Introduction:

Ideas are born out of the need to solve a problem that consumes time and material resources. Similarly, the concept of observability was born out of the need to solve the issue of grey areas in the concept of monitoring. Organizations concentrating on next gen technologies will grow large data sets and as the volume of data continues to grow, organizations will struggle to manage the health and quality of their data sets.

With the adoption of multiple hyperscalers in a single infrastructure, Microservices, containerization, serverless infrastructure and with data platforms getting connected across the infrastructure to enable efficient LLMs, there are multiple “Unknown Unknowns” which the monitoring tool cannot capture. This leads to a very inefficient monitoring environment that fails to capture the alerts and warnings or even does not provide the complete insight into the issue and hence the support becomes inefficient. The concept of Observability provides the “Eagles Eye” into the enterprise.

While monitoring is about the functionality, availability and performance of the application and microservices, Observability is about the status of the transaction within the microservice by performing a realtime analytics of the metrics, traces and logs. This conquers the “unknown unknown” and provides a 360 degree view of the internal state of the application or microservice.

Is this relevant for Digital Workplace?

The answer is Yes. Observability is about building trace level visibility into every layer of the business across the value chain providing greater insight into issues and user experience, and creates more time for more strategic initiatives, instead of troubleshooting issues. This gives a greater visibility into the functionality of the systems that constitutes digital workplace.

The concept of Observability will be a big push towards the strategic initiative of moving from resolving failure demands to addressing business value demands. When the industry is moving from the standard knowledge management and machine learning to deep learning and LLM based knowledge, The analysis of the internal state of the user experience, application and device performance using observability will increase the efficiency of the resolvers to create more efficient and effective automation use cases.

This in-depth insight will increase the efficiency of the auto-heal, Bot based resolutions and also I also foresee increase in accuracy of the content of the knowledge, SOPs and troubleshooting manuals accessed by bots, users, resolvers and business.

This brings our vision to reality where we foresee that in the future, The Service Desk will be the only human point of contact who will be involved in troubleshooting irrespective of the criticality of the issue and the level of skillset that would be needed to resolve. The L2 would be fulltime concentrating into analysing the output of the observability platform and providing innovative resolution in DevOps ways of working to be truly pre-emptive and preventive. They become the service reliability layer pushing the resolution to the left and also ensuring elimination of the incidents at the alert stage itself.

This service reliability layer draws synonym to the pacemaker of a human heart which ensures that the heart does not skip even a beat and every part of the human body receives the right amount of blood needed. this team will ensure that the business and the users receive the right level and the right quality of service and the overall productivity does not skip a beat.

What is the Future?

As someone close to the industry and observing the changing trends of the digital workplace, I see businesses defining the ways of working and IT is realigning to defining the operating model to a more business value model with the fusion teams playing the core and complex value delivering role. Definition of the business value model need deep insight into the IT functioning and actions that are impacting the financials of the business in terms of TCO and ROI.

Implementation of the observability into the digital workplace will help us ask “Why” a system is not working whenever there is an experience issue and will provide useful insight into the data gathered and understand the overall behaviour of the end user environment. These deep analytics will provide greater insight for the business to contextually define the business values that they want to extract from IT.

While we understand that observability is popularly looked for effective use cases in the area of applications and distributed infrastructure, We also are sure of the effective use cases available to enhance user experience. The answer to the “Why” for any user impacting issue enhances the accuracy of the auto heal or automation resolution provided. The observability dashboard in integration with the analytics engine will have more detailed trace level data correlated and provided to the resolvers ensuring faster and accurate resolution.

What are the Use cases?

Additional Points:

• Efficient AI Integrated Operations: Data flow is getting integrated across the environment for efficient functioning of AI products such as Co-Pilot and users will have the choice of device and location to operate. The digital workplace will be a more dynamic environment, and observability will be the way forward to tame it and ensure that the performance and availability of the same exceed expectations.
• User Personas: With the combined data from persona analytics from the management layer and observability data from their day-to-day job function and consumption pattern of IT, greater insight into the needs and challenges of the users will be provided. This will assist in accurately identifying user contextual influencers of experience, which is a significant advancement from today’s definition of generic business unit-specific influencers. The continuous observation of user IT consumption and deep analytics of issues causing loss of productivity will ensure the dynamic nature of the list of experience influencers.
• Efficient Monitoring Contributes to Sustainability: While monitoring answers the question “Is my system up?”, Observability answers, “How long is it up?” and if down, “Why is it down and what can I do to ensure the issue is not recurring?” If the system is up and idle, it can be proactively shut down.
• Autoheal: This marks a shift from standard analytics-based auto-heal to a deep and dark analytics-based self-heal which eliminates the human interface completely and reduces backlogs considerably. Dark analytics involve analyzing raw data buried in texts, tables, and figures that businesses acquire from various business units. The deep and dark analytics will help the service reliability layer automatically identify and define the metrics across the business value chain.

Conclusion:

As the need for generative AI, Dark analytics, and deep learning increases, we will also see the necessity of observability increasing. We will closely monitor and define the use cases as the Digital Workplace trend matures or changes.