Live Data Is Rapidly Reshaping Product Development Practices
Modern product development is undergoing a structural transformation, one that collapses the traditional divide between experimentation and analytics. As the pressure to deliver smarter, AI-enabled user experiences increases, engineering and product teams are embracing continuous improvement cycles driven by real-time feedback. The end goal? Make faster, data-backed decisions, not just about features, but about their impact on the business.
This shift isn’t just about tooling; it’s about architecture and accountability. The rise of warehouse-native platforms is enabling teams to run high-velocity experiments directly against live customer and financial data, without sacrificing speed, fidelity, or governance. In this emerging and transformative model, product development becomes a continuous feedback loop: ship, measure, learn, repeat.
I’ve seen the transformation firsthand. Earlier in my career, I worked on teams where launching a feature meant crossing our fingers and waiting weeks to see whether it made a difference. We had analytics tools and experimentation tools, but they didn’t integrate. Everyone was working with their version of the truth. It was frustrating, slow, and deeply inefficient.
That frustration ultimately led me to co-found Houseware, a warehouse-native product analytics company. We started with the belief that product teams should be able to work directly off the data infrastructure they already had. What began as a scrappy idea to empower product teams to work directly from the warehouse evolved into a broader movement to reimagine how data informs software. That journey gave me a front-row seat to how teams think, build, and scale smarter through integrated experimentation and analytics. Now, as part of LaunchDarkly, we’re scaling that mission to thousands more teams around the world.
I recall a recent conversation with a product leader at Fi Money that perfectly captured this shift. Before adopting a warehouse-native model, their product and data teams would spend days piecing together data from multiple tools just to understand how a new feature performed. “Time to insight” meant navigating through SQL gymnastics, hunting down events across different systems, and syncing Slack threads to make sense of it all. After transitioning to a unified, warehouse-native stack, the picture changed dramatically. Time to insight dropped by 60%, and more importantly, the data became trustworthy, centralized, governed, and readily accessible. That clarity empowered the team to iterate more quickly, make informed decisions, and align more closely around measurable outcomes.
The opportunity today is broader than just new tools. It’s about building systems where delivery and learning happen in the same place. That’s what makes the loop tight and the insights fast.
Legacy Gaps: Why the Old Ways No Longer Work
For years, experimentation and product analytics evolved along separate tracks. Product teams used standalone analytics tools to track user behavior; experimentation was typically handled by purpose-built A/B testing platforms. These systems weren’t designed to communicate with each other or to access the company’s core business data stored in the warehouse.
This created several compounding challenges:
- Siloed Data, Incomplete Insights: It was challenging to understand how a feature influenced key business metrics, such as customer lifetime value or retention, because these metrics weren’t accessible from the experimentation platform.
- Manual Processes, Delayed Feedback: Analysts had to manage pipelines, write custom SQL queries, and wait days or weeks to deliver actionable results.
- Governance and Cost Issues: Moving data in and out of siloed SaaS tools introduced compliance risks and often resulted in duplicative spending.
- Vendor Lock-In: Once teams committed to a proprietary analytics vendor, they were constrained in how and where their data could be used.
As software became more dynamic and user expectations more fluid, this disjointed approach became a liability. Product teams needed a faster, more integrated feedback loop, one rooted in the same infrastructure powering their analytics experience.
I’ve spoken with data teams who’ve spent hours trying to align definitions between tools, only to end up reverting to spreadsheets. I’ve seen PMs copying data from dashboards into Notion just to make sense of what was happening. The friction in legacy systems isn’t just about latency — it’s about trust. If your metrics don’t line up, how can you be confident in your decision-making?
Warehouse-Native: A Better Foundation for Learning
The emergence of warehouse-native analytics and experimentation has fundamentally changed the way product teams make decisions. By transforming the data warehouse from a passive system of record into an active system of engagement, organizations can now run experiments directly on governed, centralized data.
In practice, this means experiment assignment logic, user event streams, and downstream KPIs all live within the same compute and storage layer — often Snowflake or Databricks. Because the schema is centralized, teams can use shared identifiers to join feature flags, cohort definitions, and business outcomes in SQL.
