The Data Industry: Is It Now Just Reactive Firefighting and Eroded Trust?

Jessie Moelzer
Aug 8
5 min read

Why the Modern Data Stack has become noise and how there is a smarter, quieter path forward.

The more you listen to today’s data conversations, the more they feel like fire alarms going off in every direction. Data quality is under attack. Tooling is out of control. Data teams are drowning in complexity. Trust in AI and analytics is fraying. The noise is deafening, and the question that keeps coming up for us at Data Tiles is:

“Why is no one taking a different path?”

Every day, a new data tool promises to “fix the mess.” Vendors pitch dashboards, orchestration, observability, even LLM plugins. But walk into most organizations and what do you see? Business teams waiting. Engineering teams’ firefighting. And trust in data? Eroding by the hour.

It’s not a tech problem. It’s a philosophy problem.

Cameron Price, recently reflected on over 35 years in data, witnessing the drift from purpose to process, where pipelines matter more than insights, and budgets flow into engineering tools instead of business outcomes.

He said it best in his recent blog, The Day They Merged ‘Data’ and ‘Engineering’ Was the Day the Industry Died,

” What was once about empowering humans to make better decisions has turned into an obsession with pipelines, processes, and platform spend—all while business outcomes stall,”

And honestly? He’s right.

A Culture of Firefighting, Not Forward Thinking

Too many data teams are stuck in reactive mode.

According to Monte Carlo, poor data quality impacted 31% of revenue across organizations in 2023. Even more alarming? In 74% of those cases, it was business users who first spotted the issue—not the data teams.

Gartner echoed this, stating that over 60% of data leaders feel their investments in AI and analytics are “undermined by complexity and lack of trust.”

Why? Because most companies are still operating in a world where data access requires translation, between domain experts and engineers, business needs and batch jobs.

That’s not data driven. That’s bureaucratic paralysis.

At Data Tiles, we believe these aren’t isolated failures. They’re signals of an industry culture that’s completely out of sync with its own mission. Tooling has become more important than transformation.

What was once a strategy of enablement has become a stack of blockers.

Everyone Is Chasing AI. Few Are Actually Ready

Forrester made it clear:

“AI’s success isn’t about the model; it’s about access to the right data.”

Yet most companies still gate that data behind pipelines, schemas, and engineering resources.

You can have the best LLM in the world, but if it can’t understand your business context because your data is fragmented or hidden behind CLI tools, it’s useless.

Meanwhile, Gartner’s 2024 analytics outlook pointed to a major blind spot,

“Data and analytics leaders continue to underinvest in data product design, prioritizing engineering and ingestion over usability.”

We’re watching the industry miss the forest for the trees. Again.

At Data Tiles, we believe it’s time to flip that script.

Engineering-Centric Models Are Breaking the System

Let’s be honest. Most modern data stacks feel more like Rube Goldberg machines, an overly complicated contraption designed to perform very simple tasks through a series of chain reactions, than actual business solutions. They keep engineers busy. They make vendors rich. But they rarely help domain teams move faster.

In his blog, Cameron argued,

“Ask a data engineer what they shipped, and they’ll list DAGs, jobs, and partitions. Ask a business leader what they got, and they’ll shrug.”

He’s not wrong. The tooling arms race has created a situation where value is measured in deployment events, not decisions made.

Harvard Business Review captured it perfectly:

“The missing link isn’t skills or tools, it’s storytelling. It’s the ability to translate data into decisions.”

And we’ve abstracted so far away from the people who need the insights that we’ve broken the loop.

Stop the Cycle of Reinvention

We don’t need another orchestration tool. We need to enable business people to work with data, live, directly, without waiting weeks for a sprint to deploy.

That’s the gap Data Tiles is closing.

At Data Tiles, We Took a Different Path

We didn’t build Latttice to join the modern data stack race. We built it because the stack itself was fundamentally flawed. We want to fix what is broken.

Latttice is the only zero-code, AI-native data mesh platform that enables non-technical users to create, govern, and connect data products in minutes, without relying on data engineers.

That last part matters. Because it means,

No more waiting on sprints or tickets
No more translating business questions into engineering tasks.
No more pipelines for the sake of pipelines.

It’s built for the business and domain teams.

It’s built for simplicity.

It’s built for insight, not infrastructure.

Reimagining Data Mesh for the Business

In a recent blog, Agentic-Driven Data Mesh: A Shift to Autonomous Data Management, Cameron explored what happens when you empower domain teams with AI agents that let them create and govern data products themselves.

This isn’t some far-off dream. It’s what Latttice does today.

It’s what we, at Data Tiles, believe Data Mesh was supposed to be. Not a technical theory, but a business-first model for sharing trusted, contextualized, governed data products across domains.

And when you give business users that power, everything changes:

Trust goes up
Time-to-insight goes down
Engineers are free to focus on system stability, not dashboard requests.

Designing for the Human at the Centre of Data

At Data Tiles, we are convinced the greatest failure of today’s data tooling isn’t just its complexity—it’s that it forgets the human on the other side.

Data isn’t meant to live in pipelines. It’s meant to live in decisions.But somewhere along the way, the industry stopped designing for the person reading the chart, asking the question, or trying to act.

Cameron Price said it best:

“We’ve mistaken movement for momentum—while the human trying to make sense of their world through data gets buried in dashboards they don’t understand or waits weeks for access they should already have.”

That’s why we built Latttice differently.

Every feature we design starts with a simple question:

“Can a non-technical person use this to make a decision?”

Whether it’s natural language querying, ai-powered data product creation, or plain-language governance, our aim is to bring people closer to their data, not abstract them further away.

Because insight should feel intuitive. Access should be instant.And data should serve people, not the other way around.

We’re Not in the Quagmire. We’re Over Here.

While everyone else is stuck in firefighting mode, managing tools, hiring more engineers, offshoring, or adding layers of dashboards that no one uses, we offer something radically different: