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Data drives decisions.
Making the right decisions requires the accurate knowledge of the state of the real world, including the markets, customer sentiments and needs, business operations and the technologies driving the business. This means that ITSM leaders cannot rely on outdated knowledge to drive critical business decisions on technology updates, IT service delivery, resource allocation and support services. In order to extract the latest knowledge about the world, organizations need access to real-time data, technology capabilities that can make sense of this information in real-time and provide accurate and up-to-date knowledge.
Business organizations typically face three stumbling blocks against the adoption of real-time data for making key business decisions. First is the cultural aspect of decision making. Traditional organizations have always relied on sustained and consistent processes for decision making. They rely on manual ways of collecting information and the information is processed across multiple hierarchical layers of the organization before a decision is found. This is a slow process, does not account for incorrect and inconsistent assumptions, and limits the ability to compete against disruptive forces in the market.
The second issue is the sheer scale and volume of big data generated across all avenues: technology, social, markets and business operations. How do you collect, process and analyze the data that’s growing in volume, variety, veracity and velocity at unprecedented levels?
Yet, end-users are expecting business organizations to establish the mechanisms necessary for solving issues faster, delivering IT services that improve service quality and yet reduce the cost of the services.
This challenge requires business organizations to invest in the technologies capable of real-time data processing and be able to use the knowledge to improve service delivery from an end-user perspective.
The third issue is related to the technology itself. Many technologies are not optimized and lack the intelligence necessary to drive data-driven intelligence that is most suitable for the unique challenges and opportunities facing your organization. In order to gain insightful knowledge from data in real-time, organizations need to enhance automation with intelligence.
The intelligence is represented in the form of AI models that replicate a system behavior. Real-time data allows the AI models of a system to simulate the system behavior in real-time, identify changing patterns and nuances, and gain contextual knowledge from data variations that occur in real-time.
The concept is similar to humans monitoring a system in real-time, except that the process is executed automatically and the technology uses its intelligence capabilities to extract insightful knowledge. This can also be referred to as the concept of human augmentation. Instead of relying on humans to perform repeated and predictable work, automation technologies are used instead.
Additionally, for tasks that require decision making based on real-time data, intelligence capabilities are embedded into the automation tools. This is precisely the overarching concept of hyperautomation intelligence: automating processes through end-to-end decision making intelligence based on real-time data.
Let’s look at some of the key examples of driving decisions through real-time data:
Similarly, the Swish AI platform enables hyperautomation intelligence using real-time data processing, which allows your ITSM organization to achieve the following key real-time decision making capabilities:
Some of the most important sources of knowledge insights for ITSM use cases is customer experience information. This information can be extracted from ticketing requests and interactions between service agents and end-users. In order to extract this knowledge, hyperautomation intelligence uses Natural Language Processing (NLP) AI capabilities. With the NLP based technology, ITSM leaders can understand customer experience and sentiment as it changes in real-time. Instead of having to manually review individual ticket requests, the intelligent systems running analysis on real-time data helps drive intelligent decision making.
