Inside the Rise of Modular Data Lakes That Power Global Fraud Detection, SaaS and Retail Insights

Modular data lakes are revolutionising fraud detection, SaaS, and retail by unifying scattered datasets. Expert Tanvi Kopardekar explains how these systems automate reporting, saving USD 250,000 annually. By integrating AI and ensuring data accuracy, businesses gain a competitive edge, enabling faster decision-making and improved security across global payment platforms and digital service industries.

Across payments, retail, and digital services, businesses now generate massive amounts of data, and making sense of it has become a major task. To make sense of this information, many organizations are turning to modular data lakes.These systems bring scattered data into one place, sort it, and turn it into useful insights. They now play a central role in fraud detection, SaaS operations, and retail decision-making.

Building such systems requires deep technical skill, and one of the professionals leading this work is data specialist Tanvi Kopardekar. Her experience offers a clear look into how data lakes are shaping how businesses operate today.

Kopardekar has worked on several large-scale data lake projects. One of her key achievements was scaling a global payments data lake used across the company. The platform became the main source of analytics for teams. It offered clean, well-governed datasets that were ready for use. Instead of relying on scattered reports, teams could finally see complete revenue flows, understand product performance, and compare results across regions.Speaking about the project, she says, “My goal was always simple—make data easy to use and trust. If people don’t trust the numbers, they won’t use the system.”

Fraud detection was one of the areas that benefited the most. The professional built early-warning rules that flagged risky transactions and triggered extra authentication when needed. These safeguards helped identify unusual patterns faster and reduced potential losses. She also improved bank reconciliation by ensuring payment data was accurate and consistent, which strengthened the organisation’s ability to handle large volumes of financial information. These efforts led to some significantefficiency gains. Manual reporting, which took teams more than 90 hours every week, was automated. The unified data lake also eliminated the need for many separate tools and databases, saving the company nearly $250,000 a year. The specialist explains, “A data lake should help people make decisions, not add more work. Automation and accuracy were non-negotiable for us.”

Additionally, she built a data lake for the global gift card industry, giving customers a clearer view of their transaction costs, ROI, and fraud risks. This gave businesses worldwide insights they didn’t have before. In another role, she developed a data lake for an agentic project that combined data from chatbots and voicebots. By organizing and modeling these interactions, the system supported everything from insights gathering to recommendation tools. It became an important part of customer service and operations.

However. these successes were not without challenges. Kopardekar had to collect data from many locations, clean it, and make sure it fit together correctly. She separated master data from transactional data, linked them properly, and built models that allowed users to filter and analyze metrics easily. She also controlled infrastructure costs through techniques like clustering, partitioning, and strict data quality checks.

Reflecting on her experience, she notes, “A data lake should never be a dumping ground. Freshness, completeness, and accuracy matter every single day.”She believes the future of data lakes will involve closer collaboration between human expertise and AI systems. In her view, AI and agentic tools will help speed up analysis, but human oversight will continue to shape the quality and direction of the data.

Lastly, as modular data lakes gain ground, one thing is clear, which is that companies using data effectively gain an edge. They can detect fraud faster, support SaaS platforms more efficiently, and understand retail trends with more clarity. The real strength of these systems lies in their ability to turn raw information into insights that help businesses act with confidence.