Market Guide for AIOps Platforms
Overview
Key Findings
- AIOps is getting entrenched in enterprises predominantly for IT operations, while some of the more mature organizations are using the technology to provide insights to business leaders.
- AIOps skills and IT operations maturity are the usual inhibitors in ensuring quick time to value when using these tools, followed by data quality as an emerging challenge for some of the more mature deployments.
- Enterprises adopting AIOps platforms use it to enhance and, occasionally, augment classical application performance monitoring (APM) and network performance monitoring and diagnostics (NPMD) tools.
- Vendors are developing strategies to use machine learning — the primary technology within AIOps — to analyze data challenges for IT operations across the three dimensions of volume, variety and velocity. At the same time, they are building specialization across both data storage and AI practices.
Recommendations
- Deploy AIOps by adopting an incremental approach that starts with historical data, and progress to the use of streaming data, aligned with a continuously improving IT operations maturity.
- Select platforms that enable comprehensive insight into past and present states of IT systems by identifying AIOps platforms that are capable of ingesting and providing access to text and metric data.
- Deepen their IT operations team’s analytical skills by selecting tools that support the ability to incrementally deploy the four phases of IT-operations-oriented machine learning: descriptive, diagnostic, proactive capabilities and root cause analysis to help avoid high-severity outages.
Market Definition
Market Description
- Ingesting data from multiple sources agnostic to source or vendor
- Enabling data analytics at two points:
- Real-time analysis at the point of ingestion
- Historical analysis of stored data
- Providing access to the data
- Storing the acquired data
- Using machine learning
- Initiating an action or next step based on the result of analysis
Source: Gartner (November 2018)

Market Direction
- Rapid growth in data volumes generated by the IT infrastructure and applications (two- to three-fold increase per annum)
- The increasing variety of data types generated by machines and humans (for example, metrics, logs, wire data and documents [knowledge management])
- The increasing velocity at which data is generated as well as the increasing rate of change within IT architectures due to the adoption of cloud-native or other ephemeral architectures
- Reduce noise (for example, in the form of false alarms or redundant events)
- Provide better causality, which helps identify probable cause of incidents
- Capture anomalies that go beyond static thresholds to proactively detect abnormal conditions
- Extrapolate future events to prevent potential breakdowns
- Initiate action to resolve a problem (either directly or via integration)
- Alerting
- Problem triage
- CMDB population
- Run book automation
- Application release orchestration
Source: Gartner (November 2018)

