“No one wants to use software. They just want to catch Pokémon.”
— The Staff Engineer’s Path
That line gets right to the heart of it. People don’t care about your tech stack — they care about results. And in data engineering, that truth hits hard.
No business stakeholder is asking for raw parquet files or a lesson in SQL window functions. They want answers. Clear, timely, actionable insights that move the needle. Our job isn’t to flex our pipeline muscles — it’s to deliver clarity in a sea of noise.
Whether it’s optimizing a campaign, spotting a churn risk, or answering “What just happened and why?”, our work only matters if it helps someone do their job better.
In this post, I’m unpacking two core ideas:
- People want insights, not raw data, and
- Technology is a means to an end.
That’s the mindset shift. When we internalize it, we stop building for the sake of building — and start delivering what actually matters.
Technology Is a Means to an End
Building data systems is a lot like designing a public transit system. Riders don’t care what kind of engine is in the bus or how the subway tracks were laid — they just want to get where they’re going, reliably and on time.
Same goes for data. Nobody in marketing or ops is sitting around wondering how your Spark jobs are scheduled. They want their dashboards to load, their KPIs to make sense, and their questions answered fast. The infrastructure behind it? That’s your job — not theirs.
Success in transit isn’t measured by bus specs or track length. It’s: Did the thing run? Was it fast? Was it cheap? Same with data. A beautiful pipeline that delivers the wrong numbers — or arrives late — is still a failure.
Our job is to keep things fast, frictionless, and invisible. Just like a transit system that fades into the background when it works well, your data stack should disappear behind the insights. That’s the bar.
Turning Raw Data into Insights Is Hard
The easiest thing in the world is to hand someone a table. You pull some raw or aggregated data, send it their way, and check the box: request fulfilled.
But now they’ve got homework.
The real question should be: What decision is this data supposed to support? If you’re not answering that, you’re just offloading the analysis.
Analytics isn’t about dumping numbers — it’s about making sense of them. It's about surfacing trends, flagging anomalies, and helping teams act with confidence. If you're working with support data, for example, it's not enough to show a list of tickets — you need to highlight spikes, categorize issues, and show how staffing lines up against volume. Raw dumps just bury the signal.
But delivering insights is hard. And it’s not just a technical problem — it’s an organizational one too.
Organizational Hurdles
- Vague requests: Stakeholders often can’t articulate what they need, so they default to “Just give me access to the raw tables.”
- Tech tunnel vision: Instead of starting with the business question, people get lost in the weeds of schemas, joins, and filters.
- Misaligned goals: Business and data teams speak different languages — and often chase different metrics.
Technical Hurdles
- Analysis paralysis: There are a million ways to slice the data. Where do you even start?
- Changing priorities: What’s urgent today might be forgotten tomorrow. Reporting turns reactive instead of strategic.
- Narrow context: I once worked on a virtual event platform where we tracked chats between candidates and recruiters. We had the duration and a rating — but no content or context. We couldn’t tell what was going well or going wrong.
Turning raw data into something useful takes more than SQL chops. It takes curiosity, empathy, and a constant push to understand the “why” behind every request.
How to Avoid Sending Raw Data
The default move is to send a dataset. But if all you do is ship numbers, you’re not helping — you’re delegating. Your goal isn’t to give people raw ingredients; it’s to serve something that’s ready to eat.
Here’s how to avoid the data dump trap:
Ask better questions
Start with:
“What decision are you trying to make?”
Don’t stop at “I just need a list.” Push gently. Ask why. Ask it again. Peel back until you hit the root business need. That’s the request you should be solving for.
Walk a mile in their shoes
Before you hit send, put yourself in the stakeholder’s chair. If you got this spreadsheet, would you know what to do next? Would it raise the right questions or just cause confusion? If it’s not actionable, it’s not ready.
Build a flexible feedback loop
Static reports die on delivery. Get into the dashboard with your users. Click through it together. Ask what makes sense and what’s missing. Quick iteration beats slow perfection every time. The longer the feedback loop, the less the data actually influences the decision.
This isn’t about spoon-feeding every metric. It’s about guiding the request so what you deliver actually solves the problem — without making them sort through 17 joins and 50 columns first.
Proactive Insights: The Future
Let’s be real: most BI today still runs on email threads and manual dashboards. Someone asks a question, someone else pulls a report, and maybe — if you're lucky — it makes it to the meeting in time to matter.
It’s reactive. It’s slow. And it’s exhausting.
Now, there’s a wave of tools claiming to flip this — platforms that scan your data and surface insights automatically. Generative AI is speeding this up. But let’s not kid ourselves: it’s early days. Most of it’s still smoke and mirrors.
And even if the tech works, the trust isn’t there yet. Especially post-GDPR, most companies aren’t eager to hand their data over to a black box for “auto-insights.”
But the north star is clear.
In a better world, insights just show up. The moment something breaks a trend — user churn spikes, sales drop in a region, performance tanks on a new feature — someone gets notified. No request. No delay. Just signal, delivered.
We’re not there yet. But that’s where we’re headed.
Conclusion
Data engineering isn’t about building pipelines for the sake of it. It’s about making the right things obvious — at the right time.
If what we deliver doesn’t help someone make a decision faster, smarter, or with more confidence, then what are we even doing?
The best data work fades into the background. The user doesn’t see the Spark job, the ELT logic, or the hours of schema cleanup. They just see clarity.
That’s the goal: deliver insights, not infrastructure. Use technology as a tool — not the headline. And always, always build with the end user in mind.
We might not have proactive, plug-and-play insight systems yet. But every time we cut complexity, clarify a request, or build something that actually helps someone do their job — we’re getting closer.
Thanks for reading.