Bringing activity data to life

This guide discusses the role of visualisation tools in the exploration of activity data.
The problem
Activity and attention data is typically large scale and may combine data from a variety of sources (e.g. learning, library, access management) and events (turnstile entry, system login, search, refine, download, borrow, return, review, rate, etc). It needs methods to make it amenable to analysis.
It is easy to think of visualisation simply as a tool to help our audiences (e.g. management) ‘see’ the messages (trends, correlations, etc) that we wish to highlight from our datasets. However experience with ‘big’ data indicates that visualisation and simulation tools are equally important for the expert, assisting in the formative steps of identifying patterns and trends to inform further investigation, analysis and, ultimately, identify (combinations of data) that enable the recommendation function that you need.
The options
Statisticians and scientists have a long history of using computer tools, which can be complex to use. At the other extreme, spreadsheets such as Excel have popularised basic graphical display for relatively small data sets. However, a number of drivers (ranging from cloud processing capability to software version control) have led to a recent explosion of high quality visualization tools capable of working with a wide variety of data formats and therefore accessible to all skill levels (including the humble spreadsheet user).
Taking it further
YouTube is a source of introductory videos for tools in this space, ranging from Microsoft Excel features to the cloud based processing from Google and IBM to tools such as Gephi, which originated in the world of version control. Here are some tools recommended by people like us:
Additional resources
To grasp the potential, watch Hans Rosling famously using Gapminder in his TED talk on third world myths -
UK-based Tony Hirst (@pyschemedia) has posted examples of such tools in action – see his Youtube channel - . Posts include Google Motion Chart using Formula 1 data, Gourse using EDINA OpenURL data and a demo of IBM Many Eyes .
A wide ranging introduction to hundreds of visualisation tools and methods is provided at