Last Updated Jun 23, 2020 — AI-Powered Analytics expert
AI-Powered Analytics

IT organizations looking to gain greater efficiencies while reducing the risk of business disruptions may look to the transformative potential AIOps offers. Interest in these solutions may even be increasing as workforces become highly distributed. But in order to achieve the benefits they seek, IT leaders must first thoroughly evaluate their available AIOps solutions. Only solutions with the right set of capabilities will be able to help them transform their organization and its practices in the desired way. 

An AIOps solution builds off of the velocity-driven framework established within DevOps practices. DevOps prioritizes both continuous development and continuous integrations of new improvements to operating environments. A primary goal of DevOps is to allow platforms and applications to constantly evolve and deliver new value.

Similarly, AIOps aims to derive continuous insights from organizational data that can lead to continuous improvements within the organization. Greater visibility of performance metrics and proactive identification of emerging trends empowers IT to extract continual value from their enterprise data. In addition, automation driven by artificial intelligence & machine learning (AI/ML) analytics models can streamline many low-level IT processes, allowing IT teams to free up time and effort spent on arduous tasks, and allowing talent to focus on projects with a bigger scope, such as proactive innovation.

Each implementation of AIOps should be custom-fitted to the organization in question. The organization must consider the specific goals it wants to achieve and the specific use cases that can facilitate those goals. With that said, there are a number of standard capabilities an AIOps solution should provide in order to derive the expected value. 

IT leaders and other key decision-makers can look to the following four capabilities to help them predict if the AIOps solution they are considering will help them achieve the desired advantages they seek.

1. Pre-built Data Integrations with Key IT Operations and Transactional Systems

Automation and AI/ML capabilities are what set an AIOps solution apart from typical business analytics ones. However, AIOps must be able to baseline IT operations, infrastructure, and business performance. Namely, it must be able to import data from all relevant sources.

Some vendor-based solutions may profess to offer AIOps-like capabilities, but only within certain siloes. A true AIOps solution must penetrate through informational siloes. The needed information should be readily obtainable through data adapters integrated within each major system of record.

Priority operational and monitoring systems can include:

  • Service desk and ticketing software, like BMC and ServiceNow
  • Application performance monitoring (APM), like Appdynamics
  • IT Infrastructure Monitoring (ITIM), like Splunk
  • Development Value Stream tools, like Jira and Azure DevOps
  • Planning and governance platforms, like OneTrust
  • Unstructured text from email and corporate social systems, like Slack

Aggregating data across all of these systems can reveal enterprise-wide insights and contextual information that each individual platform could not achieve on its own. For example, IT leaders can monitor sentiment towards IT service performance using KPIs derived using data from corporate chatter and service desk platforms.

Also, an AIOps solution’s data integration capabilities must not only be source-agnostic, but also have automated workflows that facilitate continuous monitoring and analysis. A canonical common data model allows for every bit of data to be objectively compared with others, ensuring apples-to-apples comparisons necessary for an AIOps solution to provide a single, accurate source of truth.

2. Built-to-Purpose ML/AI Engines

The “AI” portion of AIOps emphasizes the ability for AI/ML engines to derive insights that manual analysis alone could not reveal. These insights should be informative, actionable, and based upon the actual data derived from key systems of record.

Not every AIOps solution necessitates a full range of AI/ML engines in order for an organization to position itself to achieve its desired goals. Each functionality must be selected with a specific use case in mind. 

Example AI/ML engine use cases include:

3. Automation Capabilities That Support Your Team

Automation allows AI to begin to do the heavy lifting for your IT team, freeing up resources to be used in a more efficient manner.

Like the use of AI/ML-driven engines, the automation functionalities you seek should be based on specific IT challenges, not just a list of enticing features.

A simple example of automation is the automated assignment of IT tickets to appropriate assignment groups. While many ITSM platforms can do this, it is often based on simple decision-making trees rather than complex analysis. Aggregating all enterprise data adds contextual layers to this decision making, ensuring a higher degree of performance accuracy, especially as time goes on.

Another example of beneficial automation includes automated remediation of low-level change risks based on their characterization within a complex change risk scoring model. Risk-flagged changes that aren’t automatically remediated can still be delegated to the appropriate assignment team with all relevant information provided, reducing the need for a change advisory board to conduct a painstaking manual analysis of the proposed change’s associated risks.

An IT organization can also set up automated SLA compliance alerts based on predictive analytics, or automated reallocation of system resources based on a trend analysis revealing that a certain application may soon run out of storage capacity given current trajectories.

4. Self-Service Dashboards and Informative Visualizations

One final AIOps functional criterion worth noting is that the information and insights obtained via analytics must be made accessible and actionable to all user groups that touch the correlated process or platform. By putting information into one source of truth that’s readily accessible to all, every IT employee can act as a self-appointed business analyst. This stands in opposition to typical roles for IT personnel: reacting to information after-the-fact, following instructions from higher-ups, or laboring to maintain the functionality of tools as changes or problems affect their performance.

Self-service dashboards present information in a way that can be explored visually. A dashboard should provide drill-down, sorting, slicing, and dicing to allow any given IT user to learn more about something that seems worthy of their attention. 

Once an IT employee determines the appropriate action to take in response to the picture the data in front of them reveals, they should not have to spend extensive amounts of time reassembling their conclusions. Rich visualizations compiling their revelations can not only empower their own insights, but it can also encourage buy-in from key stakeholders.

Other Considerations: Costs and Security

When considering an AIOps solution, analytics and automation capabilities are not the only thing that matters. Organizations also have to consider the costs of the solution, including both the acquisition costs and the costs of using the solution over the long term. Derived value should, naturally, outstrip these costs. Ideal solutions provide an accelerated timeline to value, allowing an organization to achieve a quick return on their AIOps investment.

Security is another factor worth considering. Being able to access data from key systems of record should not inherently make those systems vulnerable to data leaks or breaches. Furthermore, the use case at hand will tend to define the security parameters required. For example, if the AIOps platform is going to be accessible via a browser-based remote portal, it should either provide the needed level of security natively or offer convenient integration with your proprietary or vendor-based IT security solution of choice.

Find the Right AIOps Solution for Your Specific Needs, Not Just One That Sounds Good on Paper

AIOps solutions that can meet all of the above criteria tend to be built-to-purpose, but developed off the backs of experience from other clients. Data adapters and ML algorithms, for example, can be deployed to suit their unique environment, but they should be established using practices proven to offer accelerated time-to-value with past clients.

Finding an AIOps solution that meets all of your needs can allow your IT teams to enhance their own capabilities, offering proactive improvements and laser-precision insights that can elicit value from everyday processes. Only under these conditions can an AIOps solution deliver on the hype that captivated the key interested decision makers in the first place.

For more information on how an AIOps solution can provide benefits to your organization and what capabilities to look for, watch our recent webinar created in collaboration with Forrester: “How AIOps helps IT Change and Service Management be more reliable and nimble”

Watch the Webinar

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