to hire additional resources to run and maintain your models so you get value from them.

Businesses are turning to citizen data scientists and tools to purchase to achieve their AI and data science goals.

No Unicorn Required

“If I were starting now, I would use a new model for AI talent. I might hire a couple of data scientists, but mostly I’d try to say, can we get by with citizen data scientists and train people to do work and use tools like Squark… I think the idea where data scientists are necessary to do all the modeling is only required in really advanced companies, ultimately.” – Tom Davenport

Squark is a no-code SaaS tool that provides an end-to-end data science solution for predictive analytics. Companies can upskill existing employees and control overhead while successfully running AI prediction projects. In just a couple of clicks, users can execute:

  1. Data Ingestion
  2. Data Preparation
  3. Build and Manage AI Models
  4. Schedule and Simulate Future Outcomes
  5. Explain Why Outcomes Occur with Transparent, Explainable AI Reports
  6. Scales as You Grow To Meet Your Needs

Takeaway: Data Scientists are just one piece of a successful data science program. Businesses either need to fill more positions or find a technology solution to execute their machine learning, predictive analytics projects.

Listen to the entire webinar here.

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