Big Data: A Revolution That Will Transform How We Live, Work, and Think
One book, placed in its field — what Viktor Mayer-Schönberger, Kenneth Cukier's book gets right, where it goes further than the rest, and what the rest of the field adds.
Mike West, Editor · Leading With Data · peopleanalyst.com/guides/profile-big_data_a_revolution_that_will
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Big Data: A Revolution That Will Transform How We Live, Work, and Think
One book, placed in its field — what Viktor Mayer-Schönberger, Kenneth Cukier's book gets right, where it goes further than the rest, and what the rest of the field adds.
Mike West, Editor · Leading With Data · peopleanalyst.com/guides/profile-big_data_a_revolution_that_will
The problem
Economic value creation erodes when it is left to instinct instead of method.
You were taught the moves piecemeal, never the whole model.
Big Data: A Revolution That Will Transform How We Live, Work, and Think · Mike West, Editor · Leading With Data
The model
- 01Using All the Data (N=all)
- 02Embracing Messiness (Imprecision Tolerance)
- 03Prioritizing Correlation Over Causation
- 04Datafication
- 05Big-Data Mindset
- 06Data Reuse and Recombination
- 07Prediction-Based Decision-Making
- 08Economic Value Creation
Big Data: A Revolution That Will Transform How We Live, Work, and Think · Mike West, Editor · Leading With Data
Step 1 of 5 · Using All the Data (N=all)
Use the full dataset when your value comes from outliers, rare events, or fine-grained segments—not when you only need a population mean.
Misconception
Practitioners assume that if a random sample is statistically valid, adding more data only yields marginal, not-worth-it returns.
Reality
The payoff of N=all is not better averages but the ability to zoom into any subgroup after the fact—sampling forecloses questions you didn't know to ask when you drew the sample.
Big Data: A Revolution That Will Transform How We Live, Work, and Think · Mike West, Editor · Leading With Data
Step 2 of 5 · Embracing Messiness (Imprecision Tolerance)
Trade micro-accuracy for macro-scale when you need the direction of a pattern, not the exact value of a record.
Misconception
Practitioners believe more errors in the data necessarily mean worse results, so cleaning must precede analysis.
Reality
At scale, aggregate patterns are robust to individual inaccuracies, and the extra volume messy data admits usually improves outcomes more than error-free precision on a smaller set would.
Big Data: A Revolution That Will Transform How We Live, Work, and Think · Mike West, Editor · Leading With Data
Step 3 of 5 · Prioritizing Correlation Over Causation
Correlation is sufficient for forecasting and detection but insufficient for deciding what lever to pull.
Misconception
Practitioners think correlation is a weak, provisional finding that is only useful once you've confirmed the underlying cause.
Reality
For prediction and detection, a stable correlation is directly actionable; the demand for causal proof is often a philosophical luxury the operational decision does not require.
Big Data: A Revolution That Will Transform How We Live, Work, and Think · Mike West, Editor · Leading With Data
Step 4 of 5 · Datafication
Datafication, not digitization, is what unlocks latent analytic value—check which one you've actually done.
Misconception
Practitioners equate datafication with going digital—scanning documents, moving records online—assuming they've already 'datafied' their operations.
Reality
Digitizing renders something into bits; datafying renders it into structured, tabulated form you can measure and compute on—a scanned book is digital, but a searchable, quantified corpus is datafied.
Big Data: A Revolution That Will Transform How We Live, Work, and Think · Mike West, Editor · Leading With Data
Step 5 of 5 · Big-Data Mindset
The most valuable secondary uses of data are usually invisible to those who created it.
Misconception
Practitioners assume deep domain expertise is the key qualification for extracting value from a domain's data.
Reality
Incumbents are often blinded by knowing how data has 'always' been used; the breakthrough uses tend to come from outsiders unconstrained by that convention.
Big Data: A Revolution That Will Transform How We Live, Work, and Think · Mike West, Editor · Leading With Data
A framework to use
Big Data Governance Framework
This framework is part of the full guide. See the guide →
Big Data: A Revolution That Will Transform How We Live, Work, and Think · Mike West, Editor · Leading With Data
The cheat sheet
- Using All the Data (N=all)Use the full dataset when your value comes from outliers, rare events, or fine-grained segments—not when you only need a population mean.
- Embracing Messiness (Imprecision Tolerance)Trade micro-accuracy for macro-scale when you need the direction of a pattern, not the exact value of a record.
- Prioritizing Correlation Over CausationCorrelation is sufficient for forecasting and detection but insufficient for deciding what lever to pull.
- DataficationDatafication, not digitization, is what unlocks latent analytic value—check which one you've actually done.
- Big-Data MindsetThe most valuable secondary uses of data are usually invisible to those who created it.
- Data Reuse and RecombinationMost of a dataset's economic value lies in uses beyond the one it was collected for.
- Prediction-Based Decision-MakingLet predictions drive decisions where they demonstrably beat human judgment, not everywhere.
- Economic Value CreationCompetitive advantage in a data economy tends toward whoever controls the scarcest input—often the data itself.
Big Data: A Revolution That Will Transform How We Live, Work, and Think · Mike West, Editor · Leading With Data
Sources
- Big Data: A Revolution That Will Transform How We Live, Work, and Think — Viktor Mayer-Schönberger, Kenneth Cukier
Big Data: A Revolution That Will Transform How We Live, Work, and Think · Mike West, Editor · Leading With Data