AI Applied Observability in detail

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The rise of digital service and technology has revolutionized the way businesses operate. As a result, companies are increasingly turning to advanced technologies like applied observability to ensure that their operations are running smoothly and efficiently. Applied observability is the practice of collecting and analyzing data from various sources to gain insight into the behavior of complex systems. It can be used to detect and diagnose problems, identify opportunities for improvement, and provide greater visibility into system performance.

Manufacturing sector

In the manufacturing sector, applied observability can be used to understand the process of manufacturing from raw materials to the finished product. It can help manufacturers identify bottlenecks in production, detect inefficiencies, and ensure that the product meets the desired quality standards. By collecting data from various sources such as sensors and machines, manufacturers can identify areas of improvement, reduce costs, and increase productivity.

Retail industry

In the retail industry, applied observability can be used to monitor customer behavior. By collecting data from various sources such as customer interactions, website analytics, and transaction records, retailers can gain insights into customer preferences, buying patterns, and trends. This can help retailers better understand their customers and ensure that their products and services meet the needs of their target market.

Software development

To better understand what features consumers care about, what they struggle with, and how to enhance the service, it is important to provide observable systems that can be monitored and analyzed by both business and IT stakeholders. To learn more about what features customers care about, what they have trouble with, and how to improve the service, it is important to have systems that both business and IT stakeholders can see and analyze.

A further benefit of observability is the new method it provides for handling organizational transformation in the face of growing complexity. However, observability depends on instrumenting processes with the appropriate data and control mechanisms to allow proactive and anticipatory measures, as opposed to monitoring business applications and processes, which is inherently reactive..With the help of Applied Observability, business and IT decisions can be made based on a consolidated view of telemetry data from many systems. Leaders in IT must go beyond relying on observability to ensure system stability and instead create an observable digital enterprise.

Some instances are:

  • Adoption rates of both new and old features by customers show what might be causing them to leave.
  • Information on the characteristics of both active and inactive users
  • An analysis of how customer satisfaction has changed over time in connection to the quality of service provided.
  • Since observability is “built-in,” it can give detailed information about how business systems and operations work.

How effectively the internal state of a system may be deduced from its exterior output is quantified by the concept of “Context Observability.” The idea of “applied observability” goes even further with this idea. It gives precise visibility not just to business processes, but also to infrastructure, data, networks, and security. Observability data makes it possible to improve innovation, resilience, consumer adoption, engagement, and experience, as well as to make things more flexible and reliable.

Due to a lack of information, businesses have historically struggled to make informed plans and learn from their mistakes. But now, we have the exact opposite problem: we have so much data that it’s impossible to tell the important signals from the background noise. By collecting, correlating, and analyzing observability data from across many technological layers and domains, and then making that information accessible to both business and technical roles, applied observability may help guide both human and automated decision-making.

Gains from AI Applied Observability Should Be Extended to Many Functions

The goal of applied observability is to fit observability data to the needs of different business and IT functions. For example, application teams tasked with making the user experience better could use data about how easy it is to observe consumers.

Similarly, I&O teams are dependent on IT observability data including events, logs, metrics, and traces in order to effectively manage service levels. In the same way, accounting teams that are in charge of keeping an eye on operational costs would benefit from having more The advantages of observability can only be fully realized when telemetry is used to connect individual pieces across the whole topology of business and IT systems.The benefits of observability can only be fully realized when telemetry is used to connect the different parts of the business and IT systems across their entire topology. 

Observability categories

  • The capacity to see data
  • The ability to monitor infrastructure components such as nodes, switches, servers, storage, and communications equipment.
  • Access to information about all parts of an application, such as its services, APIs, databases, open source, and 3rd party dependencies
  • Transparency in Security
  • Product-related business decision making
  • When applied to problems in business intelligence, observability’s use is greatly expanded. In order to monitor how many people are using a newly launched feature and how often, for instance, you need to have a constant eye on the user experience and use trends. Having access to observability data that can be used to answer open-ended questions is a boon to the organization’s technical and business stakeholders, as it allows them to fill in knowledge gaps and learn something new.

With the help of applied observability, observability becomes a company-wide initiative and standard operating procedure.

Applying Observability at Critical Inflection Points

Observability data is useless unless it is used to improve judgment at various “points of value.” Points of value are the nodes in a company process where service quality, customer satisfaction, and strategic decision making all converge. That’s why it’s so important to make observability data accessible to both the teams pushing the change and the teams that will be affected by it.

The use of Observability aids in the discovery of inconsistencies between the expected and observed behavior of systems.

End-to-end observability is important for making sure that systems are reliable and resilient as their dispersed nature makes them more complex. The failure modes and consequences of individual components in distributed systems are difficult to foresee (as a whole). Customers increasingly rely on digital services as their primary and, in some instances, only point of contact, which drastically raises the need of digital resilience. For resilience measures like MTTR and RTO to be optimized, there must be enough observability data for troubleshooting and finding the root cause.


Apply techniques like observability-driven development and make observability a fundamental part of application design. Apply observability throughout the whole software development process, just as you would with security.

Discover, learn, and improve the use of IT services to enhance the user experience of digital goods and services by instrumenting business operations and applying observability to all layers of the technological stack.

To turn system performance into business results, you need to improve the team’s knowledge and skills in design and architecture for observability. If interested, contact our sales team in order to estimate your project.

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