According to the Forrester Survey, “The State of IT Operations Management,” 68% of decision makers cite speed and agility as the top drivers for change in IT operations. This drive for change is causing a shift towards the modernization of ITOps management and monitoring tools. So, why is this?
As companies shift toward hybrid cloud and modern app architectures such as containerized microservices and serverless—their environments become more complex. This complexity challenges most organizations, whether they are modernizing their tools or not. But IT complexity is compounded with the use of 10 or more infrastructure and application monitoring tools, and according to the Forrester survey, 33% of our nation’s largest enterprises use more than 20, significantly hindering agility and visibility.
Another issue hindering this shift? The management of massive amounts of IT data. This is driven by the three Vs of data:
- Volume: The amount of data generated by IT infrastructure and applications is increasing annually at a rate of 2 to 3x, according to Gartner.
- Velocity: The accelerating pace of change is being felt by both application and infrastructure teams. App teams are constantly delivering, integrating, and pushing out changes that affect the application and its composition. Simultaneously, the infrastructure that the app runs on is changing –VMs and containers are spinning up or down, and the software-defined network is reconfiguring on the fly. The demand for app and infrastructure teams to absorb quick changes is now measured in milliseconds, if not faster.
- Variety: Consider the collection of technology that exists today: there’s multi-cloud, private and public cloud, different generations of technology, traditional data centers, software-defined data centers – the list seems infinite. Now consider that a typical business service is comprised of many different applications that run in different environments on different generations of technology with different code—even via partners and third-party providers,, but they all need to work together.
This fast growth in data requires a new set of capabilities:
- Business service visibility across domains
- Massively scalable architecture to manage and process the data
- Support for modern technologies (cloud, containerized microservices, etc.)
- Automation to keep up with the ever-increasing workload
What provides these capabilities to deal with all of this data? Artificial Intelligence for IT Operations (AIOps). According to Forrester, 68% of organizations are moving to AIOps within the next 12 months. And 82% are expanding their monitoring to combine infrastructure and apps within the same 12 months. An AIOps-enabled monitoring solution provides ITOps teams with these capabilities by consolidating and minimizing toolsets and creating a viable environment for expanding AI/automation usage.
So, what do you need to make this transition to AIOps? A vast majority think that AIOps is only about machine learning and artificial intelligence. In addition to AI/ML, AIOps involves three primary components:
- Data: Merge and correlate relevant IT data from across multiple sources.
- Analysis: Derive relationships, bubble up insights.
- Automation: Execute tasks needed to restore service or remediate issues.
A modern AIOps-enabled monitoring solution like the ScienceLogic SL1 platform helps you achieve speed and agility —unlike the paralyzing nature of legacy tools. AIOps enables your ITOps teams to focus on more innovative advancements—propelling digital transformation. We have an AIOps maturity model that can guide you on your journey to AIOps. Discover where you are in the maturity model and the steps to get there. If you haven’t started your journey, don’t be left behind.
