.svg)
The modern data stack promised a revolution: a data renaissance where insights would flow as freely as oil. Billions were poured into data warehouses and tools to manage it all. But the promise of lightning-fast insights and seamless automation has remained elusive, with timelines still measured in weeks or months instead of hours.
Enter Pavleen Thukral, serial entrepreneur and CEO of Magic Data, a company built to untangle custom data engineering challenges in the modern stack. According to Thukral, companies have solved for performance, but faster insights and real automation have still remained largely out of reach.
Performance performs: "We’ve made huge, huge advancements in the performance problem," Thukral says. "You can now meaningfully run queries and store and move really large quantities of data through the modern data stack." But there’s a catch: "Insights and automation still lag. It waits to be seen if we’re going to get amazing time to insights." Performance is no longer the bottleneck, yet speed and clarity remain stubbornly out of reach.
Not over 'til the warehouse sings: Despite years of investment, most CTOs and CEOs still wait days or weeks for insights—not hours. "The types of data intricacies that we’re talking about are in the hundreds or thousands," Thukral says. On top of that, "the change that’s happening in that data warehouse is enormous," often triggered by M&A, legacy knowledge, and duplication. Magic Data was built to tackle this exact mess, to make the warehouse sing and turn insight time from sluggish to snappy.
AI's next act: "Given the data intricacies, we strongly believe in the concept of human in the loop with AI," Thukral says. And looking ahead, Thukral sees AI unlocking new value. "A lot of CEOs and CTOs are waking up to higher-level forms of benchmarking that are now possible with AI," he says. He also expects partnerships to be "massively transformed as a result of the AI transformation," especially through white-labeling of data.
Rethink the rules: As AI and data sharing scale, governance becomes impossible to ignore. "Most data guidelines are written from the perspective of assuming massive human failure," Thukral explains. He pushes for a more proactive model: "If you can plug in not only your data warehouse but your file systems, your spreadsheets, your various different API infrastructure, and then you're able to know what data you have even at big data complexity." In a stack built to move fast and fuel AI, that kind of visibility is what separates real intelligence from raw infrastructure.
