What will I learn about: how analyzing data on a massive scale unlocks unprecedented predictive insights, like forecasting viral outbreaks, that are completely invisible at smaller scales?

Discover how analyzing data on a massive scale unlocks unprecedented predictive insights, like forecasting viral outbreaks, that are completely invisible at smaller scales.

What will I learn about: the rising trend of datafication, where everyday physical actions from the way you sit to how you walk are continuously captured to drive product innovation?

Understand the rising trend of datafication, where everyday physical actions from the way you sit to how you walk are continuously captured to drive product innovation.

What will I learn about: Learn why big data eliminates the flawed reliance on traditional sampling, empowering you to analyze entire populations and confidently draw conclusions about highly specific subgroups?

Learn why big data eliminates the flawed reliance on traditional sampling, empowering you to analyze entire populations and confidently draw conclusions about highly specific subgroups.

What will I learn about: why leveraging massive volumes of uncurated, messy data ultimately produces far more accurate and useful results than relying on small, meticulously accurate datasets?

Find out why leveraging massive volumes of uncurated, messy data ultimately produces far more accurate and useful results than relying on small, meticulously accurate datasets.

What will I learn about: Recognize why simply knowing that two data points are strongly correlated is often enough to make highly effective decisions, even when big data cannot explain why they are connected?

Recognize why simply knowing that two data points are strongly correlated is often enough to make highly effective decisions, even when big data cannot explain why they are connected.

Big Data

A Revolution That Will Transform How We Live, Work and Think

Viktor Mayer-Schönberger and Kenneth Cukier

★ 4.3 / 5(128 ratings)

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Technology & the Future

If You're Curious About These Questions...

You should listen to this audiobook

💡Did you know that in the era of big data, understanding *why* something happens is becoming far less important than simply knowing *what* will happen next?
💡Have you ever wondered how the world's most successful companies are turning 'messy' piles of information into a goldmine of profit and innovation?
💡What if you could predict future trends and human behaviors with startling accuracy, and what are the hidden risks of living in such a data-driven society?

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Key Takeaways from Big Data

✓Discover how analyzing data on a massive scale unlocks unprecedented predictive insights, like forecasting viral outbreaks, that are completely invisible at smaller scales.
✓Understand the rising trend of datafication, where everyday physical actions from the way you sit to how you walk are continuously captured to drive product innovation.
✓Learn why big data eliminates the flawed reliance on traditional sampling, empowering you to analyze entire populations and confidently draw conclusions about highly specific subgroups.
✓Find out why leveraging massive volumes of uncurated, messy data ultimately produces far more accurate and useful results than relying on small, meticulously accurate datasets.
✓Recognize why simply knowing that two data points are strongly correlated is often enough to make highly effective decisions, even when big data cannot explain why they are connected.

Learning Tools

Reinforce what you learned from Big Data

Mind Map

Big Data

The Shift in Analysis+

Datafication & Value Extraction+

Enhancing Products & Services+

Risks & Ethical Pitfalls+

Quiz — Test Your Understanding

Question 1 of 10

According to the text, what is the inherent problem with traditional data sampling methods that big data helps to overcome?

A. They require highly expensive computers to process even small amounts of information.
B. They lack sufficient observations to draw meaningful conclusions when examining very specific subgroups.
C. They rely too heavily on statistical probabilities rather than established grammar rules.
D. They inherently violate modern privacy laws by collecting too much personally identifiable information.

Question 2 of 10

What key lesson about big data was demonstrated by comparing IBM's 1980s translation program with Google's later approach?

A. A vast amount of messy data can be more useful than a smaller, highly accurate dataset.
B. Translation algorithms must rely strictly on grammar rules rather than statistical probabilities.
C. Data must be perfectly clean and accurate to train an effective artificial intelligence system.
D. Small, high-quality samples are more effective for predicting infrequent occurrences.

Question 3 of 10

How does big data change our approach to understanding relationships between variables, such as the discovery that orange used cars have fewer defects?

A. It requires us to develop strict theories of cause and effect before analyzing the data.
B. It proves that human intuition is always superior to automated data analysis.
C. It demonstrates that correlation only matters if the underlying causation can be scientifically proven.
D. It shows that knowing *that* two things are correlated is often practical enough, even if we don't know *why*.

Question 4 of 10

What is 'data exhaust' in the context of online services?

A. Outdated information that must be deleted to free up server storage space.
B. Passively collected behavioral data, like mouse movements and typos, used to optimize services.
C. The environmental impact and carbon footprint generated by large data processing centers.
D. Data that has been completely anonymized to comply with modern privacy laws.

Question 5 of 10

Which of the following best describes a 'big-data mindset' as illustrated by individuals who started companies like FlightCaster?

