I’ve always been a generalist at heart. I love learning random bits of information, and the idea of narrowing down to a single specialization felt limiting to me. When I was breaking into data, people advised me to pick an industry or niche to focus on—but I couldn’t do it. I wanted to learn as much as I could.
After working as a data analyst for over a year, I’ve started figuring out how to strike a balance: specializing enough to leverage my skills effectively while staying general enough to keep my options open.
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Solution: T-Shaped Approach
In my earlier blog post, How to Stand Out as a Data Analyst: The Power of T-Shaped Learning, I described the concept of T-shaped skills (see image below):
T-shaped professionals have deep expertise in one area (the vertical stroke of the "T") and broad knowledge across multiple disciplines (the horizontal stroke).
This combination allows you to be both a specialist and a generalist, giving you the best of both worlds.
Image Description: Diagram of a T, to represent a “T-Shaped Person”. The horizontal bar of the 'T' represents a wide range of knowledge or skills, while the vertical bar represents depth of expertise in a specific skill. Text labels clarify the horizontal bar as “Wide range of knowledge/skills” and the vertical bar as “Depth of expertise in a skill”.
Why It Works
Being both specialized and general helps me in two ways:
Adaptability: I can transfer my skills between industries or roles within a similar business function, like moving from product in gaming to product in e-commerce.
Unique Value: A diverse skill set keeps me engaged and growing while also making me stand out.
Specializing alone isn’t rare, and being a generalist alone can lack focus. But combining both approaches—what Tim Ferriss (Should You Specialize or Be a Generalist Video) calls "skill stacking"—creates something unique and valuable.
Cal Newport in So Good They Can’t Ignore You (Amazon affiliate link), highlights that rare and valuable skills make you indispensable. Similarly, David Epstein’s book Range (Amazon affiliate link) argues that broad, interdisciplinary thinking improves problem-solving and transferrable knowledge, making generalists better equipped to navigate modern work environments.
How I Apply it To Data
The T-shaped approach gives me great framework for balancing specialization and generalization. For me, the 3 pillars of expertise: data, department, and industry, help translate this concept into actionable focus areas. My depth (the “I”) comes from specializing in data analytics, while my breadth (the “dash”) comes from applying those skills in product teams and within the social gaming industry.
Image Description Diagram of a T to represent a “T-Shaped Data Person”. The horizontal bar of the T is labeled “Department & Industry”' representing breadth of knowledge across business domains. The vertical bar is labeled “Data Field”, representing depth of expertise in a specific data role. The title below the T reads “T-Shaped Data Person”.
The Three Pillars
I think about my career in three pillars that support my growth:
Data: What type of data work do I want focus on? Examples: data analytics, data engineering, or data science. This pillar focuses on my technical skillset.
Department: Which business area do I want to support? Examples: product, customer service, or finance. These roles are often transferable across industries.
Industry: What overall domain am I in? Examples: healthcare, education, or gaming. This requires building domain knowledge specific to my field.
While I didn’t focus on soft skills here, they’re just as important. I’ve often heard that technical skills get you in the door, but soft skills like communication, problem-solving, and attention to detail are what get you promoted.
What I Specialize In
Below are my specific specialities:
Data: Data analytics (tools: SQL, Python, and Google Looker Studio).
Department: Product. I support the product team by understanding our product, how we make money, and key business metrics.
Industry: Social gaming. I study competitors, follow industry trends, and network with others in this space.
Building a Roadmap
Each pillar gives me a clear idea for my learning and growth:
Data: Improve technical skills like SQL and Python, and stay updated on data trends. For instance, Python isn’t always required for data analysts, but it’s invaluable if I want to transition to data engineering or data science.
Department: Learn to think like a product manager (and better if you can collaborate with a product manager). Understand what we sell, how we generate revenue, and foundational product concepts.
Industry: Dive into social gaming by researching competitors, reading industry blogs, and connecting with peers.
If you're building your own roadmap, start by identifying the pillars that align with your role and goals. Focus on improving your technical skills, understanding your company's goals and processes, and immersing yourself in your industry. Whether it’s improving in SQL or Python, collaborating cross-functionally, or staying up-to-date on industry trends, each step helps you grow with your long-term career goals. Remember, this isn’t just about technical skills understanding your department and industry adds context that makes your contributions more impactful.
Conclusion
Finding a balance between generalization and specialization isn’t about rigidly sticking to a single path. It’s about being flexible and focused by knowing what skills to build, how to transfer them, and what makes your expertise unique in a crowded field. By knowing these three pillars: data, department, and industry. Hopefully you can create a roadmap for your career that allows you to grow, adapt, and stand out.