Analytics/Limn/design

This page compiles design information to improve some aspects of Limn. Feel free to provide ideas, questions and comments at the talk page.

Visualisation creation workflow
Limn allows to create visualisations of different kinds of data. Datasources represent a type of data (e.g., number of active editors), and contain more specific metrics that can be added to a visualisation (e.g., active editors from India, active editors from Asia, total of active editors...). Creating a new visualisation involves deciding which metrics to display and making some additional adjustments about the way they are displayed.

Users, goals, and scenarios
A target user for Limn:
 * Lacks analytics knowledge
 * Makes important decisions based on data

The user goal is to get quickly the visualisation they need. Some possible scenarios:
 * I want to view "daily and monthly mobile visits for South America countries" to compare the growth of hose communities (daily and monthly visits being separate datasets).
 * I want to view the number of active editors (on mobile and desktop) for each Spanish-speaking country as separate visualisations that I can share with the Chapter of each country.
 * I want to combine my usual visualisation (which I use to report to a team mnthly), with this new data about rain, to check if users edit more when they stay at home because of the rain.

Current status
Two sessions were organised to observe the current use of Limn regarding the creation of new visualisations. The following observations were made:
 * Metric selection lacked flexibility. Selection of metrics was done in a one step. Users select datasets for which all metrics are added to a common list. the users had little options to filter this list ("remove" or "only" to remove the rest).
 * Cross-concept selection was not supported. Users focusing on information about some countries across different datasets, had to select the same countries again and again for each dataset.
 * Some context information was not provided. there was no easy access to the recent values, or the proportion of a specific value with respect to the total.
 * Not possible to export the data from the visualisation. Once a visualisation is made, it represents a specific view of the data across different datasources. Currently users could not export this view of the data in a tabular form for additional analysis.
 * The tool was perceived as slow. At different points it was not clear whether the system was processing information or not. In addition to the possible performance improvements, there is an opportunity to improve the perceived speed, and to better communicate the status system on waits.

Design principles

 * Allow direct manipulation. Avoid indirection steps that feel as configuration. By affecting directly the visualisation, users will be encouraged to explore since the effects of their actions are clear.
 * Be flexible. We don't know beforehand the kinds of data the user will be dealing with and the ways they want to combine it. The tool needs to provide generic mechanisms to effectively play with data in different forms.
 * Accommodate complexity. Make the creation of simple visualisations simple, and the creation of complex visualisations possible. Supporting complex scenarios should not add additional complexity to the creation of simpler visualisations.

Create visualisation
Ideas for the design:
 * Live-updated visualisation. The visualisation is the central element of the process and it should reflect the user actions as they happen.
 * Adding metrics can be an iterative process. Users are able to open a panel to add new etrics at any time. The panel allows the user to still view the visualisation, so that when a metric is added (or removed) the result is reflected in the visualisation. More details about the