What is AIOps?
You’ve probably heard this term used over and over again as the next big thing in IT management. And maybe you’ve read something about it having a significant impact on system operations and administration in numerous trade publications. But what exactly is Artificial Intelligence for IT operations (AIOps)? And why should you care about it?
AIOps is a term first coined by Gartner. It is the application of advanced analytics—in the form of machine learning (ML) and artificial intelligence (AI), towards automating operations so that your ITOps team can move at the speed that your business expects today.
AIOps marries big data with ML to create predictive outcomes that help drive faster root-cause analysis (RCA) and accelerate mean time to repair (MTTR). By providing intelligent, actionable insights that drive a higher level of automation and collaboration, your ITOps can continuously improve, saving your organization time and resources in the process.
Why is AIOps important?
A successful digital transformation requires AIOps. And the push for business agility leaves an undesirable by-product in complexity—making it extremely difficult for humans to keep up. While agility is core to business innovation and customer experiences, executing to it has created a highly ephemeral state of IT workloads and processes.
Major advances in distributed architectures, multi-clouds, containers, and microservices, to name a few, have created copious, multi-dimensional data flows that create excessive noise and stifle IT’s ability to identify and resolve service incidents.
Why You Need AIOps Now More Than Ever
Because there are so many different layers of technologies making up your IT infrastructure, there are an increasingly complex set of dependencies between these technologies. Adding to the complexity, your IT infrastructure is shared across an ever-expanding set of business services and applications. Any type of change to one of these services, applications, or the underlying infrastructure occurs so fast and frequently that we are beyond the point where humans can figure out how these parts are related. We need a machine to do it for us.
AIOps builds real-time systems in the form of context-rich data lakes that traverse the full application stack in order to reduce noise in modern performance and fault management systems and drive automation—with the ultimate goal of improving time to resolution.
Digital Transformation in Action
One of the most common questions we hear from customers and prospects is … “How does AI/ML factor into your AIOps platform? And how can I tap into AI/ML with ScienceLogic SL1?”
Watch this webinar to learn how ScienceLogic leverages machine learning to isolate meaningful insights today and build a foundation for enhanced service-level monitoring tomorrow.
Your Guide to Getting Started with AIOps
When there’s lots of buzz around new technology, it’s easy to suspect there’s a lot of buzz and hype to follow. And often you’d be right, but with some effort, you can find the truth. It’s no different with the buzz around AIOps, but knowing what’s what and where to start can be quite the effort.
Buzzwords aside, it looks like Artificial Intelligence for IT Operations (AIOps) is going to be a permanent fixture. In fact, a recent study by Forrester states that “68% of companies surveyed are actively investing in AIOps-enabled performance monitoring solutions within the next 12 months.”
A Journey to Fully Automated Operations
Ask yourself these questions:
- Is your organization ready to migrate to the cloud?
- Do you need large capacity scalability?
- Do you need help growing IT to support the variety of tasks essential to the success of your organization’s goals?
- Does your future include automation and a need for smart, predictive analytics?
If you answer yes to the following questions, you are ready for AIOps to help you meet the challenges of today and the challenges you may face in the future.
The final goal for enterprises is a system that automatically predicts and addresses operational disturbances before they arise. Your system should then make recommendations or advise on the next steps, and an operator can then make more informed decisions. The AIOps journey is not for the faint of heart, but the results are well worth the investment over time. And every journey begins with a single step. At ScienceLogic, we have created a maturity model to help our customers and partners think through their current starting point on their journey. And we’re here to help you each step of the way.
Context-infused AIOps brings meaning to your data.
A new approach to IT operations and operations management is needed–one that works at machine speed. But to transform operations, IT leaders must commit not only to collecting data but to enriching data quality with context, enabling automated outcomes.
So, the central challenge of AIOps is: How do you collect, consolidate, and contextualize data–in real-time–so that it becomes actionable?
