Multidimensional Data Literacy
Holographic Vision in the World of Connected Information
Wet with Data
Imagine a world where every object you interact with, from your coffee mug to your car, is constantly generating a stream of data. Now imagine that these data streams are not just isolated trickles, but part of a vast, interconnected ocean of information. This is the world we live in today, a world where data is not just something we consume, but something that surrounds us and permeates our lives.
As someone who has always been fascinated by the nature of information, I've been on a journey to develop what I call "multidimensional data literacy" - a framework for navigating and making sense of this complex data landscape. It's a journey that has taken me from the daily life of operational data work to fundamental principles of complexity theory, physics, and cognitive science, and has fundamentally transformed how I see and interact with information.
In this essay, I want to share some key principles and insights I've discovered along the way. These are not just technical skills, but ways of seeing and thinking that I believe will be increasingly essential for anyone who wants to thrive in the age of data. Whether you're a business leader trying to make sense of customer analytics, a scientist working with large datasets, or just a curious individual looking to expand your mind, I invite you to join me in the multidimensional world of data.
Information as Objects
One of the foundational ideas of multidimensional data literacy is to treat information as objects. Just as a child playing with a set of wooden blocks can combine and reconfigure them in countless ways, we can think of data points as modular units that can be assembled and reassembled to create different structures of meaning.
Think of a social media post, for example. At one level, it's just a sequence of characters, a snippet of text. But it's also an object with multiple properties - a timestamp, an author, a set of tags or topics, a number of likes and shares. By treating this post as an object, we can start to explore its relationships to other objects, and begin to see patterns and connections that wouldn't be apparent if we just looked at the raw text.
This object-oriented view of data has some powerful implications. It means that we can apply the same kinds of operations and transformations to informational objects that we would to physical ones - we can group them, split them, combine them, reshape them. It also suggests that there might be a kind of "informational physics" - a set of rules and principles that govern how these objects interact and evolve over time.
Complexity: Objects within Objects
One of the key challenges and opportunities of the data age is the sheer complexity of the information we're dealing with. We're not just talking about a few isolated data points, but vast networks of interconnected entities, each with its own internal structure and external relationships. To make sense of this complexity, we need to be able to zoom in and out, to see both the forest and the trees.
This is where the concept of informational objects really comes into its own. Because when we start to look closely at any given object, whether it's a person, a product, or a piece of content, we can see that it's not just a single, monolithic thing. It's a composite, made up of smaller objects with their own properties and behaviors. Think of a news article, for example. At one level, it's a single object, but if we zoom in, we see that it's made up of paragraphs, sentences, and words. And if we zoom out, we see that it's part of a larger section, publication, and media ecosystem.
This idea of objects containing other objects is fundamental to programming and data structures, but it's also a powerful way of thinking about the world more generally. It suggests that complexity is not just a matter of scale, but of hierarchy and abstraction. By breaking complex systems down into their component parts, and understanding how those parts fit together, we can start to make sense of even the most intricate and multifaceted phenomena. At the same time, by modeling the relationships between objects at different levels of complexity, we can start to uncover the hidden structures and dynamics that drive behaviors and patterns.
Dimensions and Shared Properties
If informational objects are the building blocks of our data universe, dimensions are the axes along which these objects can vary and relate to each other. In the simplest case, we might think of a two-dimensional table of data, where each row is an object and each column is a property or attribute. But the power of multidimensional thinking is to recognize that these properties can themselves be complex and multifaceted.
Consider the temporal dimension, for example. Every data point is situated in time, whether it's the moment a sensor reading was taken or the publication date of a news article. But time isn't just a flat line; it has its own rich internal structure of cycles and rhythms, of sequences and durations. By understanding how informational objects are situated in these temporal landscapes, we start to see patterns of growth, change, and evolution.
Similarly, informational objects are situated in multidimensional spaces of categories, relationships, and measures. Some of these dimensions, like physical location, are grounded in the external world. Others, like topic tags or sentiment scores, are constructed by us to capture specific aspects of meaning. By navigating and manipulating these dimensional spaces, we can slice through the data in different ways, uncovering insights and patterns that might be hidden in a flat representation.
Models as Analogies
Closely related to the idea of dimensions is the concept of models. In essence, a model is a simplified representation of a system or process, a way of mapping between one domain of objects and another. In the context of data literacy, we can think of models as analogies that allow us to reason about complex informational systems using more familiar or intuitive frameworks.
One of my favorite examples of this is using a physics engine to model customer interactions on an e-commerce site. By mapping user behaviors to physical properties like mass, velocity, and friction, we can start to see patterns of attraction, repulsion, and flow that wouldn't be obvious from looking at raw clickstream data. The model becomes a lens that reveals hidden dynamics and relationships.
