Applied Observability in practice

Observability refers to the capability of gaining an understanding of the inner workings of a system by analysing the data it generates. This involves collecting and connecting data from various sources, and enhancing it with relevant context, in order to facilitate troubleshooting and problem-solving.

The key elements of observability are:

• Logging: Collecting and storing log data from various parts of the system.

• Metrics: Collecting and storing numerical data about the system's performance and usage.

• Tracing: Tracking the flow of a request or transaction as it moves through the system, including timing information.

• Alerting: Setting up notifications or triggers when certain conditions are met, such as when a system's performance degrades or when an error occurs.

• Dashboards: Creating visualisations of the data collected from logging, metrics, and tracing, to aid in understanding the system's state and behaviour.

Applied Observability is the utilisation of observable data in a cohesive and holistic manner across various business functions. It differs from Observability in that combines multiple Data Layers present in the organisation to shorten the time between stakeholder actions and organizational reactions enabling proactive planning of business decisions. It is the fusion of typical infrastructure metrics and BI processes.

The primary goal of Applied Observability is to give stakeholders an evidence-based source of decision-making and provide instant feedback on any action.

There are several challenges that organisations face when implementing Applied Observability:

Data Collection

Collecting a large amount of data from different sources is a serious problem for an enterprise. On average SMEs can have up to 5-10 independent systems, whilst a bigger company can have 50-100. Integration with all of them and normalising their Data can be a challenge.

Data Labelling

Data labelling is the process of assigning labels or tags to data samples in order to classify or organize them. The labels can be in the form of text, numbers, or other forms of annotation.

Whilst It is possible to label the data manually or via a static 1:1 relationship - it becomes difficult very quickly as the number of Data streams increases.

Data Complexity

The complexity of the incoming Data often prevents its direct re-use and forces it to undergo complex and bespoke transformation to normalise the data and to extract the actionable analytics.

Data Privacy and Security

Organisations are responsible for the Data they collect. Keeping this Data secure and private is crucial for any company. 1 out of 6 companies that experienced a Data Breach never recovers.

New compliance legislation in many countries enforces special handling of the customer's Data and poses massive fines for mistakes.

Use-Cases and Action

The final and most challenging stage in implementing Applied Observability is creating a self-correcting and self-healing system by identifying the use cases for your observable data, taking action based on them, and measuring the feedback to create a loop.

Solving these challenges is a reason for companies like MetaOps exist. Hundreds of products offer integrations and end-to-end Data pipelines but so many businesses still have to fiddle with spreadsheets and bespoke scripts due to the everchanging IT ecosystem.

Get in touch if you would like to know more about how we deal with these problems.