What is the Problem with Data Science?

This is the question I’ll be asking Andrew Jones on November 14th (and yes, you’re invited!).

(click here to register)

If you’re wondering who Andrew Jones is, I’ve included his bio below. Suffice to say, he’s a trailblazer in data science—a thought leader who has trained thousands of data scientists and influenced hundreds of thousands more.

So, I can’t think of a better guest to discuss the problems with data science and how they can be solved. I’m also eager to test a few of my own theories with him.

Because I believe that the biggest problem with data science is simple: it just isn’t used enough.

As someone who consults with a diverse range of companies, I’m consistently surprised by how few have invested in the resources needed for data-driven decision-making. And that’s what data science is really about—using data to make informed decisions at both strategic and operational levels.

But there’s an even bigger issue.

Even in companies that have invested in systems to capture relevant data, the tools to analyze it, and the resources to translate it into insights, a final mile problem still exists: the decision-makers don’t listen to the data scientists.

This challenge appears at both the board and executive levels. And it has less to do with data science and everything to do with human bias.

Cognitive dissonance, loss aversion, the “not invented here” syndrome, skepticism, hyperbolic discounting—there are many psychological reasons why leaders fail to act on the data they’re given.

In my view, this is the most significant challenge data scientists must tackle.

Because when you boil it down, data scientists can’t just present data. If they want to succeed, they have to translate it into something more compelling. They need to be storytellers.

And the stories they craft must evoke emotions that are strong enough to override biases and build trust.

I’m excited to ask Andrew whether he shares this perspective.

I’m also looking forward to exploring other challenges companies face with data science and the practical steps they can take to maximize its power.

Because data science is no longer a “nice to have.”

The rise and rise of large language models means we have reached a tipping point where companies can no longer thrive without embedding data science at their core.

And as most struggle to even used data on the fringe, how on earth are they going to achieve that!

Hopefully Andrew will provide the answer!

Andrew Jones Bio

Andrew Jones is a trailblazer in data science and AI, dedicated to empowering individuals and businesses to navigate the fast-evolving landscape of data and technology. As the founder of Data Science Infinity, Andrew has built a reputation for helping thousands of aspiring data scientists and established professionals master the skills and practical knowledge needed to succeed in the field. His unique approach combines rigorous technical training with real-world application, bridging the critical gap between data theory and impactful practice.

With a background at industry giants like Amazon and Sony, Andrew brings a wealth of practical insights from his hands-on experience working with data at scale. His expertise spans machine learning, data engineering, and strategic AI implementation, making him a sought-after thought leader in the data science community. Andrew’s commitment to demystifying complex concepts and fostering an inclusive, supportive learning environment has inspired a global following of data enthusiasts.

In addition to his work with Data Science Infinity, Andrew is a respected speaker and educator, frequently sharing his insights on AI trends, upskilling strategies, and the essential alignment between data science initiatives and business goals. His mission is clear: to prepare the next generation of data scientists and ensure they are equipped to drive meaningful change in their organisations.

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