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GR1 - User and Task Analysis

User Analysis

Analyst’s Notebook (ANB) has an extremely wide range of users. The representative users that we interviewed are from the sectors of academia (MIT), industry (IBM), and military (U.S. Army), but all were working on problems related to government and international development.

Within the scope of government-related users, we learned that ANB has two classes of users that use the program in very different ways:

  1. Senior Analysts
    • Age: 30-40 years old
    • Experience: ~15 years, very highly experienced in their field and in using ANB
    • Education: most have Bachelor's degrees, many have at least one Master’s, some have PhD’s. Fields of study are mostly social sciences (i.e. sociology, anthropology, psychology). For those with two Master’s degrees, one of them is usually technical (i.e. engineering, math).
    • Usage: “use it like they’ve always used it.” Senior analysts are expert users of ANB and have become very familiar with it over the years, so they are not accustomed to changes in UI. They tend to prefer seeing relationships as text data fields or in tabular format, and frequently use queries to search within the data.
  2. Entry-level government contractors, enlisted military, or researchers
    • Age: early- to mid-20’s
    • Experience: not much experience with data analysis and ANB, if any
    • Education: recent college graduates. Bachelor’s degrees.
    • Usage: very visual. These users have a fast learning curve, and often rely on intuition to navigate the UI. Rather than see data as tables or text fields, they prefer to visualize relationships, i.e. as “honeycombs of influence” in a social network (people with influential relationships will be close to each other in a honeycomb visualization). Big emphasis on visualization of data and intuitive GUI.

Both of these user groups share the following characteristics:

  • Frequency of use: daily. They "live it.”
  • Hardware: high-end laptops with plenty of memory and processing power. Usually Dell Precision workstations.

The users emphasized that efficiency is the most important aspect of UI design for them. Many of the users are familiar with both ANB and Palantir, and said that Palantir's UI is superior in both aesthetics and efficiency. They complained that ANB's UI requires too many clicks and mouse movements to perform simple, common tasks.

Task Analysis

With the back-end and database support provided by IBM, we will focus on designing effective ways to visualize data in a way that allows users to see relationships between large amounts of data efficiently. Standard data visualization tools are available from open-source libraries such as processing.js. We will select a few visualization methods (e.g. web, map, graph) to focus on, geared toward understanding the data sets for WiFi in Africa. Some high-level tasks include:

  1. Seeing relationships between data sets: It should be easy for users to modify one aspect of the visualization and see the effects in realtime.
    1. Manipulation: This can include tasks like dragging and dropping, zooming, and panning, as well as changing the method (e.g. web, map, graphic), which will allow the user to see information in the most efficient way.
    2. Optimization: This will allow the user to optimize and manipulate certain data given certain restraints.  It is similar to querying a database, but expressed in a more visual way.
    3. Realtime: The user should be able to change the values of one datapoint, and see how that effects the entire visualization in a meaningful way.  Our users have expressed a need for this to replace the tedious methods of querying and optimization that they currently use.
  2. Sharing and collaborating on visualizations: Analysts would like to be able to share their data and visualizations with superiors and co-workers as well as collaborate on certain projects.
    1. Collaborating: Users often need to annotate data, making notes both for themselves and to share with others. They will need the ability to insert text comments, highlight parts of the visualization, etc. We will be in communication with our representative users to pinpoint the features that will be most useful to them.
    2. Data Sharing: It should be easy for users to choose the source of data for a given visualization, as well as to share their data set with another user.
    3. Visualization Sharing: Users should be able to share their visualizations easily without having to export and import large amounts of information.
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