5 Steps Towards Actionable, IT Operational Data
Step 1 - Collect
Data collection includes the initial and continuing discovery of data from various sources–including agents, devices, applications, and services–while the collection process itself should match the type of asset being monitored. AIOps demands continuous knowledge of the current state and health of the IT environment.
Step 2 - Consolidate
There are multiple aspects to data cleansing and consolidating, including a common data model, data deduplication, time synchronization, and a single data lake. AIOps cannot succeed if data is incomplete, imprecise, or out of alignment.
Step 3 - Contextualize
The most critical element of data enrichment for AIOps is context. Because context brings additional insight to raw data by adding meta-data related to your device or service metrics, infrastructure, application, and business service mapping should be in place in order to be successful.
Step 4 - Advise
The vast amounts of operational data collected by IT management systems place a significant burden on operations teams and incur significant analysis costs, in terms of staffing, compute, and storage. AIOps applies machine learning to solve problems–rapidly eliminating and consolidating data where possible.
Step 5 - Autonomize
Once data is collected and organized with context, decisions can be made with real insight, based on timely and accurate data. Automated actions can be initiated to make changes, recommendations, or notifications to ecosystem components or users. AIOps empowers automation made possible by context.
The ScienceLogic SL1 AIOps Platform
Get visibility, context, and action across your entire IT operations to maximize business performance.
Here’s how we are helping Hughes
Watch how ScienceLogic SL1 boils down thousands of systems and applications, forty gateways, two million clients and ten satellites into overall service status views with real world actionable information for one of the world’s largest satellite service providers.
Hundreds of customers already trust ScienceLogic.
Increased visibility from 30% to 100% by replacing 7 legacy tools with a single platform; establishing a firm foundation for their journey to AIOps
Decreased incident resolution time by 21%; 60% reduction in SLA breaching incidents
Consolidated 33 tools down to 6 to increase IT efficiency, automation, and focus on creating “positively outrageous” customer experience
Satisfy modern consumer expectations by supporting legacy IT while rapidly deploying new technology
Maintain visibility into shifting landscape with 100+ applications across private and public cloud
What Customers and Experts are Saying
“For AIOps to succeed, real-time data – delivered with context – is a mandatory requirement and will be the only sound basis for advanced automation and machine learning to be successfully adopted in the enterprise.”
“If I were to pinpoint one of the things Opus is most proud of with the ScienceLogic relationship is the ability to up level our operational maturity with automation, and by continuing to extend the SL1 platform's capability, observability, and AIOps across a wide variety of technologies.”
Jeremy Sherwood, Head of Products, Opus Interactive
“When we started on our AIOps journey, we noticed there were quite a few redundancies that were creating extra noise, extra work and inefficiencies across our business. We made bold changes throughout the organization that set the way on our journey to automate and virtualize our NOC with SL1 in the cloud.”
Chris Ruffieux, VP Architecture, Gannett/USA Today Network
“We found ScienceLogic suits us and helps us in achieving our AIOps journey and is highly collaborative by nature. They go out of their way to support us on this difficult journey. ScienceLogic brought the right consultants to support us to design the solution, to implement the solution, and realize the outcome for us. With ScienceLogic, we're well positioned to build a better future infrastructure monitoring and related solution.”
Chellanamasivayam Murugiah, Executive VP & Head of Global Infrastructure Services, Capgemini
“When we think about AIOps, our mission is to ensure availability of our applications and services so that they are available for our workforce and direct to veterans. We do that by improving our response time, quickly finding the root cause and resolving faster, with as much automation as possible, which again speeds our response.”
Dave Catanoso, CIO, Dept of Veterans Affairs
“We develop our technologies to become the essential infrastructure provider for our enterprise customers. And we can't do that alone. ScienceLogic has been a really important partner to us to help bring the insight and operational controls and to drive components of our automation story. We are better together.”
Peter Lacoste, Senior Vice President, Dell Technologies