Most data professionals are really good at one thing. They know their domain, they know their tools, and they go deep. That depth is how you get hired. It is also how you get stuck.
The further you go in your career, the more you run into problems that don’t live inside a single domain. A pipeline that nobody uses because the data model underneath it was wrong. A dashboard that answers the wrong question because nobody talked to the business before building it. A data strategy that looks good on paper and falls apart when it hits the actual architecture.
I sat down with Dylan Anderson , data strategist, consultant, and author of The Data Ecosystem newsletter, to talk about what it actually means to think across the system. Dylan came from business consulting before moving into data, which means he sees this from both sides.
The conversation covered how companies actually operate, what separates the people who get ahead from the ones who stay stuck, and one habit that changes how you work without requiring you to become an expert in everything.
Here is what I took away.
The Data Ecosystem Is Not Your Tech Stack
What most people call the data ecosystem is really a landscape, a snapshot of tools at a point in time. It changes occasionally, but it’s static.
The ecosystem is something different. Think of a pond. Everything inside it is connected: the soil, the water, the microorganisms, the plants, the animals. Remove one element and the whole system shifts.
Data works the same way. Your data model affects your engineering. Your engineering affects your analytics. Your governance affects your ML. Your business model shapes all of it. These aren’t separate domains you visit when a ticket arrives, but a system, and if you only know your corner of it, you’re operating blind.
The problem is that most people work reactively. They handle what’s in front of them. They talk to the governance team when there’s a compliance issue, not because they thought about how governance connects to what they’re building. They don’t think about the relationships between domains. They just navigate them when forced to.
Dylan’s point is that the people who think proactively about those connections, who anticipate how one decision ripples into another domain, are the ones who produce better work and get trusted with bigger problems.
That’s a systems skill. And most data training never touches it.
Paid members consistently share they got promoted or praised because they apply my guides.
Most Companies Are Still a Mess
Dylan has worked with a lot of billion-dollar organisations. His read: mostly chaos.
Data strategy documents exist. They talk about vision and use cases. They don’t connect to the architecture underneath, the governance layer, or the engineering reality on the ground. Nobody in the building knows how all the pieces fit together, not even the CDO, who spends most of their time managing upward.
The result is piecemeal data. Companies ahead in one domain are five steps behind in another. They buy great BI tools on top of poor data models and wonder why the dashboards don’t deliver. They bolt AI onto existing business processes and get some ROI but miss the structural shift entirely.
What’s almost always missing is the link between business strategy and how data actually enables the organisation. And the people who see that link, who say “we need to think about this before we build that,” are the ones who get trusted, get promoted, and get asked into the room where decisions happen.
Dylan’s observation from client work: the best performers in every organisation he’s walked into are the ones thinking across domains. LinkedIn will have you believe most companies are running cutting-edge data stacks. The reality for 95% of them is legacy chaos, and that gap is an opportunity for anyone willing to think holistically about it.
Data People Need Business Literacy
We obsess over data literacy. Get the business stakeholders to understand data, read the dashboards, ask better questions. Dylan flips it.
Data people need business literacy just as much, and most of them don’t have it. They don’t know how the business model works, what the real KPIs are, why the marketing team cares about a number that looks meaningless from an engineering seat. They build things nobody uses because they never asked what outcome was actually needed.
The gap is generational too. Five or ten years ago, most people who moved into data migrated from somewhere else, finance, operations, product. They understood business from the inside.
Now whole cohorts start their careers as data people. They’ve never sat on the other side of the table.
Dylan’s framing is direct: if you want to influence the business, you have to speak its language. That starts with understanding the business model, the KPIs, what each stakeholder is actually measured on.
Once you have that, you stop presenting data and start presenting decisions. Stakeholders notice. They start saying you’re different from the engineers who only think about the technical problem. That’s when trust builds and trust is what gets you the work that matters.
The “So What” Frame
Dylan had a consulting partner early in his career who rejected every slide that didn’t answer one question: so what?
Not “is this technically correct“, or “did you finish it“ So what. What changes because of this? What’s the action out of it? Dylan hated it at the time. It’s second nature to him now.
It’s the fastest way to pull yourself out of ticket mode and into strategic thinking. You don’t need to study the whole data ecosystem to start thinking more holistically. You need one habit applied every time you deliver something:
Does this matter?
To whom?
What do they do next?
If you can’t answer that, you’re producing output. The business is paying for impact.
That question also protects you from the trap Dylan sees in most data teams, the reactive, firefighting, ticket-based mode that makes it almost impossible to step back. When every day is about closing tickets, you never ask whether the tickets matter.
The “so what“ frame forces that pause. Fifteen minutes of strategic thinking before you build something is worth more than three days of building the wrong thing fast.
What AI Changes and What It Doesn’t
AI is taking over individual technical tasks faster than most people are comfortable admitting. Routine work, boilerplate code and first-draft outputs are largely handled.
What’s left is context. AI doesn’t know your organisation. It doesn’t know why a specific stakeholder cares about a specific number, or whether a proposed solution fits your actual business model, or what you’ve already tried and why it failed. That’s your job.
Domain expertise plus the ability to connect domains, to say “this decision over here affects that system over there“, is what makes AI output useful rather than generic. The people who will matter in the next five years are the ones who can think across the system.
AI handles the execution. You handle the judgement.
Dylan is also working on the next layer of the problem. How business models fundamentally change in an AI-first world. Most companies are bolting AI onto existing processes and getting some ROI.
The shift is rethinking how the organisation operates at the root. It has implications for data modelling, governance, architecture, strategy.
Nobody has solved that yet. It’s the open problem, and it’s where the real opportunity sits for anyone thinking holistically right now.
Where to Find Dylan
Dylan writes the Data Ecosystem newsletter on Substack. Weekly deep dives on every domain in the data space and how they connect. If you want to build the holistic view we talked about, start there.
He also posts daily on LinkedIn. If you want to connect, include a note. He reads them and responds.
And this summer he’s releasing a LinkedIn Learning course on insights-led thinking in the age of AI. Worth watching for.
Final Thoughts
The data industry has spent years rewarding depth. Know your tool, know your domain, go deep. That got a lot of people to senior. It stops working somewhere around there.
The problems that matter at the senior level and beyond don’t live inside a single domain. They live in the connections between domains.
Between the data model and the engineering. Between the engineering and the business outcome. Between what the team is building and what the organisation actually needs.
The people who see those connections are the ones who get trusted with the hard problems.
Business literacy, holistic thinking, asking “so what“ before you ship anything. None of this is complicated. It’s just not what most data training covers, so most people never build it deliberately.
You can wait until your career stalls to figure that out. Or you can start now, while the gap between you and everyone else who only knows their lane is still wide enough to matter.
Until next time,
Yordan
PS: Many paid members tell me they got promoted after working through the resources inside. If that sounds relevant to where you are right now, here’s what you get with a paid membership.