Market Analysis
- Historical and streaming data management — Software or appliances that allow for the ingestion, indexing and persisted storage of log data, wire data, metrics and document data (see Note 2). The resulting databases are mostly unstructured or polystructured, while the stored datasets accumulate in high volumes, change with high velocity and are implicitly structured according to highly varied formats. This historical data management functionality can be called “big data management.” To provide value under the IT operations use case, the tool must also present data in time scales perceived by a human user as real time, delivering data directly at the point of ingestion without requiring access to a persisted database. It must provide a coherent analysis across multiple streams of real-time and historical data.
- Basic and advanced statistical analysis— A combination of univariate and multivariate analysis, including the use of correlation, clustering, classifying and extrapolation on metrics captured across IT entities as well as for curating data at source.
- Automated pattern discovery and prediction — Use of historical or streaming data of one or more of the types mentioned above, to elicit mathematical or structural patterns that describe novel correlations that may be inferred from, but are not immediately present in, the datasets themselves. These patterns may then be used to go forward in time and predict incidents with varying degrees of probability.
- Anomaly detection — Using the patterns discovered by the previous components to first determine what constitutes normal system behavior, and then to discern departures from that normal system behavior.
- Root cause determination — Pruning down the network of correlations established by the automated pattern discovery and prediction component to isolate those links of dependency that represent genuine causal relationships in the sense of providing recipes for effective intervention.
- Prescriptive advice — Performing triage on problems, classifying them into known categories. It may then mine stores of prior solutions, analyzing these for applicability and offering them in a prioritized form for usage of remediation. Eventually, these will use a closed-loop approach and enable voting on their effectiveness after they are utilized.
- Topology— For the patterns AIOps detects to be relevant and actionable, a context must be placed around the data ingested. That context is topology. Without the context and de facto constraint of topology, the patterns detected, while valid, may be unhelpful and distracting. Deriving patterns from data within a topology will reduce the number of patterns, establish relevancy and illustrate hidden dependencies. Using topology as part of causality determination can greatly increase its accuracy and effectiveness. Capturing where events occurred and their up and downstream dependencies using graph and bottleneck analysis can provide great insight on where to focus remediation efforts.
- Vendors going to market with a data-source-agnostic AIOps platform. These products tend to be generic and cater to the broadest use cases.
- Vendors that have the key components, but tend to have a restricted set of data sources. These vendors are typically focused on one domain (for example, network, endpoint systems and APM), or are selective about data types like alert streams from other tools. Such tools tend to have a restricted set of use cases, targeted at a certain segment of IT operations.
- Some vendors with existing monitoring solutions limit data sources to their own monitoring products or extend to a limited partner ecosystem. This is again a case where the target audience is limited to those with the right mix of data sources.
- Some open-source projects enable users to assemble their own AIOps platforms by offering tools for data ingest, a big data platform, ML and a visualization layer. End users can mix and match the components from multiple providers.
Representative Vendors
Market Introduction
Table 1: Representative Vendors
Vendors
|
Year Founded
|
Headquarters
|
Website
|
---|---|---|---|
Anodot
|
2014
|
Israel
|
|
BigPanda
|
2014
|
United States
|
|
BMC
|
1980
|
United States
|
|
Brains Technology
|
2008
|
Japan
|
|
CA Technologies
|
1974
|
United States
|
|
Devo (Logtrust)
|
2011
|
United States
|
|
Elastic
|
2012
|
United States
|
|
Evolven
|
2007
|
United States
|
|
FixStream
|
2013
|
United States
|
|
IBM
|
1911
|
United States
|
|
InfluxData
|
2013
|
United States
|
|
ITRS
|
1993
|
United Kingdom
|
|
jKool
|
2014
|
United States
|
|
Loom Systems
|
2015
|
United States
|
|
Moogsoft
|
2011
|
United States
|
|
Scalyr
|
2012
|
United States
|
|
ScienceLogic
|
2003
|
United States
|
|
SignalFx
|
2013
|
United States
|
|
Splunk
|
2004
|
United States
|
|
Stackstate
|
2015
|
Netherlands
|
|
Sumo Logic
|
2010
|
United States
|
|
VNT Software
|
2010
|
Israel
|
|
VuNet
|
2014
|
India
|
Market Recommendations
Ensure Success in the Deployment of AIOps Functionality by Adopting an Incremental Approach
Select AIOps Platforms Capable of Supporting a Broad Range of Historical and Streaming Data Types
Source: Gartner (November 2018)

- Digital experience data from APM
- Order data pulled from payloads in business transactions
- Sentiment data from social media
- Service desk requests and statuses
- Account activity from the CRM system
Choose Tools Offering the Ability to Systematically Progress Across the Four Phases of IT-Operations-Oriented Analytics and Machine Learning
Source: Gartner (November 2018)

- Initially experiment with allowing the software to reveal patterns that organize large volumes of data.
- Next, test the degree to which those patterns allow them to anticipate future events and incidents.
- Finally, work with root cause analysis functionality.
Evidence
- Platform selection
- Deployment strategy
- Multiple AIOps use case within and outside IT to aid visualization, decisions and diagnostics
Note 1Representative Vendor Selection
- Ability to ingest data from multiple sources, including historic and real-time streaming.
- Different offerings that include proprietary, open source, free and commercialized versions, including deployment that cuts across on-premises and SaaS-based options.
Note 2Data Types
- Log data ingestion — Software that allows for the capture of alphanumeric text strings from log files generated by any software or hardware device, and the preparation of that data for access and analysis, indexed for storage.
- Wire data ingestion — Software that allows for the capture of packet data direct from taps on the network. All protocol and flow information should be prepared for access and analysis, and indexed for storage.
- Metric data ingestion — Software that allows for the direct capture of numerical data (for example, the capture of data to which time series and more general mathematical operations can be immediately applied).
- Document text ingestion — Software that allows for the ingestion, parsing, and syntactical and semantic indexing of human readable documents. This may include the use of technologies commonly described as natural language processing (NLP).
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