06 DTC 101

Data & Information

6.1 Big Data

Big data refers to massive data sets that can only make sense with the assistance of a computer. On the one hand, big data makes possible predictions that can help people live happier, safer and healthier lives. Google collects data around forest fires, earthquakes and virus outbreaks just by aggregating and interpreting search terms. But search terms can also provide aggregated data about personal details in network behavior. On August 4th 2006, AOL accidentally released a sample log of 20 million search queries. 650,000 AOL users had their personal searches exposed to the world. Their identifying names were removed, but the search terms revealed very private information about user's sex lives, fears, desires and, in some cases, criminal intentions.

An individual U.S. citizen produces an enormous amount of public data as a byproduct of just living in a networked world. Searches, purchases, geographical movements, preferences in food and media entertainment might seem like insignificant raw data on the individual level, but when aggregated and merged with other data from other individuals the data set could result in patterns of behahvior that might produce useful information. "Millenneial purchasing history" is a query that might scan vast data sets over a period of time and then return useful patterns for companies trying to market to millenneials.

Algorithms also capture and process personal data to make consequential decisions about individual lives, such as whether someone should get a loan, keep their health care, or be released on bail. What are the implications and consequences of this type of automated surveillance and analysis? How and when is the big data collected about human behavior an invasion of individual privacy? How might computer systems, which lack the nuance and subtlety of human-to-human interaction, make harmful decisons in the interest of pattern prediction and efficiency? How can humans trust digital data when it is quite easy for AI to create "fake" data?

There are no easy answers to these questions. Big data promises to make the world more efficient and safer, but it also introduces new technological processes that are largely invisible to humans and therefore unpredictable by humans.

Data Analytics

Data analytics is the process of transforming raw data into meaningful information. Analysts collect, clean, organize, and interpret data in order to discover patterns, answer questions, and support decisions. Many of these tasks are now assisted by computational methods that can process millions or billions of records far more quickly than humans alone.

Data rarely speaks for itself. Patterns become meaningful only when they are interpreted within a particular context. To communicate findings, analysts often use data visualizations, infographics, dashboards, maps, and interactive graphics. These visual forms help people recognize trends, compare values, identify relationships, and understand complex information at a glance.

Data analytics now shapes many areas of everyday life. Businesses analyze customer behavior, scientists examine experimental results, governments study public services, journalists investigate social issues, and streaming platforms recommend music, films, and books. Increasingly, machine learning systems also rely on large datasets to identify patterns and make predictions.

Although data analysis can reveal important insights, it is never completely objective. Every dataset reflects choices about what was collected, how it was measured, which questions were asked, and what information was left out. Likewise, every visualization highlights certain relationships while downplaying others. Understanding data therefore requires both technical skill and critical thinking.

Machine Learning

Machine learning is a method of data analysis that gives the computer the task not only of sorting and processing big data, but also of analytical model building. As a branch of artificial intelligence, machine learning gives systems the power to learn from data by identifying patterns and making informed decisions with almost no human involvement.

Machine learning marks an important transition in this chapter. Data is no longer valuable simply because it records the past; it becomes the raw material from which computational systems learn patterns, make predictions, and later generate text, images, music, and code. Understanding where training data comes from—and how it is collected, filtered, and labeled—is essential for understanding contemporary AI.

Contemporary AI systems depend on enormous datasets for training. Large language models, recommendation systems, image generators, and speech recognition systems learn patterns from data rather than being explicitly programmed with fixed rules. Chapter 07 will explain these systems in more detail, but the key point for this chapter is simple: AI begins with data. The next chapter explains how machine learning models transform these datasets into modern AI systems.

Data and Privacy

Web search began in 1994, when the World Wide Web was in its infancy. Search has since become an enormous business based purely on the data a search engine extracts about its users' queries. Google delivers ads and packages of data sets are sold to other companies based on keywords in search terms.

Personal data is more than a record of search queries. It is the aggregation of all the data that trails a person with a mobile phone and/or home computer: geolocation, purchases, likes and favorites, keywords in emails. The following documentary presents some of the questions around privacy in the era of big data. Who owns the data of a person's life?

6.2 Data and Culture

There are some things humans cannot do efficiently, such as finding patterns across billions of records or matching a face against millions of images in seconds. Data systems extend human perception by helping us discover relationships, trends, and anomalies that would otherwise remain invisible. These capabilities are transforming science, medicine, business, the arts, and everyday life—but they also raise important questions about bias, ownership, privacy, and trust.

Datasets as Cultural Artifacts

Datasets are not neutral collections of facts. They reflect human choices about what is measured, preserved, categorized, ignored, or erased. A census, museum archive, search log, medical database, or social media dataset can reveal important patterns, but it also carries the assumptions of the people and institutions that created it.

When data is used to make decisions about culture, health, education, policing, work, or creativity, those choices matter. What counts as data? Who collects it? Who owns it? Who gets represented? Who becomes invisible? These questions are especially important in the age of artificial intelligence, because AI systems often learn from datasets created for other purposes.

Data-Driven Creativity

Data increasingly shapes creative work. Streaming services, social media platforms, and online marketplaces analyze patterns in audience behavior to recommend music, films, books, games, and videos. Artists, designers, and producers also use data to understand audiences, identify trends, and evaluate how creative work circulates online. While these systems can help people discover new works, they can also encourage creators to optimize for popularity and engagement rather than experimentation or artistic risk.

Data and Health

Healthcare increasingly depends on large datasets collected from medical images, electronic health records, genetic sequencing, wearable devices, and public health systems. By analyzing these datasets, machine learning systems can assist physicians in detecting diseases, predicting health risks, identifying drug interactions, and accelerating scientific research. While these tools promise earlier diagnosis and more personalized medicine, they also raise questions about privacy, bias, informed consent, and who controls sensitive medical information.

