Product Business Analyst

Clearwater Analytics reported strong third-quarter 2025 results, with revenues reaching $205.1 million, up 77% year-over-year. Annual recurring revenue grew to $807.5 million, also rising 77% from the previous year. The company delivered adjusted EBITDA of $70.7 million, up from $58.3 million in the second quarter. CEO Sandeep Sahai described the results as "stunning" and highlighted positive feedback from clients, partners and analysts regarding the company's strategy. Clearwater is positioning itself as an investment management platform provider, demonstrating strong business model durability and predictability through its recurring revenue growth.

From the floor at InvestOps 2026: "good tools are table stakes...trusted data is the differentiator" At InvestOps, Clearwater hosted a closed roundtable with AWS that brought together investment operations leaders from across the industry. The agenda was technology and the conversation kept returning to something more fundamental. Before anyone wanted to talk about AI, automation, or infrastructure, the room kept landing on the same problem: data they couldn't fully trust, moving through systems that didn't communicate, maintained by teams spending most of their time cleaning rather than analyzing. For all the investment in data trust technology over the past decade, the foundation underneath most firms' operations was built for another era. What made it striking was hearing it consistently across firms of different sizes, strategies, and sophistication levels in 2026. The problems haven't changed. The cost of not solving them has. The unglamorous bottleneck firms keep finding themselves in. Attendees described a set of problems that will sound familiar to anyone running investment operations today. Validating data across systems that should agree but don't. Accessing historical records back to inception for transparency requests and audits. Spending hours on manual cleanup before any downstream work can begin. The people doing this work aren't the problem. They're experienced, capable professionals and they're burning bandwidth on tasks that keep them from doing more strategic work. The problem is the infrastructure underneath them: systems that were reasonable decisions a decade ago but were never designed to work together, and were certainly never built for what's being asked of them now. In an environment where data governance in finance has become a board-level conversation, most firms are still managing it at the spreadsheet level. The compounding effect is what makes it serious. When the data foundation isn't solid, every process built on top of it - reporting, analytics, compliance, decision-making - is carrying hidden risk. The kind that surfaces at the worst possible moment. Private credit is accelerating the problem. A significant portion of the discussion focused on private credit and alternatives, which tracks with where allocations have been going. As private market exposure has grown, so has the operational complexity behind it. And most firms' infrastructure hasn't kept pace. The challenge is structural. Private credit workflows weren't designed to slot into systems built for public markets. Deal capture involves unstructured data. Reporting requirements are bespoke. Trade and lifecycle management across complex instruments requires manual intervention at every step, intervention that doesn't scale as AUM grows. Unlike public markets, where data flows are relatively standardized, private credit creates a category of operational problems that compound quietly, including more deals, more counterparties, more customized reporting, and more exceptions to handle by hand. The operational cost is often invisible, until it isn't. By the time it surfaces as a problem, it's usually already affecting team capacity, reporting accuracy, or both. Why data reconciliation best practices matter. When asked where time actually goes, the answers across the table were nearly identical: * Cleaning and normalizing data before it can be used * Moving information between systems that don't connect * Reconciling outputs across teams before anyone can act * Manually building reports that should be automated The firms gaining the most ground have made data reconciliation best practices a core part of how their workflows are designed. Firms want fewer handoffs, with a model where data, workflows, and reporting live in the same environment rather than being passed between platforms that were never designed to work together. Before AI can deliver, data trust technology comes first. Every firm at the table was using AI in some form to summarize earnings calls, review documentation, answer ad hoc questions. This is useful, but nowhere near the real opportunity. The more meaningful application is embedding AI directly into operational workflows. An AI that flags a reconciliation issue before it becomes a problem. That automates repetitive tasks without waiting to be asked. That surfaces what matters without someone having to go looking for it. Getting there requires something most firms don't yet have: a data foundation clean and unified enough to support it. The firms making real progress on AI are investing in data trust technology that makes these models reliable. That's the prerequisite that kept surfacing, and it's why the AI conversation and the data conversation are ultimately the same conversation. You can't build intelligent automation on a fragmented foundation. Firms investing in AI without first addressing data quality are, at best, making their existing inefficiencies faster. The firms moving forward are consolidating - building coherent data foundations, connecting front-to-back workflows, and deploying AI where it can actually change outcomes. The blueprint is straightforward: trusted data, connected workflows, and AI that works because the foundation beneath it actually holds. The firms moving in that direction are finding that the operational leverage is significant, and that it compounds. What this means if you weren't in the room. The conversation at this roundtable wasn't unique to the firms in attendance. It's the conversation happening across the industry. The questions worth taking back to your own team include: * What percentage of your operations team's time goes to data cleanup versus actual analysis? * How many systems does a piece of data touch before it becomes a report? * Where would a reconciliation error most likely go undetected - and for how long? * Is your current infrastructure built for the private credit exposure you have today, or the one you had five years ago? When Clearwater Analytics, Ltd put the AI question directly to InvestOps attendees - where would AI have the biggest impact on investment operations today - 53% said automating manual workflows. Only 11% said managing alternative assets. The room was thinking about the repetitive, manual work that consumes their teams every day. That's where the pressure is and that's where the opportunity lies. The operational model described at InvestOps - fewer handoffs, trusted data, AI that actually delivers - is what Clearwater was built for. If any of this reflects your current environment, it's worth a real conversation about what a more integrated model could look like for your team.