The Data Anarchy Tax: Why your team is firefighting 45% of the time.
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“My CEO, who funds the organization out of pocket, discovered vibe coding for analytics. So, he’ll upload a spreadsheet and keep hammering it until he gets the answer he wants. Then we have to explain to him why the answer he got was wrong.”
“Leadership changes calling for AI wins without knowing how to lead and align to actually accomplish what they are asking for causing misaligned expectations, no direction, and burnout across many teams.”
In my surveys this year, “lack of leadership direction” and “poor requirements” combined for nearly twice the share of “legacy systems” as the top bottleneck. Yet this is relatively unexplored territory, and to my knowledge, nobody has actually mapped what that organizational dysfunction for data engineers looks like in practice. In this pulse survey, I asked 8 to 9 (1 optional) questions that took a minute or two to fill out.
This pulse survey got 212 responses, and I’ve been chewing on the results. Who owns the stuff their company runs on? Not in theory. In practice. Who is on the hook when a dashboard breaks at 9pm, when a number looks wrong in a board deck, or when a data model quietly rots in the corner?
For most of them, the answer is often, nobody. Let’s dive in.
Who Owns Data Products? Often, Nobody.
The survey split ownership into two buckets, because there’s a bit of nuance. There’s the infrastructure - the pipelines, jobs, and transformations. Then there’s the data products - the datasets, dashboards, and models that stakeholders actually touch.
The infrastructure is fine. Somebody owns the plumbing:
- 54% point to a dedicated platform or data engineering team.
- 31% name the specific engineer who built it.
- Call it 85% with a clear owner for the pipes.
Now the data products. The things the business makes real decisions on:
- 74.5% have no dedicated owner. None.
- 25.5% said nobody owns them at all. The survey option for that one read “nobody, it’s anarchy.” A quarter of you picked anarchy.
- The rest limp along on the data lead carrying it by default (34.9%) or whatever stakeholder happened to ask (14.2%).
Sit with that gap for a second. We solved the boring, invisible layer (infrastructure) and left the layer EVERYONE depends on to fend for itself (data products). One in four of you is shipping dashboards into your company that no living human is responsible for six months from now.
If you build data products for a living, none of this is news.
No Owner? You’re Paying the Anarchy Tax.
Here’s where org-chart trivia turns into your actual calendar.
I asked how much of the work week disappears into unplanned, reactive work - the stuff nobody put on a roadmap. Overall it runs about a third. Then I split it by ownership:
- Dedicated owner: ~27% of the week reactive.
- Nobody owns it (anarchy): ~45% of the week reactive.
This is the Anarchy Tax: If you don’t own your data products, you spend 45% of your week firefighting - 18% more than teams with clear ownership.
The fires start where you’d guess. Pipeline failures. Bad data caught by a stakeholder before you caught it. And urgent asks from leadership that land like a brick through the window. The real kicker is how those questions show up. Only 16% of you get a proper ticket with acceptance criteria. Everybody else gets a Slack message, a hallway “hey, real quick,” or nothing written down at all. You’re expected to build durable things out of disposable instructions, and then you own the outage when the disposable instructions turn out to be wrong.
One respondent nailed the no-owner life better than I can:
We have ceased being engineers and are instead like TPMs tracking data and responding to everyone’s incidents. We only react and have zero agency.
That’s the 45% week of reacting to stuff, in one sentence.
AI Didn’t Rescue the Messy Teams
Let’s now talk about AI, since I’m sure you’re wondering if it’s helping, hurting or doing nothing. I asked whether AI has made your organizational dysfunction better or worse over the past year. The headline looks like a shrug:
- ~31% said better.
- ~34% said worse.
- ~35% said no real change.
Stop there and you’d swear AI does nothing to how teams function. But everyone in this survey already uses AI, the March pulse had exactly one holdout out of 194. One. So the tool can’t be what separates the happy teams from the miserable ones. Something else is.
Surprise, it’s leadership. Split the same responses by whether leadership gives the team any real direction, and the shrug falls apart:
- Leadership gives you direction (even a messy, constantly shifting one): AI nets +18 (Productivity).
- Leadership gives you nothing (delegates with no cover, or has no strategy at all): AI nets −26 (Chaos).
That’s a 44-point swing in how people feel about AI, and the thing that moved it wasn’t the model. It was whether an adult set a direction.
At the extremes, it’s almost comical. Teams who said leadership is fine reported AI helping them five to one. Teams whose leadership delegates with no air cover reported it hurting by more than three to one. Same tools, opposite worlds.
Here’s a respondent stuck in the bad one:
All that AI has done is put data operations and analytics teams in the trenches with horrible adhocs by leadership to optimize for things that don’t need to be optimized... Yet leadership thinks that creating more agents will solve the problem.
Here’s another:
AI accelerated the data debt of data modelling & Business comprehension.
AI is an amplifier. Point it at a team that has its act together and it genuinely makes them faster. Point it at a team drowning in bad direction and it just helps them drown quicker. Whatever was already in your org, AI cranks the volume.
This Keeps Showing Up. Every Survey. All Year.
None of this is a one-off. It’s the same finding my last few surveys have been circling, just aimed at a different corner of the org:
- January (n=1,101): organizational problems beat technical ones as the top bottleneck, and lack of ownership was already the #2 data modeling pain point at 51%.
- April (n=334): I asked what would most improve data modeling. “Better tooling” finished dead last, at 4.8%.
- June (n=212): the same ownership hole, measured this time on the products people actually consume. It’s wider.
Three surveys. Same answer. Nobody’s accountable for the work, and there is no tool on earth that sells you accountability. Plenty have tried. It doesn’t come in the box.
So What Actually Works?
The good news is buried in the same data, and it’s deeply unsexy. The teams that got better didn’t buy anything. They gave the work to an owner.
A dedicated Data PM:
Getting a Data PM has helped significantly with bridging the gap between engineering and product.
Ownership shoved out to the domains that requested the work:
A payments driven pipeline should be owned by the payments team... stakeholders have more skin in the game when it comes to building resilient solutions.
A team that just built the foundation, brick by brick:
For the first time in our company data is aligned for this domain... a company over 150 years old.
Notice what’s missing from all three. A tool. Every one of them is a decision about who owns what, made and then actually backed by leadership. The fix for an ownership problem is ownership. No shit.
What Should You Do?
If you read your own 45% week in these numbers, I won’t pretend a survey fixes your org chart. It doesn’t. But it hands you something useful: evidence. Next time you ask for a data product owner, or for the right to say “no, that’s not a real ticket,” you can drop 212 of your peers on the table, plus the ugly gap between the teams that have an owner and the teams running anarchy.
Use it. Push. Get clarity (and sanity on ownership).
Anyway, go find out who actually owns your data products. I’ve got a bad feeling you already know the answer.
Finally, reply to this email or drop a comment: Who actually owns your most critical dashboard?
I want to hear the horror stories.
The June 2026 Practical Data Pulse Survey: 212 anonymous responses, May 26 to June 21, run through the Practical Data community, Substack, and LinkedIn. Like every pulse, it’s a read on the same community at one moment in time, not a matched panel, so treat the January and April comparisons as directional. Full transparency: one respondent said they found the survey through a survey-swap service, which doesn’t move the numbers, but I’d rather you hear it from me. The dataset’s free. Just view or download from Google Sheets.