Octus scaled trusted credit intelligence with Qualytics, saving $200K annually, empowering domain experts, and validating billions of rows without engineering bottlenecks globally.
Feb 5, 2026
4
min read
Table of Contents
The Challenge
With dozens of data products spanning private credit, portfolio analytics, cloud screening, and AI-driven insights, data quality is table stakes to build customer confidence and business value. Expectations around accuracy, consistency, and timeliness are extremely high. As the business scaled to new data product offerings, how data quality was maintained became increasingly expensive.
Before Qualytics, Octus engineers invested significant effort into building and maintaining data quality business logic across millions of rows of data directly in SQL and BI tools. Identifying and validating bad data required custom queries, bespoke logic, and ongoing engineering involvement. This approach worked, but it came at a cost: Highly skilled engineers were spending a growing portion of their time focused on data validation and rule maintenance rather than on higher-value initiatives such as developing new datasets, enhancing existing products, and improving the customer experience.
Additionally, each change to a pipeline or business rule often triggered repetitive, piecemeal QA cycles. Engineering teams had to re‑test data manually to ensure no downstream impact, creating hidden time costs that compounded as the data ecosystem grew.
As Octus continued to expand its product portfolio, this created a prioritization challenge between maintaining rigorous data quality standards and delivering new capabilities to customers at speed. Traditional warehouses and visualization tools can run custom queries, but data quality isn’t their job. Making data quality itself efficient and scalable needed to be a core capability. The goal was to preserve data quality while allowing engineering teams to focus on the work that most directly impacted customers.
The Solution
Octus selected Qualytics as its data quality platform to operationalize the practices it already had in place without requiring constant engineering intervention.
Because most Octus datasets live in Snowflake, the ability to point Qualytics directly at those environments was critical. Qualytics integrates cleanly into the existing data architecture, allowing Octus to profile, validate, and monitor data where it already lives rather than introducing new pipelines or workflows.
Qualytics fundamentally changed how data quality work gets done at Octus. Data analysts can build complex computed tables directly in Qualytics, without waiting for data engineers to create views in Snowflake. This enabled a shift from manual, point-in-time spot checks to a true set-and-forget model, where critical datasets are audited continuously—multiple times per day when needed.
That same model dramatically improved speed and responsiveness. New data sources can be onboarded in minutes rather than days, allowing the business to pivot quickly as new data needs emerge. Qualytics runs dozens of complex checks across very large datasets in minutes, giving teams confidence in their data as volumes and products grow. Teams can also choose how audits run for control over performance and cost, like scanning only fresh or recently changed data for efficiency, or executing full audits when comprehensive validation is required.
Just as important, Qualytics shifted ownership of data quality closer to the people who understand the data best. Its low/no-code interface allows technical business analysts to define, maintain, and refine quality rules themselves, while engineering teams retain visibility and governance. As business logic evolves, checks can be easily tuned based on stakeholder feedback, allowing data quality to evolve alongside the products it supports.
Together, these changes made data quality continuous, scalable, and shared without increasing engineering overhead.
“Qualytics has enabled us to go faster and do much more than we imagined,” said Vishal Saxena, CTO at Octus. “When you can build faster, you can do so much more for the business.”
The Results
With Qualytics in place, Octus preserved its rigorous approach to data quality while significantly improving efficiency, confidence, and scalability across the organization.
“The biggest change has been how confident the business is in using our data. Our analysts and product teams don’t hesitate anymore. They know the data is being continuously validated, so they can move faster without second-guessing every number,” said Chris Benedict, Senior Director, Data Product Owner at Octus.
- Engineering time reclaimed for product innovation: Engineers no longer spend time authoring, maintaining, or re-testing data quality rules. Previously, Octus estimated that writing and maintaining data quality rules for new product offerings required 3–6 months of full-time engineering effort upfront, followed by roughly 50% of an engineer’s time on an ongoing basis. Qualytics eliminated that workload, translating into an estimated $200,000+ per year in engineering cost savings and freeing teams to focus on building new datasets, enhancing existing products, and delivering features that directly impact customers.
“Without Qualytics, maintaining the same data quality standards would require dedicating an engineer roughly half-time solely to writing and maintaining rules. With Qualytics, that effort is absorbed into a scalable system that supports growth without increasing engineering load,” said Vishal Saxena.
- Reduced repetitive QA burden: Because data quality checks are centralized, automated, and continuously running, pipeline and logic changes no longer require manual, piecemeal regression testing. Existing rules immediately validate new changes, reducing QA effort, minimizing rework, and lowering release friction across teams.
- Expanded ownership without added risk: Non-technical power users actively work in Qualytics today, writing, validating, and monitoring business logic. This reduces handoffs between teams while keeping engineers out of day-to-day quality management, without sacrificing oversight or control.
- Proven scale across the data ecosystem: Across Octus’ data environment, teams have created approximately 80 customized computed tables and more than 450 authored data quality checks, covering over 6.3 billion rows of data. This breadth of coverage would have been impractical to build, maintain, and validate manually, and it supports continued growth across Octus’ expanding portfolio of data products.
“Qualytics changed how quickly our business can respond to new data needs. Analysts can build and evolve complex logic themselves, without waiting on engineering, and the data is continuously audited in the background. We’re running dozens of complex checks across millions of rows in minutes, ensuring data quality doesn't become a bottleneck,” said Vishal.
Looking Ahead
As Octus continues to expand its data products and invest in AI-driven capabilities, data quality will remain a strategic priority. The difference now is efficiency.
The team views data quality investment similarly to cybersecurity: it’s essential, ongoing, and foundational to scale. Qualytics allows Octus to uphold that standard without constraining engineering velocity—an increasingly important balance as the company grows.
