Reducing Decision Fatigue at Petabyte Scale
Discover how organizations reduce decision fatigue at petabyte scale by using automation, data intelligence, and scalable decision-making frameworks.
As organizations expand their analytics capabilities into the petabyte range, the limiting factor is no longer storage or compute power. Increasingly, enterprises are finding that human decision-making itself has become the bottleneck.
In large analytics environments, leaders and analysts rely on multiple screens, applications, and data systems at once. While data refreshes in real time, keeping views aligned across systems often requires users to repeat the same actions, setting filters, synchronizing time ranges, and reconciling inconsistencies across tools. The result is slower decisions, higher error rates, and growing decision fatigue.
Addressing this overlooked challenge, Nandakishore Leburu, a Lead Engineer working within Walmart Labs, led the development of a software-driven system designed to reduce interaction friction in large-scale analytics environments.
An Invisible but Costly Coordination Problem
In complex analytics hubs, different displays and applications often handle different datasets or functions. When users adjust filters on one system, they must frequently replicate those changes across others. Over time, this repetitive coordination becomes a significant drag on productivity and decision quality.
“This wasn’t a data volume issue,” Leburu explained. “It was a coordination problem of how people interact with multiple systems when decisions have to be made quickly.”
While hardware-based synchronization approaches were available, their cost and rigidity limited their practicality in real-world environments.
A Software-First Approach to Synchronized Decision-Making
Rather than relying on specialized hardware, Leburu originated a software-first architecture that enables real-time sharing of user interactions across independent systems.
When a filter or view is adjusted on one display, the change is propagated instantly and securely to other displays. Each application remains independently scalable, while collectively behaving as a synchronized decision environment. The result is a unified experience without forcing users to repeat actions across systems.
The work was carried out as part of the Data Café, an internal analytics initiative focused on enabling real-time, multi-source decision-making at scale.
From Prototype to Production
After initial prototyping in 2016, the system was deployed into production analytics environments in 2017, supporting high-velocity data streams and time-critical decision workflows.
The development and deployment were led from Walmart Labs’ engineering organization in India, where Leburu originated the software-based solution to a problem articulated within the broader analytics architecture by a U.S.-based architect, supporting production environments used across multiple regions.
By removing repetitive interaction steps, teams were able to focus on analysis and action rather than system alignment reducing decision latency and minimizing human error.
Recognition Through a Published U.S. Patent
The architectural approach has been formally disclosed in a published U.S. patent application (June 2018), recognizing the originality of the system and placing it in the public technical record.
The patent describes systems and methods for multi-modal synchronization and interaction across distributed applications and devices, with applicability extending beyond analytics into any environment where coordinated decision-making across multiple systems is required.
Why It Matters
As organizations increasingly rely on real-time data, improving infrastructure alone is no longer sufficient. The ability to reduce cognitive load and interaction overhead has become just as critical to effective decision-making.
Leburu’s work highlights a shift in large-scale system design, one that treats human interaction as a first-class architectural concern. By focusing on how people engage with complex systems, rather than only on raw data throughput, the approach opens new possibilities for faster, more reliable decisions across industries.
