Navigating the Doubtful Truths of Dashboards and Discerning the Real Potential of AI Agents

Top enterprises are drowning in a tsunami of data. Investments on building data infrastructure, warehouses, real-time pipelines, machine learning platforms have turned into the norm rather than the exception. But with growing emphasis on data-driven decisions, conflicting insights from various teams are creating a trust issue that’s leading to challenges in decision making and abandoned dashboards. Enter the caveats of ‘data-as-a-service’ model.

Companies now face a surprising truth: the problem doesn’t lie with data per se, it’s a product think-tank issue. Build it keeping the usability, interpretability and decision-making in mind, and you’re sailing towards successful data interpretation. A new role has thus emerged - the data product manager, designed to navigate this brittle terrain, with a focused approach towards consolidating data insights.

As businesses steer the choppy waters of data analysis and AI implementation, addressing the product-centric approach becomes imperative, along with acknowledging the distinctly ‘bounded’ nature of AI. Real-world use-cases demand balanced reliability and defined problem-solving that can only be achieved by tempering the AI hype with a dose of reality.