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Monitoring is fundamental to every IT process. However, with recent advancements in technology, businesses no longer require human intervention or a manual workforce to monitor the performances of various services. One can easily use automation tools for quick detection and remediation of issues. However, while these solutions have been designed to detect certain types of issues, they may not be able to isolate an issue. This usually happens if adequate metrics are not available. Even with the implementation of remote infrastructure monitoring services and other monitoring tools, businesses may continue to lack observability. This is where cardinal data is essential. It will help to ensure that metrics are sufficient to identify, isolate and even mitigate all problems that may slow down services or affect the system as a whole.

Understanding High Cardinality for Observability

Every business involved in the IT sector needs to monitor specific aggregate signals or metrics. This ensures the streamlining of business processes and helps maintain a consistent quality of the services provided. Businesses usually use IT infrastructure managed services along with monitoring tools. But it is crucial to understand if these services and tools are actually effective. Now, before using any monitoring tools to increase observability and visibility, one needs to understand the significance of data dimensions and cardinality.

Data dimensions are specific attributes related to the available data. They are in numeric form and are essential facts that one should monitor. These data dimensions will be related to the business and, therefore, will be in the interest of business development. On the other hand, cardinality is the set of specific unique values that are available within the data dimensions. There are two kinds of cardinal data: low cardinality and high cardinality.

High cardinality is what ensures absolute observability by providing metrics that can assist in effective detection and remediation. On the other hand, low cardinality has limitations and cannot assist in promoting observability. For complete observability, the available tools will need to keep track of all transactions across the entire stack. Each transaction will also have to be augmented with infinite keys to quickly understand the correlation between the root causes and the errors. However, this rarely happens, primarily because various monitoring tools do not support high cardinality data.

More often than not, as the cardinality rises, the volume of the data will also increase. Therefore, the process of computing and storing the data will become even more complex, resulting in low observability. However, if monitoring tools and platforms are powerful enough to scale high cardinal data, then this issue will not arise.

Due to the limitations of monitoring tools used on high cardinal data, there can be various issues. One of these is the limitation on the data dimensions. Operations teams will not be able to extend the number of dimensions available. Another issue will be that instead of actionable data, the team may be stuck with just aggregate or sample data that will not provide adequate insights. The storage duration will also reduce significantly, and there will be a limitation on the tags that one can use. If the limit exceeds, then the punitive costs will increase as well. If these issues persist, then the observability will decrease significantly. Therefore, high cardinality is essential. It will ensure that there is not just a monitoring tool that detects anomalies. With high cardinality, there will be data granularity which will be able to determine the root cause and reduce errors quickly. Once this is done, observability will begin to increase, and thus the business processes will become more efficient and optimized.

Why is High Cardinality Essential for Observability?

Both low cardinality and high cardinality are essential for observability. However, high cardinality is more crucial. Low cardinality data works the same way as monitoring tools and helps to identify potential anomalies. But it is not sufficient to provide an effective solution for the issue and, thus, cannot ensure the required observability. This is where high cardinality comes in.

High cardinality data is often concentrated on customer behavior, response, and information. High cardinality data is collected through customer data like App IDs, specific SQL queries, hosts, and different processes. Analyzing such data helps determine which issue correlates to which customer. High cardinality is also instrumental in identifying the location of the problem and the root cause. Thus, it is crucial for maintaining observability. The observability suite or solutions will be focused on determining the cause of errors that may affect the transparency of business processes. Therefore, it requires more than just specific metrics. High cardinality provides the essential insights that go beyond the monitoring metrics and help to ensure increased observability across platforms.

The IT sector and its infrastructure have become very complex, and therefore, operations teams need relevant data that can provide actionable insights. High cardinality can ensure this. It helps to identify different failure modes across microservices and multiple systems. Businesses will continue to evolve, and newer processes will become relevant. This is why observability needs to increase, and it can only do so if high cardinality data is available.

High cardinality will also ensure observability in terms of transactions. As the IT environment evolves, companies will have to deal with different transactions. Every transaction will have a unique set of parameters and, therefore, need to be analyzed separately. If granular data is available, it will be easier to detect the anomalies within individual transactions and increase observability.


Observability is challenging to achieve without high cardinality. In the absence of high cardinality, observability and visibility will be low, and that can impact the condition of the services available. If this happens, the user experience will become poor, and thus, the business will suffer. Therefore, IT professionals and operations teams must concentrate on using and generating insights from high cardinality data. It will help get maximum benefits from the processes and services.

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