While Big Data promises big returns, the right ITSM strategy should focus on obtaining insights from the right data. This has implications for ITSM organizations in several ways.
Firstly, many organizations are too busy collecting information without sufficient thought into data architecture planning, data security as well as Big Data tooling capabilities to analyze the vast data deluge at scale.
Secondly, organizations are readily embracing a data-driven strategy and act on data without accurate (or relevant) insights. This means that the ITSM organizations must collect data on metrics and KPIs that are most relevant to achieving business objectives and improving customer experience.
When this data is available, they need a hyperautomation intelligence strategy that provides a holistic view of the ITSM performance and complement intelligence actions across otherwise disparate ITSM automation functions.
ITSM frameworks such as ITIL4 provide a basic set of guidelines that enable organizations to achieve operational excellence. When organizations adopt these frameworks rigidly and leave little room for flexibility in their ITSM workflows, they fail to operate as an agile business.
An example is the case of strict governance models in place to reduce risk associated with provisioning new IT services and third-party solutions to engineering teams. When these technologies are crucial for product development and the slow governance models fail to provision service access fast enough, employees bypass the guidelines of ITSM frameworks such as ITIL and access external tools without approval from relevant ITSM departments. This is the concept of Shadow IT and it jeopardizes the security posture of the ITSM organization, as a direct consequence of strict governance models and interpretations of ITSM frameworks that are designed to maximize enterprise IT security in the first place.
According to a recent Ponemon Institute study, 73% of IT professionals believe their organization is understaffed. For an ITSM organization, it’s easy to recognize the problem: growing service requests, tickets taking disproportionately long duration to resolve, repetitive occurrence of known IT issues… the list goes on. These issues not only suggest a staff shortage or inadequate performance of IT service delivery and incident management teams, but also clearly indicate bottlenecks in ITSM operational workflows.
Many delays in the IT Service Desk are only caused due to underoptimized ticket routing. Tickets and service requests hop between resolution teams that are supposed to collaborate together in resolving issues, but work in silos with a separate set of tools, information and expertise. IT fails to automate resolution of requests that occur repetitively and manual intervention is required due to complex workflows and lack of hyperautomation intelligence. And instead of identifying and resolving a problem’s root cause, resolution teams choose to patch issues repeatedly instead.
These are common scenarios indicating an overwhelming IT Service Desk that lacks the necessary technology capabilities to identify operational bottlenecks and optimize workflows intelligently.
Your IT Service Desk serves as the first line of contact with your customers facing immediate impact of performance issues with your IT services.
The task for ITSM organizations is to have in place the right set of capabilities and workflows that help resolve all issues facing end-users. In order to evaluate the performance of the IT Service Desk in this regard, ITSM teams typically review metrics such as ticket volumes, average resolution time, conversations per agent and customer ratings. While this information provides organizations with knowledge of customer dissatisfaction, it provides little insights into the behavior and sentiments of the impacted users, and the underlying causes.
This is precisely where advanced Natural Language Processing (NLP) capabilities as part of your hyperautomation intelligence can assist in several ways: identifying service issues in real-time, evaluating the changing dynamics of customer sentiment based on recent ticket archives and connecting new service requests with known issue resolutions from past interactions with different customers.