This approach eliminates the fragmentation that once plagued analytics workflows, bringing experimentation closer to the source of truth.
With experimentation and analysis happening on the same platform, teams avoid the version mismatches and context gaps that occur when data is scattered across tools. Real-time event streaming into the warehouse enables product teams to observe results as they unfold, providing faster and more actionable insights. The influx of critical business signals — from Salesforce, NetSuite, Gong, Marketo, and others — has transformed the warehouse from a passive store of data into an active runtime layer. It’s now the canonical source powering experimentation, cohorting, and downstream decision-making across the org.
Crucially, this approach also enhances governance and security. Sensitive data remains within the organization’s existing infrastructure, ensuring compliance while eliminating the need for risky exports or duplicative pipelines. And thanks to the rise of no-code and composable interfaces, even non-technical stakeholders, from product managers to analysts, can self-serve insights and iterate on experiments without relying heavily on engineering.
This shift isn’t just about better tooling. It represents a new operating model, one in which the data warehouse becomes the analytical heart of product development.
Real-Time Feedback Loops: The Future of Product Workflows
What makes the convergence of experimentation and analytics especially powerful is its impact on velocity. When teams can move seamlessly from insight delivery, the feedback loop tightens, turning software development into a continuous learning process.
Instead of waiting days for experimental results, data now flows into the warehouse in real-time, giving product teams immediate visibility into performance. That immediacy allows for faster hypothesis cycles, where each result directly informs the next iteration. The shared foundation between product analytics and business intelligence also fosters deeper collaboration between teams, ensuring alignment on metrics and goals from the start.
I’ve worked with teams who, after integrating analytics with experimentation, discovered that the features they thought were “winning” were underperforming for key segments. When you shorten the time between signal and decision, the result isn’t just speed — it’s better product judgment.
The result is a system that supports broader experimentation with less overhead. When insights flow fast and infrastructure scales with you, product teams aren’t limited to a handful of quarterly A/B tests. They can test continuously, across features, user segments, and experiences, and use that feedback to adapt on the fly. It’s a fundamentally different way of working, one that prioritizes learning over perfection and speed over certainty.
A New Kind of Stack
One real-world example of this convergence is the integration between feature management platforms and warehouse-native analytics. LaunchDarkly’s acquisition of Houseware, the startup I co-founded, which is focused on product analytics directly on Snowflake, reflects this broader industry movement.
Our vision at Houseware has always been to “flip the data warehouse from passive storage to an active decision engine” (that’s why we named it Houseware — a literal flip of warehouse). LaunchDarkly, with its robust experimentation and feature flagging capabilities, provides the event layer that feeds that engine. Together, they’re creating a system where product decisions can be made and adjusted based on direct insight into user behavior. I have seen this in action where customers like Fi Money observed a 60% decrease in time to insight for new feature metrics. Transparent, predictable computing costs also gave the team confidence to reinvest in their new stack and scale adoption more aggressively.
This kind of integrated architecture enables organizations not only to deploy faster but also to adapt more quickly. It’s a foundation for smarter, more responsive software.
The convergence of experimentation and analytics doesn’t just make product teams more efficient; it opens the door to autonomous optimization. As systems become more data-aware, opportunities emerge for real-time rollbacks, automated flag cleanups, and even AI-driven feature tuning based on continuous performance signals.
For technology leaders, this is about more than agility. It’s about resilience. Software that adapts itself based on live data doesn’t just ship faster, it fails less.
The convergence of experimentation and analytics, underpinned by warehouse-native infrastructure, represents a new strategic lever for product and engineering leadership. It enables a culture where every feature is an opportunity to learn, every metric is traceable to business value, and every system is designed to respond, not just run.
This is the new frontier of software development: learning loops built directly into the stack. And it’s already happening.
The owner of TNS, Insight Partners, also invests in LaunchDarkly. As a result, LaunchDarkly receives preference as a contributor.
