While great strides have been made in the adoption of real-time analytics in the marketplace, artificial intelligence could ramp this up.
We’ve come a long way with analytics in recent years, in which data is applied against algorithms or analytics engines to determine what it may mean to the business.
Lately, there’s been a lot of progress with real-time analytics, especially when applied against streaming data from systems or devices. But with artificial intelligence coming into the picture, we ain’t seen nothing yet.
That’s the word from a group of McKinsey Global Institute analysts, led by Michael Chui, who connected the dots between AI and hundreds of use cases from across 20 industries in a recent study. Notably, they observe, the most value coming from AI, as indicated by more than two-thirds of projects studied (69%), are in improving the performance of existing analytics efforts. For purposes of clarity, the analysts define AI as “Deep learning techniques using artificial neural networks.”
It’s significant that every industry is finding a way to benefit from AI-driven analytics because the potential case studies vary considerably. A manufacturer may be concerned with syncing its production-floor machines with its supply chain, while a retailer may want to know what customers are using which channels, and healthcare establishment may be concentrating on better ways to track patients’ vital signs remotely. Recognizing the wide variety of use cases for real-time analytics and operations, RTInsights maintains a library of case studies across a number of major industries. Every company has a different story to tell, and different ways of innovating.
When cognitive computing technologies such as AI are applied to enhance real-time analytics, the innovation explodes. Chui and his McKinsey team describe the following key applications arising from the intersection of AI and analytics:
Predictive maintenance. AI is being trained to detect a wide range of anomalies. “Deep learning’s capacity to analyze very large amounts of high dimensional data can take existing preventive maintenance systems to a new level,” Chui and his co-researchers observe.
