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Human Obsession with Prediction - Coggle Diagram
Human Obsession with Prediction
Good predictions need:
Sound data
Data become Information when they are sorted, condensed, and contextualized
Eg:
temps in your ZIP code
Data become Insight or Knowledge is simply actionable information
Eg:
need a coat tomorrow
Unreliable data -> untrustworthy insight
IT unit's job: collecting, cleaning, organizing, and making data flow
not all data is worth managing
Not all information it produces is competitively valuable
The non-IT manager must help your IT colleagues figure out what types of information are competitively valuable to your line function and how fast you need it
Data is raw facts
Eg:
24, 32, and 28 are data
Sound Reasoning
Insight does not emerge spontaneously from data without a lot of energy deliberately
directed to transform it
Software apps transform data into information then into business insight
Turning raw data into actionable insight must begin with questions
combining alternatives, information, and values to arrive at a decision
models translated into software codes
Predicting a baseball superstar, rain, a box-office hit, pricing insurance, catching spam
Predictive models
Assumptions
Past behaviour can predict future
Rely on historical data
The bread-and-butter of analytics
Firms got better at collecting data
Spreadsheets
Data center
Data warehouse
A 2000 year history
8BC: Ancient Greeks thronged to hear predictions from the Oracle of Delphi
Since then: Commonsense, nonsense, and wishful thinking
Prediction turned scientific with statistics in England around 1700AD
Hyper-progress around World War II (1939-1945)
Business analytics: Using data to predict outcomes your firm cares about