Service-driven Auto Remediation: 3 Keys to Success

For today’s businesses, there’s a premium on delivering optimized user experiences—all the time and every time. However, as environments continue to grow in size and complexity, the task of delivering optimized service levels gets increasingly difficult. This post looks at why service-driven auto remediation is emerging as such a key imperative, and it reveals the three key requirements to make it happen.

Overcoming the Barriers to Optimized Digital Experiences

Across industries and markets, personal interactions continue to be supplanted by the digital. Now, applications are where battles for customer loyalty can be won or lost. In the digital economy, it’s application quality that separates market victors from laggards. While optimizing service levels and experience is critical, it seems to be getting more challenging to do every day.

Increasing Complexity

Most enterprise-class business services now rely not only on traditional systems, including on-premises mainframes and distributed systems, but on a plethora of new, dynamic technologies, such as containers, cloud delivery models, virtual and software-defined components, and more.

Increasing Scale

The volume, variety, and velocity of data that needs to be managed, correlated, and analyzed continues to grow dramatically. In the wake of initiatives like multi-cloud deployments, microservices development, and Internet of Things (IoT) implementations, teams continue to see explosive growth in the operational data being generated. Ultimately, internal team members simply can’t keep pace.

Reactive, Disjointed Tools Fuel More Complexity

Exacerbating matters is that, as IT teams looked to manage their increasingly diverse environments, they’ve had to add more point monitoring tools and automation capabilities to the mix. These disjointed tool sets compound the complexity and challenges:

• Point monitoring tools result in reactive issue identification and alert fatigue. Working with dozens of tools, teams struggle with hundreds of thousands of alerts that feature a high rate of inaccuracy and redundancy. Lacking unified visibility that spans their hybrid environments, staff spend too much time inspecting various systems and domains in order to identify the root cause of issues. As a result, customer experience suffers while triage calls run for hours.
• Point automation capabilities don’t scale or work in complex environments. When organizations employ limited automation that is connected to domain-specific tools, they encounter a number of challenges. First, with these one-to-one integrations, they can’t easily automate complex workflows that span multiple technology platforms and domains. Second, these integrations don’t work well in most cases. For example, an alert from a server monitoring tool can trigger server-related remediation, while the actual issue may stem from a network device.

Establishing Auto Remediation: The Requirements

Today’s IT teams can’t simply try to do the same things better. To ensure their complex, hybrid, interrelated, and highly dynamic environments deliver an optimized user experience, operations teams must achieve fundamental breakthroughs in scale and efficiency. It’s no longer enough to just react a little faster when issues arise. Teams must gain the visibility needed to identify potential issues—and auto remediate them before they affect service levels.

To contend with the explosive growth in data, complexity, and user demands, IT teams need to adopt an artificial intelligence for IT operations (AIOps) platform that provides service-driven, autonomous remediation. The following sections reveal the three requirements AIOps platforms need to address.

Auto Remediation Requirement #1: Predictive Identification of Potential Risks to Services

Leveraging traditional, reactive monitoring tools and approaches, IT teams lack the insights needed to effectively predict issues before a business service or application is disrupted. Given the criticality of delivering a phenomenal user experience, these teams need an AIOps platform that offers algorithmic- or machine-learning-based insights for detecting abnormal behaviors and predicting potential issues.

It’s also essential that AIOps platforms offer capabilities for mapping issues to associated services, so IT teams can intelligently prioritize troubleshooting and remediation efforts based on which issues will have the biggest potential business impact. For example, if two issues arise and administrators can see that one is affecting a payroll service that isn’t being run currently, and another is hitting an e-commerce service that runs 24/7 and accounts for the bulk of the company’s revenues, they can prioritize their efforts accordingly.

Auto Remediation Requirement #2: Automate Root Cause Analysis Across Domains and Technologies

Even with the best predictive tools in place, downtime and performance issues may still arise, whether due to an administrator’s configuration error, external service outages, or a host of other causes.

Within many IT organizations, when these performance issues or downtime occur, operators struggle to determine why. While a single issue may be the culprit, large numbers of redundant or false alerts may be generated, making it difficult for administrators to filter through the noise and identify the issue that needs to be addressed. At the same time, when operators see that a service is experiencing issues, it may be difficult to determine how or if the issue is affecting business services.

To combat these challenges, operators need timely, targeted insights that can enable fast, automated root cause analysis. To address these requirements, AIOps platforms need to provide machine-learning-driven intelligence that can automatically identify the probable root cause. To support this machine learning, these platforms must also offer a topology analytics service that automatically discovers and maps key IT assets and stores topology information in a graphic database. This service needs to consume data and correlate intelligence from multiple architectural layers to effectively determine the probable cause.

Auto Remediation Requirement #3: Establish Comprehensive, Contextual Automation

Once an issue has been identified, whether predictively or through automated root cause analysis, IT teams need comprehensive, intelligent capabilities that can automatically execute remediation tasks required in a complex, dynamic enterprise environment. To ensure success, AIOps platforms need to provide scalable, flexible, and easy-to-use automation that can be aligned with fast changing business and technology environments.

AIOps platforms must be able to orchestrate the delivery of services in business, application, and infrastructure layers, across on-premises, cloud, and hybrid environments. This automation should seamlessly support complex, organization-specific processes. For example, an AIOps platform may detect an impending storage issue in an Amazon Web Services EC2 instance and trigger the provision of an additional instance. This server provisioning may need approval from a budgetary, compliance, or business perspective.

These approval workflows should be easily accommodated. By leveraging these contextual auto-remediation capabilities, IT teams can ensure that service requests aren’t just logged—they’re acted upon before there’s any impact on the user’s digital experience.

Conclusion

With the above capabilities, teams can establish the auto remediation that powers significant improvements in operations and digital experiences.