Logging platforms are valuable business commodities but, if used inappropriately, they can produce large and complex piles of impenetrable data.

The structure of your log data impacts its ability to be understood, consumed and correlated with other data for in-depth analysis. Here are our three top tips to ensure your log data is structured in an accessible format:

1. Don't dive in head first

There may be thousands of logs available for collection and further analysis within any system, but you need to make sure you fully understand the requirements of this log data. What will it be used for and why?

It is important that your business comes up with a range of user cases and questions that need to be addressed by the log data. This should be done before any logs are added to the system.

2. Be aware of false positives

All logs are susceptible to data replications, where repeating keys are present in sections of a log. It can be easy to confuse one key with another key that may be intended for a full text search or query. This does not pose a problem for the log software, but can cause confusion with the humans interpreting the data - leading to false positives. It is important to be aware and approach log data with caution where you think there is potential for such false positives.

3. Choose the right tool

It is important to select an appropriate and versatile logging platform to better structure and capture your log data. Our software at Logit.io allows users to search, create alerts and get live stats on all the apps and servers within their system. It also allows a range of audiences, from operations to development to management, to structure and extract the data crucial to their business role. For more information on choosing a logging tool, please click here.


Before you begin any log data gathering and analysis exercise, it is important to have an idea of what you are going to do with that data. What questions regularly need addressing by the business? How will you report on the log data in an understandable and consistent manner to the rest of the company?

The majority of log analysis platforms are pretty resilient and can take whatever you throw at them. But, by carefully selecting what you feed them, you can optimise the efficiency of queries, produce meaningful reports and ultimately avoid a lot of frustration.