Network monitoring web app

Overview

This is a monitoring and diagnostic tool for telecommunications networks. It is a B2B web app for internal use in large Telecom companies. The end user is the technical staff of these companies whose goal is to monitor the overall performance of the network and spot possible deficiencies. The app provides a wide range of insights to help them do so.

The objectives of the project

My role

In this project I was in charge of all the design aspects. Everything presented here was executed entirely by me.

UX Research
UX Design
UI Design

UX Design decisions

One of the project's difficulties was that it is highly technical and demands specialised knowledge. A lot of discussions and questionnaires with/to the stakeholders were required to understand the concepts involved, to clearly define the scope of the app and to translate it into a structured and insightful tool. The main feedback came from technical staff of existing Telecom clients. The communication with them was indirect through the Product Manager. The Product Manager had a Telecom engineering background, so he was able to provide valuable information as well. The communication with the backend engineers of the development team was also a very big part of the process, since the feasibility to display every piece of filtered data had to be checked with them.

The parameters that turned out to be the most basic and frequently required were placed on the most prominent position and with a single-click access (second top bar). These are the date/time picker, the network type (2G/3G/4G) and the roaming options. Supplementary to these, I designed a filter bar for adding extra filters (third top bar). Apart form the accessibility reasons, I placed these parameters in this prominent position to imply the fact that they are global configurations for all the sections of the app.

Big data app basic filters first level expanded Big data app basic filters second level expanded

Out of the huge volume of metrics we were able to show, I tried to extract from the stakeholders the most important ones, categorise them into three families (usage, connectivity, control plane) and present them as clearly and concisely as possible. The purpose of this process was to create a dashboard as the landing view of the app, presenting some quick and rough insights on the network.

Big data app main dashboard first tab
The initial dashboard view showing the Usage related metrics
Big data app main dashboard second tab

One of the main objectives of the technicians was to spot deficit nodes of the network. I approached this task similarly to code debugging, by isolating big parts of the network, check if they contain a deficiency, and if so, repeat the process by drilling down into that specific part, until the deficit single node. I decided that the best way to visualise that is through a tree map. The node parts classification was done both per node clusters (purely technical), but also per geographic areas.

Tree map visualisation first level
The tree map visualisation showing the node clusters of the network, ranked by the selected metric
Tree map visualisation second level
The tree map visualisation showing one level down the network elements
Tree map visualisation per geographic location first level
The tree map visualisation showing the network elements per gegraphic regions, ranked by the selected metric

Another important function of the tool was to relate network deficiencies to external events (e.g. fires, earthquakes, accidents). In order to do so, a visual overview over time was needed. So, in this section all the main metrics are presented as time-axis graphs in the selected timeframe. A compare functionality was also designed to help with spotting deviations from regular metric patterns (e.g. compare the current data volume to the respective of the same time one week ago).

Network traffic section, various metrics graphs
The "Network traffic" section
Network traffic section, various metrics graphs, comparison mode
The "Network traffic" section, with the "compare" functionality enabled on the Throughput panel

Another approach for spotting anomalies in a data set would be to simply collect the highest and lowest values of an object as per each of it properties. So, the purpose of the next section was to present these highs and lows, organised in five categories.

Top entries section, key apps tab in grid view
The "Key Applications" tab section as grid view
Top entries section, key apps tab in list view
The "Key Applications" tab section as list/table view
Top entries section, subscribers tab

Finally, since each view is highly customisable with many options for filters combinations and time frames, I suggested that each parameters combination can be "bookmarked" and stored in the last section of the app for quick access. There are also options to extract each dataset into CSV/JSON files and share a customised view.

UI Design decisions

Some noteworthy UI decisions:

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