Most data teams nail the infrastructure—pipelines, schemas, dashboards—and then stall. They crank out reports and close tickets, but they never ask whether anyone actually uses those dashboards to make decisions. One audit found 200 dashboards behind solid data pipelines; stakeholders opened only ten of them before acting. The rest were accurate but irrelevant, because there was no shared definition of success or linking work back to real decisions. To break that cycle, Goutham Budati proposes the Data-Perspective-Action framework. The first layer is familiar: build reliable, trustworthy data systems. The second, Perspective, is the interpretive layer where teams add context, surface key metrics, and recommend next steps. With AI tools automating much of the plumbing, Perspective is the irreplaceable edge. Bean’s AI & Data Leadership survey shows 93 percent of leaders cite culture and change management—not technology—as the top barrier to data-driven decisions. Netflix’s Mick Dreeling calls this “golden agents”: as stakeholders query AI, accountability falls on data teams to guarantee accuracy and relevance. Most professionals shy away from Perspective work, mistaking objectivity for passivity. They deliver raw analysis and leave it to busy executives to connect the dots. That hands off the toughest interpretive job to people with limited time and context. By skipping that layer, teams build more volume but less impact—and become easy to automate. Those who develop a systematic habit of adding perspective increase their value, while those who stay buried in pipelines risk redundancy.