Shuchi Agrawal: Finance & AI leader “Breaking the Binary” by reimagining legacy banking through human-centric, autonomous systems.
Across my career in financial services, healthcare and aviation, I have watched large organizations wrestle with the same basic problem: Everyone wants to use data, but no one trusts it or understands it in quite the same way. Risk sees the world one way, marketing another, operations a third. Regulators add their own lens on top.
In that environment, organizing data engineering around products and business lines was survival. “Data as a product” became a way to put structure around chaos with clear ownership, shared definitions, reusable datasets and lineage that auditors, regulators and business leaders could live with. It allowed teams to move away from fragile, one‑off extracts and homegrown spreadsheets and toward something more stable and scalable.
For a while, that shift delivered exactly what it promised.
What Data Products Actually Fixes
In a global banking environment, moving to data products could mean risk, pricing, compliance and customer teams finally stop arguing about whose numbers are “right.” Instead of stitching together their own data, they pull from a shared, governed source. Credit calls improve, and pricing meetings become less about reconciling reports and more about decisions.
In a healthcare environment, product‑oriented data work might help connect patient, operational and experience data that has lived in separate systems for years. That enables more coherent patient journeys and more consistent reporting to executives and regulators.
In aviation, airlines and airports use data products to bring together operational, booking and customer data. The conversations shift from “Why doesn’t this match?” to “How do we reduce missed connections?” and “How do we recover faster when a storm hits?”
At its best, data product strategy helps align data engineering with real business domains by building reusable assets around actual needs and making data easier to discover and trust.
Where The Gap Shows Up
The gap becomes obvious when you look at outcomes.
A pristine customer dataset does not, by itself, improve retention. A modern risk platform does not automatically reduce losses. A real‑time market feed does not make a trading desk smarter unless the right insights show up in the tools traders and risk managers actually use to act.
I have seen beautifully engineered data products celebrated at launch and quietly sidelined six months later because they never really made it into the day‑to‑day workflow. The dashboards existed and the numbers reconciled, but the impact never quite materialized.
Most executives I work with are not asking for more data in the abstract. They are asking for better, faster, more consistent decisions around better pricing, earlier risk detection, fewer customer surprises and more effective interventions.
That is the heart of the problem. Many organizations became excellent at making data available, but much less effective at embedding it in the moments when decisions are made.
How Expectations Have Changed
Ten years ago, delivering reliable, governed data to the business was a major accomplishment. It meant executives did not have to negotiate over whose spreadsheet would be used in the meeting. In some organizations, that alone was transformative.
Today, that is the floor, not the ceiling. Business leaders are tired of juggling dashboards and reconciling conflicting metrics. They want clear insight. Increasingly, they want the system to help them prioritize and, where appropriate, trigger the next action.
In capital markets, for example, the conversation is no longer just about building a better risk data mart. It is about spotting emerging risk earlier, triaging which positions to look at and surfacing those concerns directly in the tools traders already use.
Once you view the problem through that lens, the definition of the “product” changes. The product is no longer the dataset. The product is the decision system.
What It Looks Like In Practice
In financial services, that shift means moving from “we built a customer 360” to “we reduced churn in this segment” or “we tightened our response to early risk signals.” I have worked with risk leaders who care far less about the elegance of a data model and far more about getting a reliable, early warning signal in front of the right person in time to act.
Pricing teams want tools that weigh market conditions, customer history, competitive context and margin constraints. Relationship managers want to know which client to call today and why—not get a 40‑page analysis after the quarter is over.
The same pattern shows up in customer satisfaction and sentiment work. Banks and healthcare providers have plenty of reports. What they need is targeted, timely prompts for which customers are at risk, which complaints are signs of deeper issues and which service failures require immediate recovery.
In aviation, I have seen the most progress when data products are connected directly to disruption management: which flights, crews and customers are most exposed and what actions can be taken now to avoid a domino effect later in the day. The magic is not in knowing a storm is coming, but in using that knowledge to reassign aircraft, adjust crews and communicate with passengers in ways that matter.
In all of these cases, the underlying data products matter. None of it works without clean, governed, trustworthy data. But the visible value is higher up the stack, where insight turns into action.
The Question That Matters Now
For several years, the organizing question was simple: How do we build better data products?
That question is evolving. The better question now is: How do we create a repeatable decision advantage?
Boards and executive teams want to know what improved, where risk decreased, how revenue or margin changed and what manual work disappeared. “Better access to data” is no longer a satisfying answer. “Better pricing decisions,” “fewer false alerts,” “faster case resolution” and “higher retention” are.
Data products are not going away. They are moving from center stage to the supporting cast.
It is the natural progression of a discipline as it grows up. The first phase was about getting the data house in order. The next phase is about wiring that house into how the business actually makes decisions.
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