The goal of this project is to understand how analysts make sense of data using data visualisation and/or machine learning. ‘Sensemaking’ is a bit broader than what is usually known as ‘data analysis’: as shown in the diagram (the ‘Pirolli-Card model’) it include:
- Searching for relevant data
- Extracting useful information
- Form of an understanding of the important factors and their relationships
- Forming and testing hypothesis
- Comparing different options and making decisions
- Presenting the results and decision process to others.
For example, a sensemaking task can be ‘finding the best camera under £500 for baby photos’, then the steps above become:
- Searching for information about camera, such as the reviews/recommendation and camera price/pixel number.
- Find the information that is relevant to you: matching use case (such as baby photos) and price range (no more than £500).
- Understand which factors are important to you (such as sharp photo) and how is this affected by other factors (such as large aperture).
- Use the understanding to form and then test hypotheses what types of cameras may meet your requirements, such as ‘will a phone camera be good enough or I need to get a mirror less or DSLR?’
- Then you will need to research and compare different camera models and make a decision
- Finally you need to communicate the results to others (such as your partner) and convince them this is the right choice.
There are many other sensemaking examples in our life, such as planning a holiday and selecting a university/degree to study. There are also many, many applications in business, medical, defence, and other industries. Academic research itself is also sensemaking.
The fundamental issue is that most of the sensemaking steps are done manually without support from specialised tools. This make it a bottle neck in Big Data analysis (for example the dozens Chrome tabs shown below).
The project aims to design and develop visual analytics tool to support all the sensemaking stages. Some progress has been made such as SenseMap and SensePath, but there is still a lot of work to do to cover all the stages of Sensemaking and in different use cases (the tool designed for camera shopping will be quite different from the one design for machine learning research).