Traditional Analytics is Dead -- Here's why you need Intelligence to improve ITSM

Analytics refers to the process of interpreting insights and patterns from historical information. This is the old way of finding answers from data – answers that are intended to improve your ITSM operations. However, traditional analytics only answers the question of ‘What’ associated with the data. Therefore, ITSM leaders are inherently limited by the capabilities of analytics tools to make the right decisions that improve ITSM KPIs.


For instance, what is the number of new users requesting access to payroll data? What are the hierarchical levels of these users in the organization? What are the new trends in cloud computing resource provisioning across different departments and what is the most popular service? Analytics tools can help answer these questions, if the answers lie within the data itself – including concrete data points and measurements that can be calculated based on the available data.


Intelligence refers to the ability to learn and reason from data. It not only finds existing trends and patterns in the data, but also finds actionable insights with concrete details on improving your ITSM operations. Intelligence relies on the modeling estimation of the system: data represents behavior, which can be modeled and used to predict future data outcomes based on historical data input.


Some of the key differentiations between an AI-based intelligence system over the traditional analytics solutions include the following:

Trends & Research

Complex and Data-Driven Operations:

Modern business organizations are inherently data-driven. The IT systems and services that power business operations generate vast volumes of information and contain unprecedented insights. These insights describe how businesses interact with end-users, how to improve the service and optimize business operations. These datasets are generated across multiple sources and in silos. Making sense of it all requires advanced AI capabilities that can model the behavior of systems and services operating across distributed locations.

Learning and Reasoning:

One of the important differences between traditional analytics tools and intelligence technologies is the ability to demonstrate capacity for learning and reasoning. While traditional analytics tools can follow predefined rules and lead to relevant answers, these answers may not be sufficiently insightful based on the evolving contextual knowledge. Intelligence takes the assumptions out of the equation and reasons based on new data, which itself can be evolving and unpredictable. AI tools essentially model a complex system behavior based on data. Instead of mapping every event to a known consequence, a system model can help predict outcomes to similar and correlating events that may be impossible to identify and accurately map to a known consequence.


Business organizations that aim to operate with high agility need actionable insights based on new and real-time data. Instead of relying on historical evidence and discarding new anomalous data, an adaptable AI system tries to find the reason for the anomaly. These anomalies may point to patterns of new IT incidents, new customer demands and changes that must be accounted for toward future decision making actions. An intelligent system is able to adapt as it learns from new patterns emerging in data, which may be significantly different from past trends.

Finding Concrete Answers:

ITSM leaders invest in ITSM technologies to find concrete answers to pressing issues: why do we have a long ticket queue? Where are the performance bottlenecks? Which changes will help optimize ticket routing to maximize business impact? These answers require contextual information from a disparate set of siloed data sources and organizational functions. An intelligent system can process information of various formats, understand business processes and use new data to predict concrete and actionable insights. These insights can be useful for answering difficult questions that otherwise require manual analysis and human expertise.


Progressive ITSM organizations are able to identify changes and act proactively based on real-time information. Unlike traditional analytics tools that simply run an analysis on historical data, AI tools can learn, re-learn and reason on evolving datasets in real-time. The models can be integrated with other ITSM automation tools to trigger an automated control action based on real-time changes in data. Intelligent technologies enable the flow of real-time data that can be integrated from multiple sources, with applicable compliance and governance controls. From the user perspective, they simply need to control how real-time insights transform into proactive actions. An example of a proactive action is the ticket routing based on agent work-load – to understand in real-time if the right agent is overloaded and to take the right decisions based on this information.

Human Augmentation:

The cognitive performance, learning and reasoning capabilities, and adaptability to re-learn and act upon real-time data allows intelligent technologies to augment humans in decision making. ITSM can apply these capabilities to solve concrete problems such as business process optimization, ticket routing, identifying performance bottlenecks and optimizing ITSM workflows. Instead of using analytics tools to find new trends and employing more experts to make data-driven decisions, modern ITSM can take advantage of AI tools to achieve these goals. The application of these tools can be varied to solve a range of challenging ITSM problems, simply by learning from end-user interactions and business processes through data. An example of human augmentation is the dynamic skillset mapping. With intelligence, we can understand which tickets are solved by specific staff members, understand the quality of ticket resolution and therefore automatically build an accurate skillset for every agent.

Value Driven:

By the end of the day, technology investments should justify returns on investments. Hyperautomation intelligence technologies that characterize human cognitive behavior for end-to-end process level optimization of an ITSM organization are specifically designed to achieve these goals. While your ITSM may rely on multiple technologies to perform various technology functions, hyperautomation intelligence can combine and augment the returns on ITSM technology investments. Intelligence tools can be programmed to evaluate how different business processes coordinate to realize collective ITSM goals such as ticket routing and ITSM workflow optimization. By contrast, traditional analytics tools function in silos and may be programmed only to process historical information available to different ITSM functions. For instance, Swish AI can detect anomalies and spikes related to service requests. While traditional analytics can only specify the number of anomalous tickets generated suddenly, Intelligence can help you understand why this happens, provide all tickets the same categories and create a new ticket that will summarize all relevant tickets so that only the right person can deal with it.

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