I was in a meeting recently with a VP of Data at a mid-size enterprise when she said something that stopped me. We were talking about her team’s quarterly roadmap, and she paused and said, almost to herself: “We have faster pipelines than we’ve ever had, and somehow decisions still take a week.”
She wasn’t frustrated with her team. She was thinking out loud about something that didn’t quite add up.
I’ve been having versions of that conversation a lot. The data infrastructure got better. The decision speed didn’t. And the more time I spend inside these organizations, the more obvious the reason becomes. The stack was never designed to make decisions. It was designed to store and move data, and somewhere along the way, everyone assumed the rest would take care of itself.
It didn’t.
The Architecture Made Sense. Until It Didn’t.
The modern data stack wasn’t built wrong. It was built for the problem that existed at the time.
(Ico-Maker/Shutterstock)
A decade ago, the core challenge was centralization. Data lived across operational systems, logs, and internal tools that didn’t talk to each other. The goal was to pull it into a warehouse where analysts could query it reliably. Every layer of the stack reflects that goal, from ingestion through transformation to a dashboard at the end for someone to look at.
The system ends where a human begins. That was fine when data access was scarce and analytical questions came in slowly. It becomes a bottleneck when an organization needs to make decisions continuously, across fast-moving operations, on a timeline that doesn’t accommodate a three-day analyst queue.
The infrastructure now processes data in seconds. The interpretation layer hasn’t changed.
SaaS Sprawl Made It Worse
One operations director described the problem plainly. “We have 14 systems that all have a slightly different definition of revenue. And my analysts spend half their time figuring out which one to trust before they can answer anything.”
That’s not a data quality problem. It’s an architecture problem. The modern enterprise runs across CRMs, billing platforms, product telemetry pipelines, ticketing tools, and accounting systems that each define the same business concepts slightly differently. ETL tools centralized the data. They didn’t resolve the semantic differences. They moved them downstream, into the warehouse, where they became the analyst’s problem.
As the stack grew more technically capable, the burden on the people inside it grew with it. Understanding which tables to trust, which definitions apply in a given context, and how metrics relate across systems requires institutional knowledge that accumulates over years. The analyst becomes the interpreter of the system, a role that never appears in a vendor’s pitch deck.
What Slow Decisions Actually Cost
The frustrating part is that the relevant information usually already exists.
via Shutterstock
Signals show up in enterprise data before they show up in the business itself. A product issue, a shift in customer behavior, a decline in engagement — these things surface first in usage data and support activity. The data stack captures them. Then nothing happens, because someone has to notice them, query them, validate the definitions, assemble the context, and bring it to the right people.
By the time the organization acts, the situation has usually escalated. One VP described finding out about a major customer churn risk three weeks after the signals first appeared in the data. “It was all there,” he said. “We just didn’t see it.”
That’s not a pipeline problem. That’s a decision infrastructure problem.
The Assumption That Needs to Change
Most attempts to fix this focus on infrastructure performance, faster pipelines, more scalable warehouses, better dashboards. Those improvements matter. But they leave the underlying assumption untouched.
The architecture assumes that a human analyst initiates the decision process. Someone has to ask the question before anything happens. A different model starts from the opposite premise. Decisions should happen when signals appear, not when someone schedules an analysis.
That requires systems that can reason across live data sources, understand how signals relate across domains, and act when conditions are met. It doesn’t require consolidating every system into a single warehouse first. The relevant context often exists across operational platforms that organizations will never fully unify, and waiting for perfect centralization is a way of waiting forever. The real work is enabling reasoning across those systems as they are, not as we wish they were.
Measuring the Wrong Thing
The organizations I spend time with evaluate their data infrastructure through technical metrics, pipeline reliability, warehouse performance, query latency. Useful for engineering. Not the number that matters to the business.
(Shutterstock AI Image)
The number that matters is the time between when a signal appears and when someone acts on it. That interval is what determines how quickly an organization responds to a customer issue, an operational failure, or a window that won’t stay open for long.
Most companies have invested heavily in reducing pipeline latency. Very few have touched decision latency. The VP asking why decisions still take a week, despite the fastest pipelines she’s ever had, has been given tools that optimize for the wrong thing. The ones who recognize that now have time to do something about it. The ones who wait will spend the next few years building faster roads to the same traffic jam.
About the Author: Soham Mazumdar is a serial entrepreneur and technology leader, currently the Co-Founder and CEO of WisdomAI, an AI-powered data insights platform that helps enterprises query their data using natural language. Previously, he co-founded Rubrik, where he served as Chief Architect and helped scale the company to IPO. Soham also co-founded Tagtile (acquired by Facebook), and led core search infrastructure at Google.
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The post The Modern Data Stack Was Never Built to Make Decisions appeared first on BigDATAwire.
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Author: Soham Mazumdar
