Databiting: Lightweight, Transient, and Insight Rich Exploration of Personal Data

Abstract

As mobile and wearable devices are becoming increasingly powerful, access to personal data is within reach anytime and anywhere. Currently, methods of data exploration while on-the-go and in-situ are, however, often limited to glanceable and micro visualizations, which provide narrow insight. In this article, we introduce the notion of databiting, the act of interacting with personal data to obtain richer insight through lightweight and transient exploration. We focus our discussion on conceptualizing databiting and arguing its potential values. We then discuss five research considerations that we deem important for enabling databiting: contextual factors, interaction modalities, the relationship between databiting and other forms of exploration, personalization, and evaluation challenges. We envision this line of work in databiting could enable people to easily gain meaningful personal insight from their data anytime and anywhere.

What is Databiting

We conceptualize databiting as the act of interacting with personal data to gain increasingly rich insight through lightweight and transient exploration. The result is a databite, concise personal insight that extends upon what can be derived from glanceable or micro visualizations. Databiting as both a new concept and a topic for research is fluid in nature: Boundaries defining insight and data exploration methods allowing for such insight are not rigidly defined or fixed.

To illustrate this concept, we draw upon analogy. Databiting can be seen as equivalent to eating a small and easily consumable snack. The size of a snack and the number of bites required may vary from person to person and from context to context. Yet, what remains constant is the lightweight and transient nature of snacking compared to consuming a meal (often a reasonably large amount of food). In the context of data exploration, databiting equates to the consumption of bite-sized information that provides rich insights or sustenance in the moment. This builds upon simply viewing a mobile data visualization and does not require more in-depth and long-term data exploration, which can be done later when necessary or more appropriate.

Importantly, databiting is not meant to replace either in-depth exploration of data or shorter-form viewing of glanceable visualizations; rather, it is complementary to them. By bridging the gap between the two forms of exploration methods, databiting offers a new form of complementary exploration that pushes the boundaries of what is attainable. This integration of exploration methods can foster a more comprehensive, valuable, and unique (i.e., richer) understanding of personal data, yet remain accessible in a lightweight and transient manner. By offering a range of exploration options, across devices and throughout a range of usage scenarios, we expect individuals can derive greater benefits from their data-driven insights anytime and anywhere.

Expected Benefits

We discuss envisioned benefits of databiting that have the potential to overcome limitations and challenges of the current capabilities of mobile data exploration. Further study is needed to identify and demonstrate any tangible benefits that may exist.

• Introductory and Intermediary Access. Databiting offers brief, insight-rich access to personal data, making it easier for users—especially beginners—to engage with and understand their information. By presenting data in small, digestible pieces, it encourages curiosity and gradually builds confidence, serving as a stepping stone to deeper and more meaningful data exploration.

• Increased In-Situ Insight. Databiting supports in-situ data exploration by delivering lightweight, insight-rich information during ongoing activities. It builds on existing glanceable and micro visualizations by adding more depth, enabling timely, and increasinlgy actionable insights. Whether on a phone, smart speaker, or laptop, databiting helps users make informed, real-time decisions based on their personal data.

• Perceived Usefulness. A major challenge with personal data tools is their low perceived usefulness, which often leads users to stop using devices, apps, or even collecting data. Databiting offers a potential solution by providing richer, more personalized insights, which can enhance the perceived value of both the tools and the data. This, in turn, may encourage continued engagement and prevent abandonment.

Research Directions

In our article, we delineate five research topics which must be further explored to enable our envisioned form of databiting:

1. Contextual Factors
2. Interaction Modalities
3. Databiting and Broader Exploration
4. Personalization (and Customization)
5. Evaluation Challenges

In More Detail

Please review our full article (linked above) for a complete discussion and more detailed call to research.