The first step in processing data is getting that data. Your best case scenario is where you are given that data. But quite often, the data you need is not given -- or even if it is, it's not in the format you want. Once you have the data, you need to analyse it. A lot of insight is found in finding averages and trends by segment. Answering "Which group is the best?" is half the work of most analysts.

The key concept in data visualisation is using code to generate visuals from data. This makes the process repeatable. We use templates that will convert data into a visualisation. When the data changes, the template is re-run to produce the revised output. We will generate our visualisations using SVG -- a vector graphic format -- due to it's widespread browser support and ease of manipulation.

Gramener's visualisation server simplifies the creation of data visualisations. It comes with a library of pre-packaged visualisations that can be put together into complex dashboards.

A good aesthetic sense is key to creating visualisations. Colour, typography and layout can be used to great effect in increasing the impact of your design. Interactivity also plays a strong role in this, and can enhance the speed at which the information is absorbed.