The latest buzzword in the world of artificial intelligence is AIOps Platforms for optimizing IT operations. AIOps is an offshoot of concepts like DevOps and FinOps and is a more holistic discipline to deal with sprawling multi-cloud systems and operations.
IT executives in large enterprises with complex IT landscapes should consider dipping their toes into AIOps and build up the capabilities to a state of continuous monitoring (a la Fitbit) and self-healing.
AIOps for optimizing IT operations is a discipline and application of machine learning. While machine learning has existed for decades, the rapid advances in bandwidth, cloud compute, and storage and the resulting decline in costs have been the catalysts for a renaissance in machine learning.
The recent shift to cloud infrastructure has also brought about a new way of doing things and a new wave of tools and techniques to facilitate the work. For example, from siloed applications to API-driven functionality, from monolithic systems to microservices, traditional waterfall to agile, and from quarterly or annual big bang releases to DevOps encompassing continuous integration and continuous deployment have become commonplace across enterprise IT.
What are AIOps Platforms? A practical definition of AIOps
AIOps is applying machine learning models and data science algorithms to voluminous data to automate, monitor, measure, and manage IT Operations. AIOPs models can ingest, synthesize, and make sense of massive amounts of network, application log, and machine data to identify patterns and predict implications and impact and forecast future possibilities. The nirvana in AIOps is the ability to automate, monitor, measure, and improve and self-heal the problem areas for continuous improvement and increasing robustness.
AIOps enables IT staff to be more efficient and focus on decision-making rather than look for patterns that most humans may miss.
The Genesis of the Term AIOps:
Gartner, the research and analyst firm, is credited with coining the term AIOps in a 2016 Market Guide. According to Gartner, AIOps is the “software systems that combine big data and artificial intelligence (AI) or machine learning functionality to enhance and partially replace a broad range of IT operations processes and tasks, including availability and performance monitoring, event correlation and analysis, IT service management and automation.”
How AIOps Platforms typically work?
At the heart of AIOps platforms are the consumption, analysis, pattern recognition, and predictions enabled by big data
AIOps starts with Aggregating Data from Multiple Sources:
The data is diverse in volume, velocity, and variety. It includes data emanating from application logs, business activity monitoring tools, machine data, network data, logs of other measurement and monitoring tools. Data is the lifeblood of the AIOps systems. Some data may only be machine-readable, and some data may be unstructured.
Bringing together all the data is the first big step in facilitating AIOps.
Analyze and Identify Patterns:
The AIOps platforms then apply advanced data science, statistical, and machine learning models to correlate vast amounts of data and discern patterns from the synthesis of disparate data. The analytics result in identifying problem areas and potential fixes and predictions for such future events and eventualities.
Manage and Automate Fixes:
In addition to populating dashboards and generating alerts, the AIOps algorithms can find and apply automated solutions to resolve the problems. Furthermore, the growing knowledgebase will help the systems predict future failures and allow for self-healing, including adjustments before problems occur.
Where and How should IT Leaders Leverage AIOPs?
Enterprise technology leaders should start with a set of goals rather than treat AIOps as another shiny object. To avoid the typical challenge in enterprises where new technologies are always searching for use cases, it is prudent for IT leaders to start with outcomes and then use cases and then bring in the appropriate machine learning platforms to run AIOps.
Potential Goals and Outcomes in terms of IT KPIs:
- SLA – Stabilize Availability and Uptime over SLAs
- MTBF – Increasing mean time between failures
- MTTD – Decreasing mean time to detect
- MTTI – Decreasing mean time to investigate
- MTTR – Decreasing mean time to resolution
The typical Value Points for AIOps in the Enterprise IT life Cycle:
- Aggregate all the data from measurement and monitoring tools and applications into one holistic data set
- Analyze the performance of Infrastructure, Network, and Applications leveraging machine learning models and data science algorithms
- Detect anomalies and problem areas, including all outliers and potential black swan events
- Make sense of data from causation and correlation analysis and find predictable patterns
- Automate Responses including introducing the concept of Self Healing where machine learning takes care of the machines
- Providing predictive analytics and recommendations for the future of operations to fine-tune the IT infrastructure, systems, network, and operations in near-real-time. Consider this as autonomous automation.
Where to start in finding the right AIOps Tools?
AIOps is an exciting field for managing the fast-growing and ever-complex IT infrastructure, network, and applications. Even as some established technology companies are making forays into the area to find product adjacencies, many startups are flourishing in this space. The thesis behind AIOps appeals to venture capital firms, and hence it is like to witness a boom in activity and outcomes.
CIOs and technology leaders can use the companies and their AIOps platform as a starting point. ITBits can conduct custom vendor scan and capability research if your company is interested in finding the right AIOps system to power your IT operations and analytics needs.
Key Capabilities and Features of AIOps Platforms:
- Data ingestion and consumption
- Data synthesis and aggregation
- Continuous Monitoring and Reporting
- Event correlation
- Anamoly detection
- Pattern Discovery
- Alerts and Notifications
- Predictive Analytics
- Autonomous Fixes
- Self Healing
- Dashboards and Reporting
AIOPs Platform Architecture:
Gartner defined a conceptual architecture for best-in-class AIOps platforms.
As one of its many products and solutions, Splunk offers an AIOps platform that enables IT modernization through end-to-end service monitoring, reduced alert noise, and predictive responses.
The OpsRamp IT operations management (ITOM) platform allows enterprises to see everything in the hybrid IT environment, take the right action faster with the integrated event and incident management, and automate with confidence with AIOps.
StackState positions itself as a topology+ relationship-based observation platform. StackState’s capabilities include its 4T model – Merging Topology, Telemetry, Tracing, and Time. It claims to provide insights from the tons of tools that generate a treasure of data.
Moogsoft delivers an enterprise-class, cloud-native AIOps platform that empowers end-to-end capabilities in analyzing and resolving IT Operations issues.
BigPanda helps IT Ops teams keep their businesses running with Event Correlation and Automation, powered by AIOps.
Scalyr provides complete log analytics and observability SaaS offering in support of modern cloud applications.
Anodot leverages AI to constantly monitor and correlate business performance and identify revenue-critical issues, providing real-time alerts and forecasts.
Dynatrace provides software intelligence to simplify cloud complexity and accelerate digital transformation.
PagerDuty sits at the center of the technology ecosystem and analyzes digital signals from all software-enabled systems to intelligently pinpoint issues like outages and capitalize on opportunities, empowering teams to take a right real-time action.