Making Payments Data Pay Off

Getting the most out of the data exhaust depends on an idea called analytics-as-a-service.

In the fast-moving world of fintech, every swipe, tap, and online checkout generates more than just a payment. It generates data. And in today’s digital economy, that data exhaust is becoming one of the most valuable assets a fintech platform can offer its merchants.

As consumer expectations continue to shift toward immediacy, personalization, and transparency, fintechs are recognizing that real-time analytics can no longer be just a nice-to-have. Instead, it is fast becoming a core differentiator and revenue driver, not just for the platform itself, but for the millions of merchants and developers building on top of it.

The volume and velocity of transaction data processed on modern payment platforms is staggering. Embedded within that data are insights waiting to be unlocked: purchasing behaviors, regional trends, risk indicators, and operational inefficiencies.

Yet, historically, merchants have had limited ability to access and interpret this data. Some have resorted to building their own custom reporting systems, often requiring significant engineering investment and continuous maintenance. Others rely on manual processes or delayed batch reports that arrive too late to be actionable.

This is where the analytics-as-a-service model comes in.

Insights-as-a-Service

Leading fintech platforms are now providing analytics layers directly within their ecosystems, giving clients real-time visibility into their transaction and payment flows. These insights are made accessible via easy-to-use dashboards and APIs, allowing merchants to generate custom reports, query financial data,
and embed intelligence directly into their workflows.

For sectors like e-commerce and software-as-a-service, where business models depend on rapid iteration and real-time feedback, the ability to interact with live data can mean the difference between scaling profitably or getting stuck in reactive mode.

The practical applications of this model span a wide range of use cases:

• Finance teams can automate reconciliation and month-end reporting with up-to-the-minute data, eliminating manual effort and reducing errors;
• Growth and marketing teams can monitor purchasing patterns and payment funnel drop-offs in real time, adjusting campaigns on the fly;
• Operations teams can detect anomalies, failed transactions, or unusual customer behavior and take immediate corrective action;
• Product managers can explore user-level data to inform feature prioritization and pricing strategies.

With these capabilities baked directly into their payments platforms, merchants are not only saving time but also unlocking strategic insights that would be prohibitively difficult to generate on their own.

Analytics-as-a-Service

Here are a couple of examples of what I’m talking about.

Stripe’s merchant analytics-as-a-service offering empowers businesses to gain real-time insights from their payment data. By providing intuitive dashboards and a flexible interface, Stripe enables merchants to monitor key metrics instantly and at scale.

Stripe’s merchant-analytics platform delivers sub-second query performance across billions of transactions, helping its customers make faster, data-driven decisions. This continuous intelligence has become a strategic asset for both merchants and Stripe itself.

Razorpay, a leading payments platform in India, is bringing the vision of merchant analytics-as-a-service to life by delivering real-time visibility into payment success rates for its vast merchant base.

The company enables its teams to monitor transaction performance instantly, identify anomalies before they impact users, and optimize payment flows across billions of data points. What once required complex batch jobs and delayed insights is now available in subsecond time.

Why Real-Time Matters

The true power of analytics-as-a-service lies in its speed and accessibility. Traditional data warehouses are well-suited for deep analysis and historical trends, but they struggle to meet the demands of real-time, high-concurrency applications.

That’s why leading fintech platforms are turning to real-time Online Analytical Processing (OLAP) engines purpose-built for delivering sub-second query performance at scale. By embedding this technology into the fabric of their platforms, fintechs are not just offering a value-added service, they’re becoming an indispensable intelligence layer for their customers.

But analytics-as-a-service doesn’t just benefit merchants. It’s a strategic lever for fintech platforms themselves. By offering built-in intelligence, platforms deepen customer engagement and become more central to their
clients’ day-to-day operations.

Merchants that rely on real-time insights to drive critical decisions are more likely to remain loyal and grow with the platform over time. It also opens the door to tiered offerings, where advanced analytics features become part of premium service plans or tailored enterprise solutions.

Furthermore, the platform reduces internal support costs by eliminating the need to build one-off reporting solutions for every customer request. It’s a scalable, self-service model that benefits everyone involved.

The Engine

At the heart of this transformation is Apache Pinot, an open-source, distributed real-time OLAP database designed to deliver low-latency queries at massive scale.

Pinot is engineered to support the kinds of high-concurrency, high-ingestion-rate workloads that payment platforms demand. It allows for querying billions of records in subsecond time, with support for high-dimensional filtering and aggregations that make it ideal for powering merchant-facing dashboards and APIs.

Its architecture makes it uniquely suited to serve as the engine for analytics-as-a-service offerings. By enabling real-time insights on top of live data streams, Pinot unlocks a new paradigm, where decisions happen as data arrives, not hours or days later.

We’re at a pivotal moment in the evolution of financial platforms. As fintechs serve increasingly sophisticated and fast-moving customers, embedded analytics will no longer be optional. The ability to deliver insights at the speed of the transaction will define which platforms lead the market, and which ones fall behind.

—Chad Meley is senior vice president, developer relations, for StarTree.