Data as Cultural Knowledge

Data has become an important tool for studying culture as well as science. Historians analyze millions of historical documents, literary scholars compare patterns across thousands of books, linguists examine changes in language over time, and museums create searchable digital archives of artworks and artifacts. Rather than replacing close reading or historical interpretation, computational methods reveal large-scale patterns that can inspire new questions about human culture and creativity.

Trust, Provenance, and Verification

Increasingly, digital systems depend on trusting the origin of data. A photograph may be authentic, edited, or entirely AI-generated. A scientific dataset may come from careful observation, simulation, or synthetic generation. A financial transaction may be verified through cryptography, while an online image or video may require metadata that documents how it was created or modified. As AI-generated media becomes more common, establishing the provenance—or history—of digital information becomes an essential part of maintaining trust.

6.3 Data and Human Experience

What does it mean to be human in a world with big data technologies making decisions about health, law, transportation, entertainment, social interaction, desire, and taste? Below are some of the most prominent voices today warning of the dangers of trusting big data technologies to solve problems without critical reflection about what we are handing over to automation.

What Data Cannot Capture

Data can reveal patterns, but it cannot fully capture lived experience. A number may record a purchase, a click, a location, or a test score, but it does not automatically explain context, emotion, intention, memory, uncertainty, or local knowledge. Human beings are more than the measurable traces they leave behind.

This does not mean data is useless. It means data must be interpreted carefully. A dataset can help us see large-scale patterns, but it can also flatten difference, ignore exceptions, and encourage decisions that feel objective simply because they are numerical.

6.4 Surveillance and Prediction

Digital data is often collected in the background as people search, shop, communicate, navigate, stream, post, and move through the world with connected devices. This constant collection creates new forms of surveillance. Some surveillance is visible, such as a security camera or a login screen. Other forms are less visible, such as tracking pixels, cookies, app permissions, location histories, and data brokers that combine information from many sources.

Data Tracking

Tracking systems collect traces of behavior: what users click, where they pause, what they buy, how long they watch, which locations they visit, and which posts they share. Each piece of data may seem small, but together these traces create profiles that can be used for advertising, recommendation, risk assessment, research, and prediction.

Prediction Systems

Prediction systems use past data to estimate future behavior. They may predict what someone wants to buy, what movie they might watch, whether they might repay a loan, how likely they are to click an advertisement, or whether they should be considered a risk by an institution. These predictions can be useful, but they can also reproduce old inequalities if the data reflects biased social systems.

Increasingly, datasets are not only collected from the world but also generated by computers. Synthetic data can help train AI systems, protect privacy, or simulate rare events, but it also raises questions about provenance, authenticity, and bias.

Recommendation Engines

Recommendation engines shape everyday digital culture. Streaming platforms recommend movies and music. Social media platforms recommend posts, videos, and accounts. Search engines rank results. These systems do not simply respond to user desire; they help form desire by deciding what becomes visible, repeated, or hidden.

Algorithmic Decisions

Algorithmic decision-making becomes especially serious when data systems influence access to jobs, housing, credit, education, health care, policing, or legal outcomes. A decision may appear neutral because it comes from a computer, but the system still depends on human choices about data, categories, goals, and design. The central question is not only whether a system is accurate, but whether it is fair, accountable, and understandable.

“Models are opinions embedded in mathematics.”

— Cathy O'Neil, Weapons of Math Destruction (2016)

6.5 Unit Exercise: Protect Your Data

From the article How to Protect Your Digital Privacy | nytimes.com

1. Use a password manager such as LastPass and 1Password to generate and remember different, complex passwords.
2. Use two-factor authentication on important accounts such as email, banking, school, cloud storage, and social media.
3. Use a browser extension like uBlock Origin to block ads and the data they collect.
4. Install the HTTPS Everywhere extension to always connect to the secure version of a site. This will make it difficult for attackers on public Wi-Fi to eavesdrop on your online activity.
5. Always update software and apps.
6. Review location permissions on your phone and turn off location access for apps that do not need it.
7. Review privacy settings on one social media account.
8. Investigate what data an AI chatbot, search engine, or writing tool may retain from your prompts and uploads.
9. Download or review your personal data from a major platform such as Google, Meta, TikTok, Apple, or another service you use.
10. Don't share private data on social media platforms or in AI systems unless you understand how that data may be stored or used.

Reflection Questions

1. Which privacy setting surprised you most?
2. What kinds of data were easiest to find and control?
3. What kinds of data were difficult to understand, delete, or manage?
4. How does data collection change your sense of trust in digital platforms?

6.6 Glossary

• data: raw facts, measurements, symbols, or records that can be processed into information.
• information: data that has been organized, interpreted, or given meaning.
• big data: extremely large datasets that require computational tools to store, process, and analyze.
• dataset: a structured collection of data used for analysis, research, or machine learning.
• metadata: data about data, such as time, location, author, file type, or device information.
• data analytics: the process of examining data to find patterns, answer questions, or support decisions.
• data visualization: the representation of data through charts, maps, diagrams, or other visual forms.
• machine learning: a method of computing in which systems learn patterns from data rather than following only fixed rules.
• algorithmic decision-making: the use of computational rules or models to influence or make decisions.
• surveillance: the monitoring or tracking of people, behavior, places, or data.
• prediction: the use of data patterns to estimate future actions, outcomes, or risks.
• privacy: the ability to control access to personal information and personal activity.
• training data: data used to teach a machine learning system to recognize patterns or generate outputs.
• correlation: a relationship between two patterns or variables that does not necessarily prove cause and effect.
• blockchain: a distributed record-keeping system that stores transactions across a network of computers.

6.7 Bibliography