You are stuck because you are optimizing for the wrong thing.
Most data professionals I talk to work incredibly hard at their craft. They go deep on Python, on dbt, on Spark, on whatever the stack is.
They take courses, they build side projects, they read documentation on weekends. And then they watch someone else get promoted. Someone who, honestly, is probably less technically sharp than them.
Introducing David Langer
Dave Langer has been in technology for almost 30 years. He started on the help desk, wrote COBOL on a mainframe at an insurance company, moved into software engineering, and lived through the dot-com crash in the early 2000s.
The last 15 of those years have been in analytics, starting with traditional BI and data warehousing, Kimball star schemas, the whole thing, and then moving into more advanced analytics.
Dave worked at Microsoft leading a data and analytics team, then at a startup called Schedulicity as VP of Analytics, and at Data Science Dojo. He is a Microsoft Excel MVP, not for writing formulas, but for evangelizing Python inside Excel, which he got early NDA access to in 2023.
David wrote a book on Python and Excel published this year, and his second book on SQL for Excel users is coming in 2027.
These days Dave is an independent consultant and trainer. He runs a Substack called The DIY Data Scientist, which is exactly what it says: practical tutorials on data analysis for professionals who want to use data better, regardless of their background.
We had a live conversation, and this is everything worth taking from it.
The Value Ladder Is Not What You Think It Is
Every organization has a value ladder. A rough hierarchy of what it perceives as worth paying for, promoting people for, and building strategy around.
The problem is that your version of the value ladder and your organization’s version are often completely different things.
When you are early in your career, this does not matter much. You are rewarded for technical output:
Write good code.
Ship clean pipelines.
Produce accurate models.
That is the ladder, and being technically excellent gets you up it.
But at some point the ladder changes. And nobody tells you.
What the organization starts rewarding is perceived strategic value.
That could mean designing systems instead of building them. It could mean being able to translate between the data team and the C-suite. It could mean understanding enough about the business to push back on a requirement intelligently, not just implement it.
If you keep climbing the old ladder while the organization has already moved to a new one, you plateau. Only because you are solving for the wrong problem.
The Outsourcing Lesson
Think about what happened to a lot of senior engineers in the early 2000s. They had gone deep on C++ (Qt will stay in my heart forever), and read all the right books, so they could architect complex object-oriented systems.
And then globalization happened, and suddenly all of that specialized knowledge became a commodity. You could hire it for a fraction of the cost from an outsourcing firm.
The people who survived that shift asked: what is the value ladder actually rewarding right now? And the answer was architecture, system design, coordination, the things that required judgment and organizational context. Not just technical execution.
The same question applies to you today. Not “how do I get better at this tool?“ but “what is my organization actually paying for?“
If you want a concrete way to map where you actually sit on your organization’s value ladder, that is exactly what the Career Progression Matrix in the premium library does. Paid subscribers get access to it on day one. Upgrade here.
Data Problems Are Not Technology Problems
Here is what nobody told me early in my career:
The hardest part of working in data is not the data itself, but people
If you’ve been in data long enough, you’ve probably been in meetings about the definition of a customer for way too longs. And all of that, just because four departments each had a different answer, and each answer made a different executive look better or worse.
This is what senior data professionals figure out. And it is what a lot of talented junior and mid-level people never fully internalize.
If your goal is to do technically excellent work in a well-scoped problem, you can stay in your lane and deliver. That is a legitimate career.
But if you want to actually drive decisions, if you want your work to change how an organization operates, you are going to spend a significant amount of your time as a mediator.
Between engineering and business.
Between what the data says and what someone wants the data to say.
Between this year’s urgent request and the foundational investment that would make next year’s requests 10x easier to answer.
The people who succeed in that space are not always the most technically gifted. But they are always the ones who learned to sit in an uncomfortable call, name the real disagreement out loud, and not flinch when someone gets defensive.
Why Teaching Is a Career Asset
One of the underrated ways to build this skill is to teach.
Not necessarily a course or YouTube tutorial. Just the act of explaining complex technical concepts to someone who does not share your context.
