Short answer
Design tools and systems that accommodate the varied goals and mental models of data analysts, rather than assuming a single approach to data exploration.
- Field
- User-Centred Design
- Source
- IEEE Transactions on Visualization and Computer Graphics (2018)
- Method
- Qualitative Interview Study
- Sample
- 30 participants
- Evidence
- Mixed findings
Professional data analysts hold divergent views on whether the primary goal of data exploration is to uncover unexpected insights or to support pre-defined analytical tasks. This user-centred design research insight is drawn from a 2018 study published in IEEE Transactions on Visualization and Computer Graphics. Using Qualitative interview study with 30 participants, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Design tools and systems that accommodate the varied goals and mental models of data analysts, rather than assuming a single approach to data exploration.
Data Exploration Goals Vary: 'Finding Something Interesting' is Valid for Some Analysts
Professional data analysts hold divergent views on whether the primary goal of data exploration is to uncover unexpected insights or to support pre-defined analytical tasks.
IEEE Transactions on Visualization and Computer Graphics · 2018
Key Findings
- 01A distinction exists between exploration as a precursor to directed analysis and open-ended exploration.
- 02Some analysts consider 'finding something interesting' a legitimate goal of exploration, while others do not.
- 03There are conflicting opinions on the role of intelligent tools in data exploration.
- 04Visualization is widely used for exploration, but direct manipulation interfaces are only used by a subset.
Application
Design takeaway
Design tools and systems that accommodate the varied goals and mental models of data analysts, rather than assuming a single approach to data exploration.
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.
Project actions
- 01When researching user needs, ask open-ended questions about their goals and motivations.
- 02Consider how different user groups might have conflicting requirements for the same tool.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Provides direct insights from practitioners in real-world settings.
- +Explores nuanced aspects of data exploration beyond simple tool usage.
Limitations
Interviews capture subjective experiences, and the sample size, while reasonable for qualitative work, may not represent all data analysts.
Reliability & validity
Reliability is moderate due to the qualitative nature and potential for interviewer bias. Validity is strong in capturing the lived experiences of professional analysts.
Think critically
How might the design of a data exploration tool be influenced if the primary goal is serendipitous discovery versus rigorous hypothesis testing?
Design Principles
"Support diverse analytical workflows by providing flexible tools that cater to different user objectives and exploration strategies."
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.
What This Means for Your Design
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.
How to use in your project
- 1.Use this research to justify your user research methodology and to frame your understanding of user needs.
- 2.Refer to this study when discussing the varied objectives users might have for a product or system.
Add to My Project
Quick Cite
Paragraph starter
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.
Source
IEEE Transactions on Visualization and Computer Graphics
Futzing and Moseying: Interviews with Professional Data Analysts on Exploration Practices
journal · 2018
View sourceQuestions About This Research
- What does the research say about data exploration goals vary: 'finding something interesting' is valid for some analysts?
- Design tools and systems that accommodate the varied goals and mental models of data analysts, rather than assuming a single approach to data exploration. Evidence: IEEE Transactions on Visualization and Computer Graphics (2018).
- Why does "Data Exploration Goals Vary: 'Finding Something Interesting' is Valid for Some Analysts" matter for design?
- 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.
- How can designers apply this research?
- Design tools and systems that accommodate the varied goals and mental models of data analysts, rather than assuming a single approach to data exploration.
- What were the main 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.
- What research method was used?
- Qualitative Interview Study with 30 participants.
- How strong is the evidence?
- Evidence strength is rated Mixed findings, based on a 2018 journal from IEEE Transactions on Visualization and Computer Graphics.
- What should I do differently in my next project?
- 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.
- What are the 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.