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GR1 - User and Task Analysis

User Analysis

Our Analyst’s Notebook (ANB) has an extremely wide range of users. The representative users that we interviewed are taken from from the sectors of academia (MIT), industry (Boingo, IBM), government (DOD), and academia (MIT). In general, users want an interface that is intuitive, interactive, and efficient. Users depend on data visualization to draw conclusions from complex data and make informed, time-sensitive decisions, so safety is an important usability aspect. Because users are generally experts with the software and depend on it for their day-to-day work, we may often choose efficiency over learnability when there is a tradeoff.

Industry: IBM is seeking an improved user interface for Analyst's Notebook, one that can compete with the more user-friendly Palantir software. Boingo needs to analyze data to build its new WiFi network in Africa, and needs a good way to visualize a combination of data sets across the continent. In general, it is important that analysts in industry can use data visualizations in reports to managers and higher-level executives.

Government: We will be collaborating with U.S. Army officers who use both Palantir and Analyst's Notebook for mission-critical decision making. One of the problems they face is the inefficiency of using two different tools, as their supporting databases are incompatible. They wish to use the superior computational tools of Analyst's Notebook, but also have an interface that can more clearly and efficiently visualize data trends.

Our representative users are from a civil affairs unit, rather than combat. They will be primarily concerned with promoting development and preventing conflict in third-world countries. They are trained to use the software before deployment, so they will have time to become expert users. Officers also analyze data to plan their missions. For soldiers deployed overseas under stressful and life-threatening conditions, efficiency is key.

Academia: Data analysis is an important part of research in many fields. In particular, research about international development in Africa has clear ties to the above mentioned users in industry and government. Our representative user from MIT is a graduate student working on development in Africa, who will use the interface we design to help advise our industry and government users.

and military (U.S. Army), but all were working on problems related to government and international development. These problems include:

1. Criminal investigations (FBI): tracking down people responsible for very elaborate, massive-scale fraud/scams; crime/drug rings, Russian mobs, etc.
2. National security & diplomacy: figuring out relationships between international leaders, particularly "who is influencing who." People-centric analysis, social networks.
3. Civil affairs: making predictions based on many different factors and data sets. Events-based analysis.

Within the scope of government-related users, we learned that ANB has two main 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.

Lessons learned:

The users emphasized that efficiency is the most important aspect of UI design for them. They complained that ANB's UI requires too many clicks and mouse movements to perform simple, common tasks. Users were very enthusiastic about the use of sliders (rather than buttons) to adjust parameters, such as time or resources. The overall movement is toward a more efficient, intuitive, slick design with primary emphasis on visualization. In other words, the ANB interface is shifting toward the younger class of usersOne important design consideration is that different users want to see different levels of granularity in the data. For example, an Army major would want to see more technical details while a colonel may be more concerned with qualitative trends; similarly with an engineer versus a top executive in industry. In academia, researchers may want to look at data on many levels to draw connections between data trends.

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 The user should be able to change the values of one datapoint, and see how that affects 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. 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 The users' main high-level tasks include:

1. Optimization: The overarching common goal among users of ANB is to discern and understand complex relationships within data sets. This is one of the users' main goals in analyzing data. Users want to optimize and manipulate certain data given certain restraints. It is similar to querying a database, but expressed in a more visual way.
2. ManipulationSeeing relationships between data sets: It should be easy for users to modify one aspect of the visualization and see the effects in realtime.  Users need to see information in the most efficient way, and thus need the ability to manipulate and interact with data visualizations. 
   1. Manipulation: This can include tasks like dragging and dropping, zooming, and panning, as well as change changing the method of visualization (e.g. web, map, graphic), which will allow the user to see information in the most efficient way.
   2. 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.
   3. .
3. Collaborating: Currently, users described collaboration in ANB as clumsy. This user task is one of the main motivations behind creating a web-based application. Users Sharing and collaborating on visualizations: Analysts may want to share their data and visualizations with superiors and co-workers as well as collaborate on certain projects. 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
4. Sharing: Analysts would like to be able to share their data and visualizations with superiors and co-workers.
   1. Data Sharing: It  It should be easy for users to choose the source of data for a given visualization, as well as to share their data set sets with another userother users.
   2. Visualization Sharing: Users  Users should be able to share their visualizations easily without having to export and import large amounts of information.

Throughout the semester, we will be in communication with our representative users to pinpoint the UI features that will be most useful to them.