It forces you to leave your own head and think from the other person’s perspective. You cannot fall back on jargon. You have to find the analogy that lands for this specific person, not the definition that would satisfy a technical reviewer.
That skill transfers directly to stakeholder work. Every time you explain a data model to a VP, every time you scope a request with a product manager who is not sure what they are asking for, every time you push back on a dashboard request by asking “what decision are you trying to make?“ you are doing the same cognitive work.
Data professionals who build that muscle early tend to accelerate later. The ones who skip it hit a ceiling.
Show & Tell is where I work through exactly this kind of thing live. One paid subscriber brings a real situation, and we work through it together in front of the group. Every session is recorded, and included in a paid subscription. Join here.
Executives Are Running the Same Hype Cycle Again
A few years ago, the mandate was “we need to be data-driven“. Companies hired data teams, built dashboards, set up data warehouses. Most of them never actually became data-driven. But “data-driven“ was the thing, so they built it anyway.
Now the mandate is “we need to be AI-first“.
The structure is identical:
An executive reads something.
The pressure cascades down.
Someone, often the data team, gets handed the brief: implement AI.
The business problem that AI is supposed to solve is treated as secondary. Sometimes it is not even identified.
I have seen a company run a churn model comparison: a basic logistic regression built by a data scientist, versus an LLM-based approach. The logistic regression hit 80% accuracy. The LLM hit 50%, which is coin-flip territory. The traditional model also cost a fraction as much to build and run.
And that is before you account for what happens when AI pricing moves from flat subscription to usage-based. When your CFO is looking at a bill where the token cost exceeds the salary of the team that could have built a simpler model, the conversation changes fast.
Most organizations are not thinking about this right now. They are still in the “we need an AI use case“ phase. The ROI calculation comes later, usually painfully.
The Hadoop Lesson Is Already Written
During the Hadoop hype cycle, an executive asked Dave whether the company could move their SQL Server databases to run on top of Hadoop instead of fast disks. Because Hadoop storage was cheaper. The executive had read about it in a trade magazine.
The answer was no. Obviously. But the fact that the question was asked at all tells you something about how technology hype filters through organizations.
It’s all about the anxiety of not being part of the trend.
MongoDB was supposed to kill relational databases.
Excel has been declared dead every year for 20 years.
SQL was going to be replaced by query builders.
None of that happened.
Some things change. Programming languages come and go. Platforms rise and fall. But the underlying problems stay the same: organizations need to make better decisions using data.
The data needs to be clean, structured, and understandable. The people consuming it need context.
The Two Questions Worth Asking
Here is what I would take from all of this.
First, be honest about your organization’s value ladder. Not the one you wish existed, but the one that actually exists.
What gets people promoted here?
What gets people noticed by leadership?
Is it technical depth, or is it business impact, or is it visibility, or is it the ability to manage up
Because the answer changes your strategy.
Second, ask whether that ladder aligns with what you are actually trying to build. If it does, optimize hard for it. If it does not, that is useful information too. The organization where the ladder aligns with your goals exists. Sometimes you have to find it.
The data professionals who stall out tend to do one of two things: they optimize for a ladder that their organization does not value, or they assume the ladder they are on right now is the one they will always be on.
Neither is true.
Find More From Dave
Dave’s Substack is The DIY Data Scientist. Every issue is a hands-on tutorial with workbooks, code, and data included, free. If you work with data in any capacity and want to actually understand what you are doing with it, it is worth subscribing.
He is also active on LinkedIn, where his content leans more toward the enterprise and organizational side of analytics.
His book, Python for Excel, is available on Amazon and in Barnes & Noble stores.
—
Until next time,
Yordan
PS: Paid subscribers get the Career Progression Matrix, the full Show & Tell archive, and the entire premium resource library. Upgrade here.
PPS: The next Show & Tell session is coming up. A paid subscriber and I will be working on a long on a long therm-career plan. Join here.
Let’s Connect
Connect on LinkedIn: https://www.linkedin.com/in/ivanovyordan
Work with me: https://www.ivanovyordan.com/coaching
Start journaling: https://www.dearself.ai
