The Future of Data Analytics in the Age of AI: Thomas Barber's Career Playbook

A Career Playbook Case Study

The Role

Thomas Barber is the Head of Commercial Data and Analytics at L.E.K. Consulting, a global strategy consulting firm.

What does data analytics actually mean? "The main objective of the job function is typically being able to create business value from data that a business might have. That could be data on its customers, data on its operations, or markets that it operates in. The point of data analytics is to have some type of business objective and then use quantitative analysis and data to back up any decisions related to that objective."

At a consulting firm like L.E.K., Thomas works across many different businesses rather than going deep in one company. Recent projects include:

• Helping a vet hospital revolutionize their pricing—figuring out how to customize dynamic pricing by location
• Helping businesses determine where to open new stores or enter new markets
• Building machine learning models for a healthcare insurance provider to predict customer retention

The variety is part of the appeal: "One day's problem could be pricing, another day's problem could be market entry strategy."

The Path

Education: Business major

Career progression:

1. L.E.K. Consulting — Started as generalist consulting analyst
2. Specialization — After a few years, chose the data track
3. Current — Head of Commercial Data and Analytics

Thomas's journey reflects a common pattern: "I graduated college as a typical business major. Didn't really know what I wanted to do. And I think like everybody in business that doesn't know what they want to do thinks, 'Oh, I'll do consulting because I'll be engaged in a ton of different types of problems.'"

The turning point came on a healthcare project: "I was consulting for a large national healthcare insurance provider. The whole project was designing a machine learning model to help figure out which customers were actually able to be retained and how they could access them and give the right care. That project was a bit of a turning point where I was able to deploy some pretty cool analytics that I hadn't worked on before, and I felt like it was for a mission that mattered to me."

Compensation

Data analytics offers solid compensation that scales with seniority and industry:

Key factors that affect pay:

• Industry: Big tech pays more than consumer businesses
• Job function: Data scientist vs. data analyst vs. data engineer all have different ranges
• Career path: Management track vs. individual contributor track
• Geography: Major tech hubs pay more

The Skills That Matter

Technical skills:

• Coding
• Analytics software
• Understanding different data types (healthcare claims look different from e-commerce data)

But the harder skill is human:

"One of the biggest challenges I face in my job is bridging the gap between what is technically feasible and what the business—let's say non-data savvy business leaders—might be expecting. You might have certain business leaders with goals, but they don't fundamentally understand the technical data we have available to solve that problem. Being a liaison between the data and the business is both probably the toughest challenge but also the most critical skill that data professionals need to attain."

The AI Question: Is Data Analytics Going Away?

Thomas's take is nuanced:

"AI is kind of commoditizing an analytic skill set."

But that doesn't mean the career is dying. It means the skills that matter are shifting. As technical skills become more automated, the human skills—translating between business needs and technical capabilities, asking the right questions, communicating insights—become more valuable.

The field is "shifting at a rapid pace." For students mapping out careers that will last decades, understanding this shift is critical.

Consulting vs. Internal Analytics

If you're deciding between consulting and working internally at a company:

Consulting pros:

• Exposure to many different businesses and industries
• Variety of problem types
• External perspectives

Internal pros:

• Go deeper into one domain
• More focused scope
• See long-term impact of your work

Thomas chose consulting for the breadth: "Part of what I really like about what I do is that given I'm consulting a broad set of businesses, I get exposure to a lot of different types of analyses."

The Hard Parts

What people might not expect:

"The toughest challenge is grappling with the gap between what business leaders want to achieve and what's actually possible with the data available. They have certain goals but don't fundamentally understand the technical constraints."

Success in data analytics increasingly requires being able to translate between two worlds—the technical and the business.

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