Points of Orientation: An Experiential Theory of Data
Overview
Data matter experientially—not only for what they show us, but for how they shape our attention and affect, conditioning how we perceive our surroundings, others, and ourselves. And yet, established representational approaches to data, which are centered on objectivity and accuracy, obscure these experiential dimensions and limit our ability to reflect critically on what data do to us, not just for us. Data are not simply representations of the world; they are points of orientation within it.
As a countermeasure to prevailing discourses on data, centered on issues of representation, this book sets out to develop an experiential theory of data across five dimensions: settings, attention, affect, intra-action, and aesthetics. Together, these dimensions form a multi-layered framework for conceptualizing and constructing experiences of data. The book grounds this framework in first-person accounts of my own participation in a series of design investigations, with a focus on experiences of environmental data:
- Map Room—a place-based platform for constructing large-scale collaborative maps that are drawn by hand, but guided by data—demonstrates how data settings work materially, socially, and discursively to establish a common ground for orientation.
- Map Spot—a pop-up variation of the Map Room project developed for middle-school students in Savannah, Georgia—reveals the way our attention can shift dynamically in relation to data.
- Turbidity Wall—a data physicalization I constructed for my children at home, to explore our difficult feelings about environmental pollution—offers insights about our affective expectations of data.
- Chromatic Lens—an extended-reality data visualization made for use in an urban wild—supports people in learning to look at the environment in terms of color. The project surfaces the mutually-constitutive intra-actions that can shape our experiences with data.
- Plasmatic Mirror—an architectural-scale visualization that prompts passersby to reflect on microplastic pollution in their bodies—illuminates the importance of data aesthetics for how we see ourselves.
Individual chapters of the book are focused on these design investigations into the constitutive dimensions of data experience. In the book’s final chapter, I assess the generalizability of the five-dimensional framework through three additional case studies on socially transformative experiences of data.
The book draws on phenomenology, critical data studies, and feminist technoscience—alongside my own practice-based research and case studies—to reframe both discourse and design for data as well as data-driven systems. It shifts the central question of data from representation (what do data show us?) to orientation (how do we orient ourselves in relation to data?).
Book Features
- Points of Orientation builds on my 2019 MIT Press academic/trade book, All Data Are Local: Thinking Critically in a Data-Driven Society, in which I argue that we should not focus on data sets, but rather data settings: the social and technological contexts in which data are meant to be understood.
- The proposed book introduces a novel and research-based “experiential theory of data,” which holds that data are not simply representations of the world, they are points of orientation within it. They shape our attention and emotions, our sense of self and others, and our ways of relating to our surroundings.
- This experiential theory of data is juxtaposed with what I call the “representational theory of data.” This is the prevailing and normative conception of data featured in most course materials, books, and software for data science, data visualization, and machine learning.
- My turn towards the study of data experience has been in the making for 8 years, beginning in 2018, when I started working on the first of five design investigations that structure the proposed book: Map Room, Map Spot, Turbidity Wall, Chromatic Lens, and Plasmatic Mirror.
- The design investigations in this book focus on encounters with data about the environment, and demonstrate how such experiences can move people towards greater awareness and action.
- The book has broad implications for the design of interfaces to data, beyond environmental examples, and suggests new questions about how emerging models of artificial intelligence—rooted in the representational theory of data—are grounded in the world.
Related Publications
Ploypilin Pruekcharoen, Sylvia Janicki, Mohsin Yousufi, Miles Appleton, Emily G. Weigel, Yanni Loukissas. “Plasmatic Visualization: Visceral Attunement to Environmental Data” In Proceedings of the 2026 ACM Designing Interactive Systems Conference (DIS ’26). Association for Computing Machinery, New York, NY, USA, 2047–2061. (2026)
Sylvia Janicki and Yanni Alexander Loukissas. 2025. “Making Local Data Memoirs: Changing Orientations in Relation to Environmental Concerns.” In Proceedings of the 2025 ACM Designing Interactive Systems Conference (DIS ’25). Association for Computing Machinery, New York, NY, USA, 2047–2061. (2025) https://doi.org/10.1145/3715336.3735760
Hyde, Allen and Meltem Alemdar, Katie OConnell, Philip Omunga, Michelle Reckner, Yanni Loukissas, Iris Tien, Mohsin Yousufi *PhD Student*, Nisha Botchwey, Olivia Chatman, Kamiya Clayton, Mildred McClain, Mustafa Shabazz, Blaine Branch. “Promoting Youth Advocacy for Resilience to Disasters: A Pilot Study” Journal for Gender and Development (2025)
Loukissas, Yanni A. and Jude M. Ntabathia. 2021. “Open Data Settings: A Conceptual Framework Explored Through the Map Room Project.” Proceedings of the 2021 ACM Computer Supported Cooperative Work Conference (CSCW’21) Association for Computing Machinery, New York, NY, USA, 2047–2061. (2021)
Loukissas, Yanni A. All Data Are Local: Thinking Critically in a Data-Driven Society. Cambridge: MIT Press (2019)