When monitoring your network traffic, you rely on tools to provide precise, actionable data. But what if some tools “lie” – not out of malice, but due to hidden methodologies that mask the truth? Let’s uncover how certain practices can lead to inaccurate traffic analyses.

A common discrepancy arises from how monitoring tools handle data over extended time periods. In order to be analysed, data first needs to be divided into buckets: the volume of traffic flowing through a network, measured in bytes, is collected in groups in order to process it. A bucket size is determined by time, so the size of the bucket is the amount of time in which the traffic data was collected, e.g. 5 minutes, 60 minutes, 24 hours, etc. Generally speaking, the smaller the bucket size, the more accurate the data analysis possible, for reasons which will follow.

Many tools use larger bucket sizes for long-term queries, which aggregate data into broader averages. For example, data might be processed on a daily basis (24 hours). While this might simplify storage and traffic visualization, it often leads to inaccurate, lower traffic values, masking critical peaks and underestimating actual usage. In other words, traffic peaks that would otherwise have been visible are averaged out, leading to smaller average values. 

This perceived accuracy can result in:

• Bad forecasting of capacity needs: Decisions based on underestimated traffic values can lead to insufficient resources, causing bottlenecks during peak times.
• Missed critical events: Outages or traffic shifts that create temporary spikes might be hidden, leading to incomplete analyses.

At BENOCS, we’ve designed our system to ensure you always have a clear and accurate picture of your network traffic. Here’s how we address these issues:

1. User-centric design: We work closely with users to understand their requirements. Long-term queries are often used for capacity management, where maximum utilization is critical. Additionally, in cases of outages, the traffic shifts must also reflect the maximum traffic seen during such events. As a result, BENOCS Analytics shows by default what the user expects: the maximum peak of any given day.
2. Transparency: BENOCS displays the bucket size and aggregation method directly in the time series. 
3. You’re in the driver’s seat: We give users full control to adjust the parameters of their network’s traffic collection to suit their specific needs at any particular time. In our close collaboration with our users, we have seen use cases for various aggregation methods, so are dedicated to giving our users the ability to easily adapt their chosen parameters on the fly.

We want to bring network analytics to everybody – from network engineers to marketing teams. For this reason, we identified the most expected behavior of these graphs as crucial default behavior. At the same time, we ensure transparency by displaying how these values are derived, leaving no room for guesswork, and enable our users to make adjustments to these default settings if necessary. This ensures that BENOCS users:

1. see what they expect to see,
2. understand what BENOCS did, and
3. can customize as needed.

By combining intuitive defaults, transparency, and flexibility, BENOCS delivers the tools you need for accurate and actionable insights into your network.

Interested in a demo? Let us know!