ML in ITSM: The Machine Learning Use Cases Transforming ITSM

Increasingly, organizations are utilizing ML in ITSM, with ITSM solution providers such as ServiceNow providing in-built ML capabilities on their platforms.

However, organizations that want to take full advantage of ML in ITSM are replicating and extracting ITSM data to feed into purpose-built ML solutions.

This post explores the potential benefits of applying machine learning in ITSM, and why ITSM integration solutions are a key enabling technology in this use case.

Table of contents:

What is Machine Learning?
Use Cases for ML in ITSM
Machine Learning in ITSM Solutions
Maximize Machine Learning Capabilities With a Native ServiceNow Integration Solution

What is Machine Learning?

Machine learning (ML) describes a type of artificial intelligence that uses data and algorithms to mimic the iterative, experiential learning associated with humans.

With the ability to “learn,” machine learning can automate improvements, adapting to data being fed into the solution.

By learning from past data, machine learning can be utilized to identify patterns and make accurate predictions.

A commonly recognized use case for machine learning is natural language processing, whereby language and communication datasets are used to “teach” computers how to “understand” language and converse.

The goal is to feed enough past data from communications to allow algorithms to predict human-like and contextually accurate responses to natural language inputs.

Use Cases for ML in ITSM

ML solutions rely on data to learn, and ITSM solutions collect a lot of insightful and useful data. As such, organizations that can effectively feed ITSM data into an ML solution have a wealth of potential use cases to pursue.

Below, we list some of the ways ML in ITSM can help improve ITSM-related processes, but organizations could even leverage ITSM data in ML solutions to influence other areas of the organization.

Auto Approvals

ML-powered help desks can benefit organizations by automating service request approvals. By analyzing an employee’s role, department, and other parameters, these smart help desks can auto-approve requests (for example, if designers/developers raise requests for additional tools) without managerial intervention.

Additionally, machine learning can improve onboarding by gleaning valuable patterns from historical data to recommend suitable software, hardware, and access permissions for new employees according to their departments and roles.

Incident Resolution

ML-enabled predictive models can improve incident resolution and management by predicting infrastructure incidents and estimating accurate resolution times. These models can analyze incident data to deliver insights that can accelerate time to resolution, improve issue-handling efficiency, and increase data fidelity.

Predictive models help reduce post-deployment incidents by identifying potential problems early in the change request process. By leveraging actionable insights derived from previously siloed data, they help improve ITSM maturity and the overall customer experience while reducing service costs.

Problem Prediction and Proactive Prevention

ML algorithms can analyze incident patterns and identify potential system malfunctions before they occur, creating scope for proactive prevention and asset optimization. Trained systems can automatically trigger notifications or create problem tickets for anticipated issues, allowing technicians to address them promptly.

For example, if an application server’s performance deteriorates, an ML-powered help desk can predict potential failures based on past data (particularly ones correlating to system failure), warn relevant users, create a problem ticket, and link related incident tickets, ensuring proactive issue management and minimizing downtime.

Efficiency in Handling Level-0 and Level-1 Incidents

Machine learning can influence self-support solutions and improve the handling of Level-0 and Level-1 incidents.

Help desks can use ML algorithms to analyze incoming tickets and learn from past experiences to recommend suitable solutions to users with minimal human intervention.

For more complicated requests, the algorithm can learn from historical data to route tickets to the appropriate support team or technicians, automating the ticket assignment process without establishing specific rules or workflows.

Furthermore, ML can empower users to resolve issues independently through AI-powered chatbots (like Google Assistant) that can offer instant solutions to customer challenges or requests without needing to create a ticket.

Through self-service support, ML-powered help desks can minimize technician involvement and burden on support personnel while offering faster resolution for priority incidents.

Asset Lifecycle Management

A considerable chunk of incidents stem from outdated IT assets with performance issues.

Machine learning can analyze performance metrics and incident history to identify the assets that are most likely to fail.

After identification, the algorithm can send timely notifications to technicians and initiate replacement orders. For instance, it can automatically create requests for printer toner replacements after a certain number of pages are printed.

While this is just one routine scenario, ML can streamline asset lifecycle management, enhancing the UX for employees and end users.

Machine Learning in ITSM Solutions

Recognizing the demand for machine learning in ITSM, ServiceNow provides machine learning capabilities through its Predictive Intelligence solution.