The power of models is that they allow us to play with data in creative and exploratory ways. By changing the parameters of the model, we can ask "what if" questions and simulate different scenarios. By comparing the outputs of the model to real-world data, we can refine our assumptions and improve our understanding over time. Models are not just descriptive, but generative - they suggest new questions to ask and new hypotheses to test.
Of course, models are also simplifications, and it's important to understand their limitations and assumptions. A model is not reality, but a map - and like any map, it's useful precisely because it leaves things out. The art of modeling is to find the right level of abstraction, the right balance between simplicity and accuracy for the task at hand.
The Power of Play
One of the most delightful aspects of working with data in a multidimensional way is the sense of play and exploration it enables. When we treat informational objects as toys to be combined and reconfigured, and dimensional spaces as landscapes to be roamed and mapped, we open up new possibilities for creative insight and discovery.
Think of the way a jazz musician plays with musical motifs, recombining and transforming them in endless variations. Or the way a child builds worlds out of LEGO blocks, testing different configurations and seeing what emerges. This kind of open-ended, improvisational play is not just a source of pleasure, but a powerful mode of learning and problem-solving.
In the context of data literacy, play might mean experimenting with different visualizations to see what patterns emerge, or mashing up datasets from different domains to look for unexpected correlations. It might mean building a whimsical simulation that exaggerates certain features of a system, just to see what happens. The key is to approach data with a spirit of curiosity and experimentation, not just as something to be analyzed but as something to be explored.
Of course, play doesn't mean abandoning rigor or discipline. In fact, the most productive forms of data play are often highly structured, with clear goals, rules, and constraints. The trick is to find the right balance between freedom and focus, between serendipity and intentionality. And like any form of play, data exploration is often most rewarding when it's done collaboratively, with others who can bring different perspectives and skills to the table.
Augmenting Human Perception
Ultimately, the goal of multidimensional data literacy is not just to manipulate abstractions, but to expand and enrich our perception of the world. Just as the microscope and telescope revealed unseen dimensions of the physical universe, tools and techniques for navigating information space can reveal hidden patterns and connections in the data that surrounds us.
Imagine being able to see the flow of ideas and influences across a scientific field, or to sense the ripples of sentiment and emotion in social media chatter. Imagine being able to hear the rhythms and cadences of market trends, or to touch the texture of a complex dataset. These are the kinds of perceptual augmentations that multidimensional data literacy can enable.
Of course, our tools for rendering data perceptible are still primitive compared to the richness and complexity of the information itself. We are still learning how to design visualizations that are both truthful and evocative, interfaces that are both powerful and intuitive. But as we continue to experiment with new forms of sensory encoding and interaction, from immersive data environments to haptic feedback systems, we are slowly expanding the bandwidth of our connection to the informational world.
In the long run, data literacy will not just be about manipulating external representations, but about internalizing new ways of seeing and thinking. Just as literacy in the traditional sense involves not just reading texts but absorbing their patterns of meaning and argument, literacy in the age of data will mean developing new intuitions and instincts for navigating complex information spaces. It will mean training our minds to think in terms of dimensions and models, to see analogies and connections where others see only noise.
Conclusion
The journey toward multidimensional data literacy requires a willingness to let go of old habits of thought, to embrace new ways of seeing and interacting with information. It requires a certain playful spirit, a tolerance for ambiguity and complexity. And it empowers a commitment to ongoing learning and growth, as the data landscape continues to shift and evolve.
This journey is not just worthwhile but necessary for anyone who wants to make sense of the world we live in today. Whether we're trying to solve complex scientific problems, make better business decisions, or simply navigate our own lives in an increasingly data-saturated society, the ability to think and act in multidimensional ways will be essential.
As we've seen, this ability is not a matter of technical skill, but of perspective and imagination. It's about seeing data not as a static resource to be mined, but as a dynamic medium to be explored and shaped. It's about embracing the richness and complexity of the informational world, while also finding ways to make it intelligible and actionable.
Multidimensional data literacy is about more than just coping with the challenges of the data age. It's about seizing its opportunities and possibilities. It's about discovering new ways of understanding and interacting with the world, new forms of beauty and meaning in the patterns of information that surround us.
We will pass beyond literacy, to fluency and mastery, to be the pioneers and poets of these new worlds, explorers and cartographers of multidimensional space. Embrace the joy and the challenge of learning to see and think in new ways, with the support of information and context all around. And wherever possible, use your holographic understanding to improve both your own personal worlds and the world we all share.