Points of Orientation: An Experiential Theory of Data
Overview
Data are not merely representations of the world; they are points of orientation within it. They direct attention, shape affect, and condition how we experience our surroundings, ourselves, and others. Yet conventional, 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.
This book develops an experiential theory of data across five dimensions: settings, attention, affect, intra-action, and aesthetics. Each dimension is grounded in a place-based design investigation—Map Room, Map Spot, Turbidity Wall, Chromatic Lens, and Plasmatic Mirror—that demonstrates how data function as experiential phenomena.
Drawing on phenomenology, critical data studies, and feminist technoscience, alongside first-person accounts of creative and community-engaged work with environmental data, the book reframes both discourse and design for data as well as data-driven systems, such as emerging AI models. 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 an novel and empirically-grounded “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 and data visualization. The representational theory mistakenly implies that data are an ideal and objective form of information, independent of surrounding people and places, and best understood rationally and abstractly.
- 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 collaborative, technological design investigations that structure the proposed book: Map Room, Map Spot, Turbidity Wall, Chromatic Lens, and Plasmatic Mirror.
- The book’s experiential theory of data has important implications for the design of interfaces to data, as well as emerging models of artificial intelligence, which are rooted in the representational theory of data, and thus inherently ungrounded.
Chapter Outline
Introduction
I begin with my experiential theory of data and the five dimensions/five projects that ground my understanding of human encounters with data.
Chapter 1: How Settings Shape What Counts
I give an account of my experience working to develop a Map Room in Atlanta. I offer a detailed analysis of my use of the mapmaking platform in a community planning meeting, which brought city planners and their constituents together with data about the places they live.
Chapter 2: What We Attend To Together
I explain how Map Spot can create shared contexts for reflecting on how we are directing our attention and to what end. Within a large-scale collaborative research project supported by the first NSF-funded “Civic Innovation Challenge,” I worked with students and colleagues to develop a map making toolkit and curriculum for young, would-be environmental advocates in Savannah, Georgia.
Chapter 3: What We Don’t Expect to Feel
I recount an experience at home during the lengthy pandemic-isolation period. Using water quality data from a government-installed sensor on the creek that runs behind my house, I built Turbidity Wall, a physical data construction out of household materials to explore difficult feelings within my family about the risks related to environmental pollution.
Chapter 4: How Intra-Actions Change Us
I describe my collaboration with a colleague, Dr. Emily Weigel, in the Georgia Tech School of Biological Sciences. We built Chromatic Lens, an application for a tablet computer that draws upon color data from a database of bird sightings (eBird), to turn an urban wild into an extended reality data setting.
Chapter 5: What Aesthetics Can Do
I explain my work with an interdisciplinary team, including other faculty, librarians, and student researchers at Georgia Tech, to build Plasmatic Mirror, an architectural-scale data setting with the capacity to influence the way people see themselves in relation to invisible sources of microplastic pollution.
Chapter 6: Points of Orientation in an Expanded Field
Most accounts of how to do data visualization assume that only formalized sources count as data. In chapter 6, Points of Orientation in an Expanded Field, I explore a broader set of “givens” not formally defined as data, and the media art projects that incorporate them as raw materials in the construction of common grounds.
Coda
I discuss the broader implications of this theory, including the stakes of the theory: its capacity to reframe how we relate to emerging models of generative AI.
Methodological Note
In order to develop an experiential theory of data, I had to establish a method for studying data as points of orientation. This section of the book offers further details on my approach for an acadmic audience.
Related Publications
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 “Open Data Settings: A Conceptual Framework Explored Through the Map Room Project.” Proceedings of the ACM on Computer Supported Cooperative Work, CSCW (2021)
Loukissas, Yanni A. All Data Are Local: Thinking Critically in a Data-Driven Society. Cambridge: MIT Press (2019)