In the first session of the IBM and Bloomberg series “Intelligent Automation: Transformation in a Time of Uncertainty,” Travis Smith, IBM Distinguished Engineer and Head of Data and AI for The Weather Company, discusses the need to bring enterprise observability to their systems so they can see, in real-time, where traffic is occurring across their applications, where they need to put more emphasis and when events could cause downtime.
“Our weather forecasting computes every 15 minutes. We constantly change our forecast in order to make sure you have the right information at your fingertips to help you figure out what you need to do with your life and also keep you safe,” says Smith.
At The Weather Company, thinking like a Chief Automation Officer (CAO) means moving from traditional application monitoring to observability in order to increase resiliency, reduce downtime and maintain the highest quality consumer experience. While monitoring can tell when something’s wrong, observability can tell what’s happening, why it’s happening and how to fix it. Imagine being able to know how everything is performing, everywhere, all at once.
Smith’s advice to leaders looking to ensure a reliable consumer experience is two-fold:
- Adopt automated observability for the deep 360 visibility into your distributed systems: This will allow for faster problem identification and resolution from the automation. Instead of measuring your resiliency in terms of mean time to recovery, use data observability and AI to avoid incidents in the first place. This is critical for IT resilience and for meeting The Weather Company’s goal of providing consumers with reliable access to weather information wherever they are, whenever they need it, despite the increasing volatility of the weather.
- Cultivate a team that’s open to new and emerging technology: In addition to observability, The Weather Company team is investigating different uses of generative AI, such as writing code to increase developer productivity and accelerate the onboarding of talent. They’re also looking at using generative AI to provide smarter messages to consumers. For example, Smith explains how most people may only need a “snow is coming” alert the night before while a single parent may need the same alert earlier so they have more time to prepare.
By adopting the mindset of a CAO, Smith and his team are making the best decisions about where to apply intelligent automation to make IT systems more proactive, their DevOps and QA teams more productive and, ultimately, their consumers more aware and prepared.
