Over the last few decades, the fields of Machine Learning and Artificial Intelligence have evolved from their basis in statistics and simple Neural Networks into complex decision-making systems. When I was in grad school studying for my PhD in Neural Networks, the fact that we could get a backpropagation network to classify complex pictures seemed like magic! Over my last few companies, I’ve witnessed this transformation into more and more complex applications. Today, we rely on AI to perform complex tasks, produce complex content, make crucial decisions, and even predict the future.
Despite these advances, a challenge persists: producing insights without actionable steps. In the business world, this is not just a minor inconvenience — it’s the chasm between data and decision-making that we need to cross (with apologies to Geoffrey A. Moore!)
Imagine a chef presented with a plate of raw ingredients but no recipe. They recognize onions, tomatoes, and beef, but are they making a stew, a stir-fry, or a salad? Similarly, having analytics without actionable insights is like having puzzle pieces without the box’s cover picture.
Drawing another analogy, consider the case of a pilot. While cruising at 35,000 feet, alarms start blaring, indicating an engine fire and a loss in cabin pressure. The analytics are shouting that there’s a problem. But what if there’s no guidance provided on how to address the situation? Thankfully, pilots undergo rigorous training for these scenarios. They don’t just have data; they have action plans. Transpose this analogy into a business setting. Technology and business leaders, like pilots, need more than just alarms. They need insight-driven directions.
Another area where actionable analytics is crucial is in the optimization and management of deployed architectures. It’s one thing to know that there’s latency in your application or that a specific microservice is a bottleneck. But it’s an entirely different ball game to know why it’s happening and how to fix it. It’s ironic that while technology has brought us AI and data-driven decision-making, the people building these technologies rarely use data-driven decisions to build their technology stacks.
In essence, the brilliance of modern AI and Analytics is not just in their ability to inform, but in their potential to guide. As leaders in technology and business, our role is not just to implement these systems but to bridge the gap between information and action. After all, analytics, when used right, should be less of an academic exercise and more of a pathfinder. To use one more analogy, it’s a compass, not just a map. Because, at the end of the day, data without direction is just noise.
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