However, many organizations want to do more with machine learning than what is made available via ITSM platforms such as ServiceNow. To do so, they need to feed large amounts of ITSM data into the machine-learning solution/algorithm. In other words, they need an integration solution.

Integrating ITSM and Machine Learning Solutions

Integration solutions automate data replication and transfer between systems, providing a scalable approach to transferring large datasets. This makes them vital in enabling ML in ITSM use cases.

With the right integration solution, organizations can make better use of their ITSM data, including feeding it into dedicated AI/ML solutions to gain advanced insights for improving various ITSM functions.

But what does the “right” integration solution look like?

High-throughput is essential

Machine learning solutions are reliant on large datasets to learn. As such, being able to get more, good quality data into the platform, faster is a huge advantage.

Organizations should evaluate integration solutions for their throughput (the volume of data that the solution can process in a specified period), ensuring the chosen solution doesn’t create a bottleneck that limits the value of the ML solution.

The throughput between specific integration solutions and types of integration solutions can vary significantly. For instance, API-based solutions often have limited throughput due to their external-to-platform nature and the performance-impeding process required to extract data from the platform(s) they integrate.

Be careful to avoid performance degradation

The other side to an integration solution’s throughput is the performance of the integrated platform. These two considerations often go hand in hand since poorer performance in the integrated platform typically means slower data transfer rates out of the platform.

However, the negative impacts of platform performance degradation are not limited to data transfers. ITSM performance degradation is typically felt throughout the platform slowing down and affecting:

The speed of database queries and retrieval in the platform
The creation of reports
The population of dashboards
The responsiveness of the platform’s UI

So to efficiently populate machine learning solutions with large ITSM datasets, it’s best to choose an integration solution that does not cause significant performance degradation.

It should be noted that in addition to limited throughput, the inefficient nature of API-based integration solutions is also a common cause of significant performance degradation.

Machine learning needs quality data

ML solutions can only learn useful patterns and deliver optimal results if they are fed credible, good quality data. This means data needs to be accurate, correctly mapped and labeled, and arrive in the correct format so it can be processed and used.

Faulty and inaccurate data will only lead to erroneous insights, which can lead to counterproductive decisions. In AI and ML, the age-old saying, “garbage in, garbage out,” is perhaps more relevant now than ever before.

The integrated data must be secure

Since ITSM data often contains sensitive data, often subject to regulatory compliance, data security cannot be overlooked. Thus, organizations must evaluate the chosen integration solution for the security risks it might introduce.

It’s no good being confident that data is secure within the ITSM platform if there aren’t provisions to ensure its security when in-transit out of the platform or at-rest within the target location.

Therefore, organizations should evaluate integration solutions for their security capabilities, particularly the available levels of encryption and obfuscation.

Organizations with acute data security concerns should also learn about the vulnerabilities of API-based integration solutions and the increased risk of using such a widely available, commonly targeted technology.

Maximize Machine Learning Capabilities With a Native ServiceNow Integration Solution

To successfully implement and use ML in ITSM, organizations must be able to transfer and feed huge volumes of quality data into the ML solution.

This calls for an integration solution that can rapidly transfer large amounts of data to the target source (in this case, a dedicated ML solution), while preserving ServiceNow’s performance.

Perspectium provides ServiceNow-native data replication solutions and a service that allows organizations to do just that.

With Perspectium DataSync, organizations have an integration solution capable of rapidly extracting and replicating tens of millions of ServiceNow records per day, vastly overcoming the Now Platform’s data export limitations and the throughput of API-based solutions.

Designed by ServiceNow’s founding developer, David Loo, Perspectium was purpose-built to address the key considerations associated with replicating and extracting ServiceNow data.

As such, DataSync enables high-throughput data replication to external systems without requiring APIs or web services that drain ServiceNow’s operational bandwidth and degrade the platform’s performance.

Instead of API, DataSync leverages push technology to transfer data into a sophisticated and secure message broker system (MBS), from which target systems can then retrieve the data.

Encryption protects data both in-transit and at-rest, and the MBS helps prevent data loss as even during endpoint outages, the data is queued securely in the MBS, and the transfer can resume when the endpoint is back online.

This makes it an ideal integration solution for high-throughput use cases such as AI and ML in ITSM, analytics, business intelligence, reporting and more.