Data Exploration Goals Vary: 'Finding Something Interesting' is Valid for Some Analysts

Why It Matters

Understanding these differing perspectives is crucial for designing effective data analysis tools and workflows. It informs how we should support both directed and serendipitous discovery in analytical processes.

Key Finding

Data analysts have different ideas about what data exploration should achieve, with some aiming to find unexpected patterns and others focusing on supporting specific analytical tasks. Tool preferences and usage also vary significantly.

Key Findings

• A distinction exists between exploration as a precursor to directed analysis and open-ended exploration.
• Some analysts consider 'finding something interesting' a legitimate goal of exploration, while others do not.
• There are conflicting opinions on the role of intelligent tools in data exploration.
• Visualization is widely used for exploration, but direct manipulation interfaces are only used by a subset.

Research Evidence

Aim: To understand the diverse practices and goals of professional data analysts during data exploration.

Method: Qualitative Interview Study

Procedure: Thirty professional data analysts from various sectors were interviewed about their data exploration activities and tool usage.

Sample Size: 30 participants

Context: Professional data analysis environments (industrial, academic, regulatory)

Design Principle

Support diverse analytical workflows by providing flexible tools that cater to different user objectives and exploration strategies.

How to Apply

When designing data analysis platforms or visualization tools, conduct user research to understand the specific exploration goals and preferred interaction methods of your target audience.

Limitations

The study relies on self-reported data from interviews, which may be subject to recall bias or social desirability. The findings may not generalize to all data analysis contexts.

Student Guide (IB Design Technology)

Simple Explanation: Some data analysts like to explore data to find surprising things, while others use exploration to answer specific questions. Tools should be flexible enough for both.

Why This Matters: Understanding user goals is fundamental to creating effective and user-friendly designs. This research highlights that even within a single domain, user goals can be diverse.

Critical Thinking: How might the design of a data exploration tool be influenced if the primary goal is serendipitous discovery versus rigorous hypothesis testing?

IA-Ready Paragraph: This research by Alspaugh et al. (2018) highlights that professional data analysts exhibit diverse goals during data exploration, with some prioritizing the discovery of unexpected insights ('finding something interesting') while others focus on supporting pre-defined analytical tasks. This divergence suggests that design solutions for data analysis tools should be flexible enough to accommodate these varied user objectives, supporting both open-ended discovery and hypothesis-driven investigation.

Project Tips

• When researching user needs, ask open-ended questions about their goals and motivations.
• Consider how different user groups might have conflicting requirements for the same tool.

How to Use in IA

• Use this research to justify your user research methodology and to frame your understanding of user needs.
• Refer to this study when discussing the varied objectives users might have for a product or system.

Examiner Tips

• Demonstrate an awareness of the diverse motivations and approaches users may have towards a design problem.
• Justify design choices by referencing user research that acknowledges varying user needs.

Independent Variable: Analyst's goal for data exploration (e.g., finding something interesting vs. supporting directed analysis)

Dependent Variable: Analyst's description of exploration practices and tool usage

Controlled Variables: Professional background of the data analyst

Strengths

• Provides direct insights from practitioners in real-world settings.
• Explores nuanced aspects of data exploration beyond simple tool usage.

Critical Questions

• To what extent do these findings apply to non-professional data users?
• How can tool design proactively guide users towards their intended exploration goals?

Extended Essay Application

• Investigate how different user personas for a data visualization tool might prioritize discovery versus verification.
• Explore the impact of tool affordances on encouraging or discouraging serendipitous findings.

Source

Futzing and Moseying: Interviews with Professional Data Analysts on Exploration Practices · IEEE Transactions on Visualization and Computer Graphics · 2018 · 10.1109/tvcg.2018.2865040