Many data scientists and analytics professionals master the technical craft; coding, modeling, and optimizing. But making the leap from hands-on contributor to leader requires a whole new set of skills.

In this episode, we sit down with Brian Evergreen — global AI strategist and author of Autonomous Transformation — to explore how Fortune 10 AI leaders think differently, and how today's data professionals can start building that same mindset. Brian shares the seven leadership skills that help data experts grow into transformative leaders.

If you're a data scientist or analyst wondering what's next for your career, or how to lead with vision, this conversation is for you.

• How technical experts can evolve into strategists and value creators

• Why systems thinking is a hidden superpower

• How to shift from optimizing existing processes to creating entirely new value

• Why storytelling is a leadership skill every data professional needs

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When people think about data work, they often picture analytics pipelines or machine learning models. But two areas that often get overlooked are just as important for broadening your impact:

1. Business acumen — understanding markets, stakeholders, and how to become a trusted advisor.

2. Data products — navigating purchased datasets like Nielsen's Billboard ratings or syndicated consumer data.

In this episode, Solomon Kahn will explain both of these worlds.

From building trusted relationships to making decisions about buying data and what is available, Solomon will show why the most effective data professionals know the business of data from all different aspects.

• Why business acumen is a critical skill for data professionals (and how to develop it)

• How to move from being a technical contributor to a trusted advisor

• How purchased datasets like Nielsen Billboard ratings shape entire industries

• The economics of data as a product, and how to assess ROI before you buy

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Too often, data conversations get stuck in silos: tools, models, or dashboards in isolation. But data only delivers value when the entire ecosystem works together.

In this episode, Dylan Anderson, Head of Data Strategy at Profusion, joins us to break down what it really means to think holistically about data. We'll explore how business strategy, organizational design, governance, tech debt, and engineering all connect, and why ignoring these links creates inefficiency and frustration.

Dylan also shares how he develops "expert generalists" on his team, building data professionals who can see beyond their technical skills and drive real business outcomes.

Whether you're leading a team, looking for your next role, or just trying to elevate your impact, this conversation will help you zoom out and think like a strategist, not just a technologist.

• Why treating data topics in isolation leads to hidden inefficiencies and wasted effort

• How to align data strategy with business goals (and avoid "models for models' sake")

• Practical ways to assess if a company is taking a holistic approach before you join

• How to grow as a data professional by becoming an "expert generalist"

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In this conversation with Nagim Ashufta, Founder of DRIVA GmbH, we dig into what it really looks like when organizations try to get serious about data.

Nagim started his own agency to help companies accelerate their data capabilities, and he's seen the same challenges come up again and again. For those in analytics seeking to understand the realities of a data role, this is a great view into the challenges that organizations face, and how you can be prepared.

If you're an analytics professional, this episode will give you a practical look at the landscape, the pitfalls, and the messy-but-important role you play in helping data actually deliver.

• Why data is almost never the first role companies hire for, and what this means

• The common patterns across organizations that struggle with analytics maturity

• Why "everyone is a data steward" and what stewardship really means in practice

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In this episode, we're joined by Terry Dorsey, Senior Data Architect & Evangelist at Denodo, to unpack the conceptual differences between terms like data fabrics, vector databases, and knowledge graphs, and remind you not to forget about the importance of structured data in this new AI-native world!

• The difference between data fabrics, vector databases, and knowledge graphs — and the pros and cons

• Why organizing and managing data is still the hardest part of any AI project (and how process design plays a critical role)

• Why structured data and schemas are still crucial in the age of LLMs and embeddings

• How knowledge graphs help model context, relationships, and "episodic memory" more completely than other approaches

If you've ever wondered about different data and AI terms, here's a great glossary to check out from Denodo: https://www.denodo.com/en/glossary

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Data scientists have the skills to model complex systems, work with messy data, and uncover hidden patterns.

Quant scientists do all of that, but with the added thrill (and pressure) of putting real money on the line.

In this episode, we sit down with Jason Strimpel, Founder of PyQuant News and Co-founder of Quant Science, to explore why data scientists are uniquely positioned to excel in algorithmic trading.

Whether you're a data scientist curious about finance, or simply interested in seeing your models have a more personal impact, this show offers a fresh perspective on how your skills can translate into the world of algorithmic trading.

• How your Python, stats, and modeling skills transfer directly into the markets

• The mindset shifts required

• Why reproducibility, auditability, and backtesting discipline are the data scientist's secret weapon

• Common pitfalls when transitioning into quant roles, and how to avoid them

• The tools and workflows Jason recommends to get started fast

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In this show, we're joined by Sean Chandler, Director of BI at CenterWell Home Health, to explore what it really means to thrive in BI today.

Sean shares his personal journey, including his move into teaching, and offers practical insights on building a career in BI, self-learning for advancement, and fostering a strong partnership between BI and data science teams.

Whether you're an aspiring BI analyst, a data scientist aiming to improve collaboration, or a career changer eyeing the BI space, this episode is for you.

• How to successfully transition from other roles into BI, and how to know if it's the right fit for you

• What good collaboration between BI and data science actually looks like, and how to recognize when it's broken

• How self-taught skills can accelerate your BI career, even without a formal background

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AI isn't just accelerating productivity, it's reshaping how we structure power, make decisions, and define what "value" means in our organizations.

In this episode, Felicia Newhouse — a data leader, AI strategist, and advocate for ethical, human-centered innovation — brings deep insight into how power structures in business are being reimagined in the age of AI.

We dive into why so-called soft skills like communication, critical thinking, and emotional intelligence are becoming core differentiators in data and AI teams.

With research showing that companies deploying AI without leadership transformation risk ethical blind spots and cultural misalignment, we ask: What kind of leaders do we need now? And how can data professionals step into that shift?

• Why soft skills are no longer optional in data and AI roles

• How leadership, ethics, and communication shape responsible AI outcomes

• Real-world examples of companies gaining trust and performance by elevating human-centered skills

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