The Analyst Trying to Make Enterprise Data Actually Useful

Ask most companies where their data lives and there is a long, uncomfortable pause. It's in the booking system, the finance tool, three spreadsheets, a warehouse nobody has logged into since 2023, and a vendor dashboard someone set up and left. The data exists, but reading it as one picture is the part that breaks. This gap is where Uday Surendra Yandamuri does his work. He's a technology and operations analyst who builds decision-support systems for industries that run on operational data. His latest research, published through IEEE, makes a blunt argument: for most enterprises, the bottleneck was never how much data they had. It was that none of it talked to each other. The paper is available on the IEEE Xplore Digital Library . The problem nobody wants to name In a large operation, it is common to find teams making confident decisions off partial pictures. Not because they're careless, but because the full picture is scattered across systems that were never built to speak to one another. Planning runs on one dataset, inventory on another, service delivery on a third. Each team optimizes its own corner and the whole thing quietly drifts out of sync. Yandamuri's research names this directly. Disconnected data environments limit visibility across departments, and that limited visibility bleeds into forecasting, inventory, service, and just about every decision that depends on knowing what's actually happening right now. When people can only see part of the operation, they plan against part of it. His answer isn't another dashboard bolted onto the pile. It's a cloud-native approach that pulls information from all those scattered sources into one foundation, so decisions get made against the whole operation instead of a slice of it. Why he reaches for the cloud The scalability argument is the practical heart of the paper. Operations grow, data volumes climb, and the old analytical setups start to buckle under the weight. Cloud infrastructure gives an organization room to absorb shifting workloads while still running the heavier machine-learning analysis on top. The move Yandamuri keeps coming back to is mixing historical data with live operational data. History tells the pattern. Live data tells what's happening now. When they are put together, a team can see a trend forming and forecast against it without a person hand-assembling a report every morning. Built for one industry, not all of them The part that separates his work from the usual analytics pitch is his insistence on industry-specific design. A general-purpose platform treats a hotel and a farm the same way. Yandamuri's argument is that they aren't the same, and pretending otherwise is why so many analytics rollouts quietly fail. His systems fold in the metrics, business rules, and performance signals that matter to a particular operation, which makes the output something a manager can actually act on instead of a generic chart. And he's careful about where the line sits. The system doesn't make the call or automate the decision. It hands the decision-maker a clear, structured read and gets out of the way. He frames it as technology strengthening human judgment, not replacing it. That instinct traces back to his own path. His background runs through hospitality technology and agricultural systems, two worlds where timely information changes what happens on the ground, alongside academic training that pairs agricultural science with informatics and business strategy. It's an unusual mix, and it shows up in how he thinks about the problem. Where this goes next Yandamuri also shows up in the wider conversation. He was a speaker at the GatherVerse AI Evolve Summit 2026 . The larger point in his work is one a lot of tech teams are circling right now. The last decade was spent collecting everything. The next one is about making any of it usable. Yandamuri's answer is unglamorous and probably right: stop adding tools, start connecting the existing one, and build the analysis around the specific business instead of the other way around. His research sits in that shift, and it's a shift plenty of organizations are only starting to take seriously. More on IEEE research and technical resources is available through the IEEE Xplore Digital Library . This story was distributed as a release by Jon Stojan under HackerNoon’s Business Blogging Program.