Common Challenges in Data Careers

After 30+ conversations with data leaders this year, here’s the most common sentence I’ve heard: “I didn’t expect the people part to be this hard.” Everyone thought the hard part of being a CDO would be the tech. Or the data. But it’s actually: → Convincing 10 departments to align on one metric → Navigating fear (“what if I get it wrong?”) → Translating insights into real decisions One CDO told me: “It took us 9 months to define ARR. Not because the math was hard - but because everyone had their own version of the truth.” This is why data leadership is so hard to scale. The best leaders I’ve met are treating this like a change management problem first, a data problem second. What’s one lesson you wish you’d known before leading a data org?

Here's a secret about the data industry that I don't think too many people realize... nearly everyone is struggling with the "best practices." When I started my data career, I thought the companies I worked for were struggling. However, when I started creating content and interviewing other leaders, I realized it was not just me. Once I joined a data infrastructure vendor, it became clear that it was an industry-wide challenge. I've been on 100+ discovery calls and countless deep-dive calls with companies ranging from startups to Fortune 500-- every single one has made it clear to me how hard the basics are to do in data. IT IS NOT A SKILL PROBLEM! Data is just that hard to work with and is in a constant state of chaos. Here are a few examples of why: • The startup that initially focused on "best practices" ultimately scaled quickly and had to refactor its tech stack... which increased complexity. • The mid-sized company pivoted in its business model, and now the data needs to support two different workflows simultaneously as it transitions customers to a new product. • The massive enterprise company acquired seven companies in the past year, and merging these systems has been painful, yet the data is business critical. Even if you reach the promised land of "best practices" within your company, it will likely be short-lived as the company inevitably changes. What have you seen in your data career?

The Hardest Thing About Data Science Has Nothing to Do with Data. When I started in data science, I thought the hardest part would be learning Python or building models. But here’s what no one told me: The hardest part isn’t the technical stuff. It’s learning how to navigate ambiguity. Here’s what I mean: - There’s No “Right” Answer: Data doesn’t speak for itself. It’s your job to interpret it and make tough judgment calls. - Stakeholders Don’t Always Know What They Want: Half the battle is clarifying vague goals and turning them into concrete deliverables. - The Landscape is Always Changing: Tools, techniques, and expectations evolve constantly. You’re always learning on the fly. - Data science is less about knowing everything and more about being comfortable with the unknown. And honestly? That’s what makes it exciting. What’s been the most unexpected challenge in your career? Let’s talk about it. P.S. I’m Alfredo, an international student turned data scientist in the U.S., and I share insights on navigating a saturated job market, building a career, and overcoming challenges like visa hurdles. If this post resonates with you, follow along for more stories and advice!