A. The belief that a company must own massive proprietary server farms to succeed in the modern economy.
B. The strategy of keeping all collected data strictly confidential to prevent competitors from using it.
C. The ability to spot opportunities to extract value from publicly available data, even without owning it initially.
D. The focus on collecting small, highly accurate samples rather than vast, messy datasets.

Question 6 of 10

Why did a Danish research group combine mobile phone user data with national cancer patient records?

A. To prove that mobile phones directly cause brain cancer in young adults.
B. To demonstrate that combining comprehensive datasets can reveal trends not visible in individual datasets alone.
C. To find secondary uses for mobile phone location data to predict traffic jams in major cities.
D. To show how easily anonymized medical data can be re-identified by malicious actors.

Question 7 of 10

According to the text, why are current privacy laws and anonymization methods becoming ineffective in the big-data era?

A. Because anonymized data can often be re-identified due to the high level of detail in big data.
B. Because users rarely read the lengthy user agreements before consenting to data collection.
C. Because privacy laws strictly forbid the collection of passive data exhaust like mouse movements.
D. Because anonymization completely destroys the statistical value of the data, making it useless for analysis.

Question 8 of 10

What major ethical risk is associated with using big data for 'predictive policing' and profiling?

A. It wastes sparse police resources by focusing too heavily on low-crime neighborhoods.
B. It relies too heavily on grammar rules rather than statistical probabilities to catch criminals.
C. It requires police departments to share sensitive tactical data with large tech companies.
D. It threatens free will by judging and penalizing individuals based on what they are predicted to do, rather than what they have done.

Question 9 of 10

How did Robert McNamara's strategy during the Vietnam War illustrate the perils of being overly data-driven?

A. He refused to look at any data, relying entirely on his personal intuition to make military decisions.
B. He became fixated on an unreliable metric (enemy body count), which incentivized false reporting and shaped a flawed strategy.
C. He focused too much on the secondary applications of data rather than the primary military objectives.
D. He used predictive algorithms that arrested enemy combatants before they actually engaged in battle.

Question 10 of 10

What does the term 'datafication' refer to in the context of the book?

A. The process of capturing real-world information, such as walking patterns or seating pressure, in the form of data.
B. The legal process of anonymizing personal information before it can be published or sold to third parties.
C. The act of deleting obsolete data that is no longer useful for primary or secondary applications.
D. The transition from using statistical probabilities to relying on rigid, rule-based algorithms.

Big Data — Full Chapter Overview

1Recommendation
21 — Data is increasingly collected and used across every part of life, from the size of our bums to the way we walk.
32 — Big data frees us from relying on small samples to represent entire populations.
43 — Huge sets of messier data can be more useful than smaller, more accurate sets.
54 — Big data does not explain why two things are related, only that they are – and that is often enough.
65 — Though data is usually collected for a specific purpose, its secondary uses can be even more valuable.
76 — Anyone can spot fresh ways to create value from the data around them – with the right mindset.
87 — Combining data sets can produce more value than each one offers on its own.
98 — Online services like Facebook record everything we do on their sites and use that data to improve what they offer.
109 — Today's privacy laws and anonymization methods are ineffective and inefficient for big data.
1110 — Big data can help predict criminal behavior, but we must never judge people before they actually commit a crime.
1211 — Being too data-driven can be dangerous: we may measure the wrong thing, reward the wrong behavior, or rely on flawed data.
1312 — Final summary

Big Data Summary & Overview

Big Data provides an insightful look at why a change to “big data” is a major shift in how we collect, use and think about the data around us. It provides great explanations and examples of how individuals and companies already ahead of the curve are using the tools of big data to create value and profit. Casting an eye forward, the book also outlines the future implications for a big-data society in terms of the risks, opportunities and legal implications.

Who Should Listen to Big Data?

Anyone who is interested in learning more about what “big data” is and what it means for society
Anyone who is looking to make a career using big data
Anyone from a company who is looking for opportunities to use the data the company collects

About the Author: Viktor Mayer-Schönberger and Kenneth Cukier

Viktor Mayer-Schönberger was on the faculty of Harvard’s Kennedy School for over ten years before taking up the position of professor of Internet Governance and Regulation at Oxford University. He is also the author of Delete: The Virtue of Forgetting in the Digital Age.

Kenneth Cukier is the data editor of the Economist, and writes widely about what is happening in the world of big data. His articles, covering technology, business and economics, have appeared in the New York Times, Foreign Affairs and the Financial Times.

Big Data

If You're Curious About These Questions...

Listen to Big Data — Free Audiobook

Key Takeaways from Big Data

Learning Tools

Mind Map

Quiz — Test Your Understanding

Big Data — Full Chapter Overview

Big Data Summary & Overview

Who Should Listen to Big Data?

About the Author: Viktor Mayer-Schönberger and Kenneth Cukier

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