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In the first part of the blog, we highlighted several challenges facing today’s Service Desk and IT support ecosystems:
a) Alert Storms
b) Siloed monitoring solutions
c) Context and Correlation
d) Disparate data sources
e) Augmented Automation
f) Closing the Loop
Here, we discuss how a Machine Learning platform like Sainapse integrates with existing support infrastructure, preserving investment while at the same time addressing the identified challenges and enable the IT support organization to reduce the ticket resolution times, increase their first contact resolution rate, reduce cost per ticket, improve customer satisfaction and reduce total cost of ownership.
Alert storms are a very common challenge for IT Support desks and is the one existing system monitoring platforms and other systems of engagement that have had some success in addressing. Essentially, it is enabling an in-depth categorization of alerts and some machine learning (ML) to ensure that the critical alerts are handled by service desk and NOC reps first. The ability to manage and triage alerts has become table stakes in the monitoring ecosystem.
Sainapse enables alert triaging and management out of the box. Sainapse learns how to segregate the critical and actionable alerts based on two inputs – how it was done manually in the past, and how alert handling policies are defined for the NOC. Sainapse can also work alongside existing tools that may be implemented for this purpose, and power up downstream actions – orchestrating remediation, and resolution recommendation – that increase the efficacy and efficiency of the human reps in the NOC.
Enterprises have also been investing in new and improved technology platforms in order to keep up with business needs and customer demand, but more often than not they do so with departmental silos that bring the promise of quick and easy deployment and integration across the enterprise, but in reality, don’t meet these lofty expectations. These point solutions, CRM, Service Desk, APM, Knowledgebases, etc., are independent silos that over time increase the level of technical debt. And organizations are hesitant to eliminate these platforms due to inertia and the level of investments that have already been made.
Sainapse was architected to address the joint challenges of siloed monitoring solutions (systems of engagement) and disparate data sources. Understanding that the need to preserve existing investment in IT is predominant in the enterprise, Sainapse integrates with existing support systems (APM, CRM, ITSM) and data sources – knowledgebases, SOP, document repositories, and third-party vendor data – without the need to scrap prior investments or perform a massive systems integration project.
Sainapse has also developed API integration with some of the more common alerting and monitoring platforms, such as AppDynamics, allowing it to receive the alerts from the platform, research the distributed data stores, query its ML engine for historical patterns, determine the context of the alert, correlate the information and provide a set of recommended resolutions to the rep, reducing the time the rep spends performing the research, enabling higher throughput of alert management and ticket resolution.
Furthermore, out of the box integrations with ITSM solutions such as ServiceNow allow Sainapse to query the CMDB to determine what if any configuration anomalies or changes may exist, query the knowledgebase or document repository to check the SOPs and Service Agreements, query the ML engine to determine context and correlate the events, and recommend a resolution or a prioritized set of resolutions for the rep to review and implement.
We touched upon augmented automation earlier, where organizations have moved towards the mantra of “automate everything” in their infrastructure, implementing infrastructure as code, DevOps toolchains, NLP, RPA, etc. Without an intelligent orchestration engine, the rep still has to research the root cause of the alert, research the SOPs and Service Agreements, find the right automation script and manually trigger the automation script.
Sainapse’s orchestration engine has the ability to determine which automation scripts should be triggered based on the context of the ticket and historical patterns (which its ML engine is constantly updating), trigger the automation scripts directly or if it is a sensitive component of the system, provide recommendations to the rep which automation scripts should be triggered.
One of the core strengths of Sainapse is how it uses artificial intelligence combined with its machine learning models to continually learn and improve its ability to make more accurate recommendations. Sainapse performs this function throughout the resolution process, initially logging the recommendations associated with a ticket and updating the recommendation in its ML engine after the rep has either confirmed the recommended resolution or changed the resolution to drive a better outcome.
By closing the loop at multiple instances in the process, Sainapse iteratively learns and enhances its ability to make the right resolution recommendation the first time, allowing the rep to focus on resolving issues as opposed to spending countless hours researching for the correct resolution.
Intelligent AIOps enabled monitoring platforms and systems of orchestration need to be able to integrate with the existing systems in the IT operations management (ITOM) chain to enable a seamless workflow, both upstream and downstream. Sainapse enables this capability out of the box with a “no code, low code” approach to configuration.
While current monitoring platforms and AIOps solutions primarily focus on managing alert volumes and ticket elimination, Sainapse focuses on enhancing the ticket resolution process through ticket correlation and determining context and providing prioritized resolution recommendations, facilitating the work of the IT support team and driving towards the achievement of the TCO, Revenue and Customer Satisfaction benefits.
For example, if a resource is low on memory or a VM close to capacity, i.e., near or at its efficiency threshold, Sainapse receives the alert from the monitoring platform, confirms that the said resource is supporting a critical service, and then either directly kicks off a runbook via an automation workflow (CI/CD) platform, e.g., Ansible that automatically allocates incremental resources, or recommends the automation path to the rep. Once the new resource is allocated, Sainapse and the monitoring platform are notified of the new resource allocation, the initial alert is closed and Sainapse logs the resolution, correlating it to the alert as it continuously enhances its ML model.
Sainapse leverages existing IT support infrastructure investments by seamlessly integrating into common CRM, APM, ITSM platforms and diverse knowledgebases (e.g., Sharepoint, Dropbox etc.), preserving IT investment. As the system of orchestration, Sainapse facilitates augmented automation so that the support reps can focus on executing the fix as opposed to spending time performing research.
Finally, Sainapse iteratively learns throughout the process, closing the loop so that it institutionalizes the learnings and continually optimizes its recommendations.
A system that is Sainapse-enabled increases Service Desk and IT Support teams’ operational efficiencies by leveraging distributed data, correlating incidents and service requests, extracting insights about the underlying systems, monitoring operational and usage statistics, and proactively recommending resolution options or automatically solving application performance problems.
