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Top Data Challenges How to make most out of your ITSM data

ITSM organizations generate a deluge of digital information. ITSM staff interact directly with end-users to talk about the most pressing issues and feedback on your product features and services. ITSM also collects data from digital solutions that serve some level of process automation for individual and isolated functional domains. This wealth of data promises useful insights that can guide ITSM workflows based on concrete information. However, making sense of the data generated across distributed and isolated sources is a growing data challenge that many ITSM leaders struggle to overcome.

 

Let’s review the core data challenges facing ITSM leaders who have access to information but remain unable to manage it effectively and therefore fail to derive insightful knowledge from ITSM data. These challenges include the sheer volume, variety and velocity of ITSM data, inadequate integration and siloed knowledge bases due to SaaS technology adoption, inaccurate and inadequate descriptions of the underlying problems due to traditional automation, limited access to information that can optimize ITSM workflows from a business impact perspective, concerns around data quality management and governance, unstructured data, multi-language barriers, and enterprise jargons and lingo.

 

These data challenges can significantly hinder an organization’s ability to deliver high-quality services and meet customer needs, which is why it is crucial to leverage hyperautomation intelligence to extract valuable insights and drive operational excellence.

Trends & Research

1. Volumes, Variety and Velocity:

ITSM data is both an opportunity and a challenge: it comes in many forms, from multiple sources and in real-time, and therefore contains unprecedented insights into customer behavior and expectations, ITSM operations and market conditions. The same attribute is also the greatest big data challenge: the sheer volume, variety and velocity of ITSM data renders traditional analytics and automation solutions ineffective for extracting that knowledge. Managing the data for quality, compliance and security only creates additional burden on the limited ITSM staff.

2. SaaS Technology Adoption:

Leaders invest in multiple technologies to serve the diverse needs of their ITSM organization. The global ITSM solutions industry is expected to reach $9 billion in the next five years – without accounting for the complementing enterprise IT solutions and services such as cloud computing and AI solutions. Increased adoption rates means that organizations are investing in multiple solutions that do not integrate adequately and maintain a siloed knowledge base. According to recent research, an average business organization with fewer than 100 employees operates over 100 SaaS applications. These tools hold critical insights into technology utilization, service delivery, finance, user experience management and other functional departments.

3. Traditional Automation:

Traditional Robotic Process Automation (RPA) allows ITSM users to automate independent processes: for instance, tickets for certain categories can be programmatically routed to specified resolution teams. In practice, these categories can be both inaccurate and not adequately descriptive of the underlying problems. While ITSM leaders access information on ticket handling and queues, the data only describes ticketing workflows that follow hard-coded automation rules with no contextual knowledge on impactful metrics such as evolving ticket workload, use expectations and business impact of ticket routing decisions.

4. Finding Business Focus?

The disconnect across hierarchical levels of the ITSM organizations means that front-line ITSM staff and resolution teams may not have access to information that can optimize ITSM workflows from a business impact perspective. Front-line staff may be expected to resolve issues based on metrics such as Ticket Volumes and First Contact Resolution, but what about metrics that capture business impact such as Mean Time to Resolve (MTTR) and Mean Time to Failure (MTTF). Since ITSM data and reporting is maintained in silos, depending on the distributed set of tools employed by various members of the ITSM organization, front-line staff and engineering teams are not always able to align ITSM workflows and ticket routing decisions based on business impact.

5. Digital Transformation is not enough:

ITSM organizations combine the growing volume of data with technology advancements to achieve operational excellence. Some of the key inhibitors to achieving these goals include inadequate risk management, data quality management, governance and controls. How can ITSM leaders trust that the data generated by multiple digital solutions independently portrays a consistent image of their ITSM workflows? Migrating from a traditional ITSM process management approach to digital solutions is not enough: decision makers need data that is reliable, complete, high quality and in line with the organization’s governance, risk management and data quality policies.

6. Unstructured Data:

A significant portion of ITSM data is unstructured data, such as free text in ticket descriptions, ticket resolution and feedback from end-users. This makes it challenging for traditional ITSM solutions to extract valuable insights from the data, as they often rely on structured data to generate reports and analytics. Unstructured data also presents challenges for natural language processing (NLP) and sentiment analysis, which can be critical for understanding customer feedback and identifying areas for improvement.

7. Multi-Language:

Enterprise companies have support agents in different regions who speak different languages, creating a challenge for having a unified overview and bird’s eye view of all the data in one common language. This can lead to difficulties in identifying global trends and patterns in customer behavior, as well as hinder the ability to derive actionable insights from the data. The ability to accurately translate and analyze data in multiple languages is crucial for global ITSM organizations to optimize their operations and deliver high-quality services to customers worldwide.

8. Enterprise Jargons and Lingo:

ITSM data is often riddled with enterprise jargons and lingo specific to the industry or company, making it challenging for traditional analytics and automation solutions to derive valuable insights from the data. This requires specialized knowledge and expertise to understand the data and identify key trends and patterns. Without a proper understanding of the language and terminology used in the data, ITSM leaders may miss critical insights that could have a significant impact on the business.

Conclusion

In today’s rapidly evolving digital landscape, ITSM organizations need to be able to extract valuable insights from their data in real-time. With the sheer volume, variety, and velocity of data generated across multiple sources, traditional analytics and automation solutions are no longer effective in providing meaningful insights. That’s why there is an urgent need to use hyperautomation intelligence to make sense of ITSM data and drive operational excellence.

Hyperautomation intelligence offers a comprehensive solution that can capture, process, and analyze data from multiple sources in real-time. It allows ITSM organizations to make data-driven decisions based on accurate intelligence and actionable insights. By addressing the data challenges outlined in this article, hyperautomation intelligence can help ITSM organizations unlock the full potential of their data and drive operational excellence.

In short, hyperautomation intelligence is the key to unlocking the true value of ITSM data in today’s fast-paced and complex digital landscape. Don’t let data challenges hinder your organization’s ability to deliver high-quality services to your customers. Embrace hyperautomation intelligence and turn your ITSM data into a valuable asset that can drive growth and innovation for your business.

 
 
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