, I started my first ever full-time job as a Senior Data Analyst at a leading health insurance company right after completing my graduate school, bringing with me a strong foundation in analytics and business.
Five years later, I’ve now worked on an array of analytics skills, including reporting, data visualization, stakeholder management, business strategy discussions, and, more recently, AI-assisted development. These five years have taught me lessons that have little to do with specific tools but a lot to do with understanding people, decisions, and outcomes.
My journey in data & analytics started seven years ago, when I started grad school to study analytics and business. Alongside my studies, I interned first as an R&D Intern working with data sources and creating BI solutions. Then came my Data Science internship where the code grew more complex, the data got messier, and the dashboards needed to meet executive standards. That experience became the cornerstone of my current success.
I realized that being someone who codes in Python or crunches numbers is not enough. I have to be a strategic problem-solver.
Reflecting on half a decade in the field, from a Data Analyst to Senior Analytics Consultant, I’ve witnessed three major shifts:
- Analytics has become more business-driven than technical
- Storytelling is more valuable than reporting
- AI is reshaping what “technical skills” mean
Recollecting my time as an Analytics Consultant, I want to share five lessons that transformed the way I approach my job and can help anyone working in analytics.
1. Storytelling with data is more important than data itself
As you grow in your career, you find yourself more often in rooms where decisions are made, you quickly realize that data alone rarely drives impact. How that data is communicated and consumed is what truly influences outcomes.
From my experience working with stakeholders with varying levels of technical experience, a stakeholder may not remember the regression model you built or understands model accuracy, but they surely remember the story that helped them make a decision. The value of data isn’t in its mere existence but it’s in its ability to be understood, trusted, and acted upon.
At a meetup a few years ago, a speaker shared that narratives make data far more memorable than numbers alone, and that stayed with me. Since then, I’ve approached most of my analyses with three simple questions:
- What happened?
- Why does it matter?
- What should happen next?
In my role as an analytics consultant, my work does not end with delivering the correct information; my job is to reduce uncertainty so that my stakeholders can act with confidence.
Data enables that process, but storytelling completes it.
That said, as AI becomes the “first analyst” before you even touch the data, here’s my caution: storytelling does not mean shaping reality to fit a narrative. AI can generate compelling stories far better than a spreadsheet, but it can also introduce assumptions or numbers that don’t exist.
Storytelling may be more powerful than the data itself, but its strength depends entirely on the integrity of the data behind it.
2. The hardest part of analytics isn’t analysis. It’s asking better questions.
I was taught in graduate school that as an analyst, we should be curious people. Because curiosity helps us find patterns and make sense of data. But over time, I realized it’s not just curiosity or the data itself that gives us great insights. It’s the questions we ask about it.
You can have the cleanest datasets and the most advanced tools, but without the right questions, your analysis will drift aimlessly.
For my team of business consultants, I recently conducted an analytics bootcamp to teach them the fundamentals of data & analytics. In the second week of sessions, I was asked: “I can learn the tools, but how do I learn what questions to ask as an analyst?” That was such a relatable question because when I started out, I had no playbook. I was constantly unsure what to ask stakeholders, which methods to use, or how to know when I’d found something meaningful. My goal with the bootcamp was to answer exactly that question.
Over time, I learned that better questions come from collaborating closely with subject matter experts (SMEs) and unpacking the problem statement with them. These conversations surface assumptions and lead your way where to dig deeper, which also reinforces the value of building a strong network for when an SME isn’t available.
Your takeaway in one line: start with curiosity, and then apply critical thinking. Don’t jump straight to the data.
Pause and ask what’s really going on, then layer your thinking with the why, what, who, and when.
3. Knowing when to keep digging and when to stop
For the first couple of years, I genuinely believed if I want to be a good analyst, I should not stop at the first answer. I should gather more, filter more, ask more. That instinct served me well, until it didn’t.
I was once working on an effort to create a service intensity report, to analyze clients who needed more support, cost more to the organization, and identify what drives the service intensity. The data was incomplete and inconsistent from the start. However, instead of pressure-testing whether it could even support the project objective, I kept pushing forward by pulling in more datasets, testing more hypotheses, and chasing anomalies that turned out to be noise. After nearly five weeks of trying to force the data to work, I finally told my manager we couldn’t proceed.
That experience taught me one of the most important lessons I now share with every junior analyst I mentor: more digging doesn’t always mean more value. Somewhere along the way, you go from uncovering insights to wasting time on finding insights nobody asked for.
So now, before I go down a rabbit hole, I ask: if I find something here, will it actually change what I do next? If the answer is no, that’s my cue to take a second look or stop, write up what I have, and move on.
4. Managing expectations is half the job done
Nobody tells you this in grad school, but a large part of being a successful analytics consultant has nothing to do with analytics and a lot to do with managing what people expect from you, your data, and your timelines.
Early on, I treated every ask at face value. If a stakeholder wanted a dashboard “by tomorrow,” I’d lose sleep making it happen, often at the cost of accuracy. It took me a while to learn that just because I can, doesn’t mean I should. The real job is having a conversation around the ask: what’s actually driving the request, what decision it supports, and what’s realistic given the data we have.
A few things I now do almost instinctively:
- Flag data limitations upfront
- Restate the ask in my own words, so misalignment surfaces early
- Communicate progress in small increments, rather than going dark and resurfacing with a finished product
Managing expectations doesn’t mean saying no more often. I have learned to set healthy boundaries with stakeholders, be honest throughout, so trust doesn’t get fractured later.
5. AI is changing what I think a “technical skill” means
When I started out, being technical meant writing efficient SQL, building clean Python pipelines, and knowing your BI tool well enough to make it tell a story. Today, AI can write that query, draft that pipeline, and suggest the chart type before I’ve finished framing the question. Those skills still matter, but the work has shifted quietly underneath us.
With all the noise around what AI can and can’t do, the real technical skill now lives not in producing the work, but in judging it. I wrote a blog post recently about metacognitive regulation being the most important AI skill nobody’s talking about—how we need to adapt our thinking as AI takes on more of the work.
I’m sure we’ve all caught AI-generated analysis confidently stating numbers that don’t exist, or recommendations that sound sharp but miss context any analyst with six months of tenure would have caught immediately. Being “technical” today is no longer limited to coding, cleaning and transformation to create a data pipeline or writing up project summaries. You need to understand the data well enough to know when an AI answer is subtly wrong in the first place.
Since 2025, with the advent of AI, I’ve stopped measuring my technical growth by which tools I know, and started measuring it by how well I can evaluate what those tools produce.
Prompting is a skill. Validating is a skill. Knowing when to trust the machine and when to trust your own judgment instead—that might be the most technical skill of all.
Looking Back, Looking Forward
Five years in, the tools I use for analytics and reporting have changed more than I expected, and I’ve up-skilled more than I ever had time for. Yet my questions for any analytics project haven’t moved much: What happened? Why does it matter? What should we do next? Can I trust this? Should I keep digging, or should I stop?
Closing out, if I had to leave one thought for anyone just starting out: the data will keep getting bigger, the tools will keep getting smarter, and AI can absolutely do a lot for you—but the job has always been, and will always be, about helping people make better decisions with more confidence.
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That’s it from my end on this blog post. Thank you for reading! I hope you found it an interesting read!
Rashi is a data wiz from Chicago who loves to analyze data and create data stories to communicate insights. She’s a full-time senior healthcare analytics consultant and likes to write blogs about data on weekends with a cup of coffee.