“When digital transformation is done right, it’s like a caterpillar turning into a butterfly, but when done wrong, all you have is a really fast caterpillar.”
George Westerman, Research Scientist with the MIT Sloan Initiative on the Digital Economy.
Do your digital transformation initiatives feel like a fast caterpillar instead of the promised butterfly? If yes, you need to rethink legacy IT management techniques to keep up with competing technological and business demands. This requires IT operations teams to evolve and embrace new strategies to handle digitalization issues better. Artificial intelligence for IT operations (AIOps) has emerged as a universal remedy, but is it all that it’s cracked up to be?
Well, yes, we think it is.
It’s all that and much more. AIOps brings the power of analytics, automation, and machine learning (ML) to glean insights from data and proactively remedy issues, automate manual and repetitive tasks while ensuring minimal disruptions to business-critical services. Every IT leader’s dream come true.
Managing the mess of hybrid and multi-cloud
Today’s enterprises face a multitude of obstacles when it comes to managing hybrid and multi-cloud infrastructure and operations. These could range from cloud assessments and migrations, optimizing distributed IT across cloud and legacy datacenter resources or enabling a dynamic and DevOps-driven organization culture.
The new world of hybrid IT is more complex than ever. The proliferation of cloud and new cloud-native services demand highly specialized skills. DevOps and IT decentralization are also adding fuel to this fire by increasing costs, enabling shadow and fragmented IT, and limiting tight governance. IT was traditionally relegated to managing and maintaining resources, such as servers, networks, storage, and data, but we see a paradigm shift taking place. IT is becoming a service provider to the business, entrusted to manage critical services and workloads. So, where does IT go from here?
Emergence of AIOps
To combat these challenges, businesses are exploring AIOps platforms which can enrich traditional cloud operations by ingesting data from multiple distributed sources. Leveraging automation, AI, and insights vetted by skilled analysts, can provide intelligence for faster resolutions. Here are 7 ways AIOps can redefine hybrid cloud monitoring and management for your business:
1.Speed up problem identification & resolution
Get the power to integrate historical and real-time data across incident, problem, and change management functions. AIOps delivers comprehensive visibility into private, hybrid, and public cloud resources and drives analytics to detect and solve problems faster.
How does that happen? Machine learning helps reduce event noise and provide service health insights. Integrations into existing problem identification and resolution workflows drive better event suppression. AIOps platforms can combine this with contextual insights on incident and problem data to analyze data at scale, find potential problems proactively, and speed up root cause analysis. Everybody wins!
2.Increase savings on cloud costs
Cloud costs are a constant headache for IT staff. Effectively managing them can be challenging due to lack of skills, unoptimized resources, dynamic provisioning, tool sprawl, and auto-scaling capabilities. When different teams use cloud platforms like AWS, Azure, and Google for their requirements, it becomes difficult for enterprise IT to keep track of spend or ensure centralized governance through approvals.
AIOps-powered solutions can detect abnormal spikes in costs, provide detailed cost insights, deploy automation to minimize unused resources, and automatically right-size servers or storage in the cloud.
