Exploring the Human Side of Data: Unlocking its Stories and Complexities

Table of Contents

1. Introduction
2. The Relationship Between Data and Analytics
3. Intermediation of Data
4. Data and Context
5. The Concept of Feral Data
6. Data and Responsibilities
7. Data and presentation
8. The Impermanence of Data
9. The Future of Data Generation
10. Designing for Data, Designing for People

Data: The Human Story

In today's digital age, data has become an integral part of our lives, influencing everything from the way we communicate to the decisions we make. However, to truly understand and appreciate the significance of data, it is important to view it through a human lens. Just like humans, data has its own set of characteristics and behaviors that Shape its existence. In this article, we will explore the various aspects of data that make it remarkably human, and how this understanding can reshape the way we perceive and interact with data.

Introduction

The world of data is vast and ever-expanding, filled with billions of bits and bytes that flow through networks, much like tiny individuals. While we often focus on data analytics and making sense of the information generated, it is essential to acknowledge the relationship aspect of data. Like humans, data thrives in a good relationship, which poses significant challenges when it comes to data analysis and interpretation.

The Relationship Between Data and Analytics

Data analytics forms the cornerstone of deriving valuable insights from the vast amount of information being generated. However, with data being intermediated, it takes on a social aspect. Just like social relationships, data starts to look suspiciously social, as it comes with a country of origin and a specific context. Removing data from its place and context renders it meaningless, emphasizing the importance of understanding the interplay between data and its surroundings.

Intermediation of Data

Imagine a man in India who sells data. He operates a kiosk at train stations and in various cities. People bring their phones to him, and he replaces the content with new data for the weekend. This man consumes an enormous amount of data while aggregating it for others. However, on the network, he appears as a crazy person due to the high data consumption. This example highlights the concept of data intermediation, where data moves between individuals, creating a web of relationships and challenges in its analysis.

Data and Context

Context plays a crucial role in understanding data. A project by Intel involved exploring the contents of people's cars. Depending on one's perspective and cultural background, the data collected from a car can lead to vastly different interpretations. For example, a chainsaw and an axe in a car may Evoke the image of a serial killer for an American, while an Australian may recognize it as the car of a volunteer firefighter. This demonstrates how data carries the context of its origin and the significance of preserving that context for Meaningful analysis.

The Concept of Feral Data

Data, much like certain animals, can become feral when it moves beyond the systems and boundaries imposed on it. Privacy and security concerns arise when data develops its own Patterns and escapes our control. The unbounded nature of data poses challenges for regulators and calls for strategies to deal with the feralness of data. Building effective systems to manage and control data's wild tendencies becomes critical as it moves beyond its initial spaces.

Data and Responsibilities

In many cultures, including Aboriginal communities in Australia, data is not just information. It carries a responsibility to be shared and transmitted correctly. The stories and knowledge associated with data carry an obligation to be told to the right people, in the right sentiment, and with the right spirit. Recognizing the responsibilities that come with data challenges the conventional understanding of information and highlights the importance of preserving the integrity of data.

Data and Presentation

Data has a natural inclination to Present itself in the best possible light, much like humans. We often curate our online presence, carefully selecting the information we share to make ourselves look good. Similarly, data can be manipulated or misrepresented, introducing lies into the world of data. This realization emphasizes the importance of understanding that not all data is true, and that the lies we tell can be as interesting as the truths.

The Impermanence of Data

While data is often considered permanent, there are instances where impermanence is desired. Just like certain things in life, some data is meant to be transient, to be destroyed, or to fade away. Designing systems that allow for impermanent data poses both an interesting challenge and a human reality. Acknowledging the desire for certain data to disappear is crucial in creating a holistic understanding of data and its relationship with the human experience.

The Future of Data Generation

As we move forward, data generation will continue to expand and intertwine with human lives, facilitated by the devices we carry. The constant stream of data from these devices highlights the monumental shift in how we perceive, analyze, and track data. The patterns and stories that emerge from this data will shape our understanding of the world. It is important to recognize the immense potential and challenges posed by this interconnectedness of data and human existence.

Designing for Data, Designing for People

In considering the human characteristics of data, one question emerges: What if we designed our systems as if data were alive and possessed intentionality? Instead of merely designing for devices that produce data, envisioning data as entities with their own desires and tendencies prompts a fresh perspective on network structure and system design. This concept challenges us to consider who will tell the stories of data and how we can design systems to accommodate the dynamic nature of data.

In conclusion, by understanding data as a human story, we gain new insights into its complexities and implications. Data possesses attributes that Resemble human traits, prompting us to consider the context, social dynamics, and responsibilities associated with data. Recognizing the feral nature of data, the desire to present itself favorably, and the impermanence it may encompass allows us to approach data from a more human-centered perspective. As we navigate this data-driven world, design considerations that Align with human interactions can shape a more meaningful and ethical engagement with data.

Highlights

• Data, like humans, seeks relationships and encounters challenges in analytics
• The intermediation of data creates social dynamics and challenges in analysis
• Context is crucial in interpreting and preserving the meaning of data
• Feral data poses privacy and security concerns as it moves beyond control
• Data comes with social and cultural responsibilities for accurate transmission
• Data has the tendency to present itself in favorable ways, embracing lies
• Recognizing the impermanence of data challenges its conventional understanding
• Data generation intertwines with human lives, transforming our understanding
• Designing systems with data's human-like attributes offers new perspectives
• Approaching data as a human story fosters a more meaningful engagement

Frequently Asked Questions (FAQ)

Q: How does the intermediation of data impact its analysis? A: The intermediation of data introduces social dynamics, making data appear more social and challenging its analysis. Understanding the relationships between data sources becomes vital in accurately interpreting the information generated.

Q: Why is context important in data analysis? A: Context provides the necessary background to make sense of data. The origins and circumstances surrounding data contribute to its meaning and relevance. Removing data from its context can render it meaningless or distorted.

Q: What are the challenges posed by feral data? A: Feral data refers to data that moves beyond our control and develops its own patterns. This presents challenges in terms of privacy and security. Developing strategies to manage and regulate feral data becomes increasingly important as it escapes our imposed constraints.

Q: How does data carry responsibilities? A: In certain cultures, data carries the responsibility of accurate transmission and sharing. Just as stories and knowledge must be conveyed correctly, data must be treated with the same respect and transmitted to the right people, in the right sentiment and spirit.

Q: Can data be manipulated or misrepresented? A: Yes, similar to how humans curate their online presence, data can be manipulated or misrepresented. Not all data is true, and the lies we tell through data can be as interesting as the truths. Understanding the inherent biases and potential falsehoods in data analysis is crucial.

Resources:

• Intel
• Cornell University