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HUMAN OBSESSION WITH PREDICTION - Coggle Diagram
HUMAN OBSESSION WITH PREDICTION
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)
Predictive model
the bread-and-butter of analytics
Assumption: Past behavior reasonably predicts the future
-Rely on historical data
Firms got better at collecting more data
Spreadsheets🡪data warehouses 🡪data centers
Led to a flurry of innovative practices in all functional area
Segment markets
Tier customers on lifetime value
Forecast demand for a product any hour
Good predictions need
sound data
Data: Raw facts
Ex: 24, 32, and 28 are data
Information: Sorted, condensed, and contextualized data
e.g., temps in your ZIP code
Collecting and managing data is the IT unit’s job
But not all data is worth managing
Not all information it produces is competitively valuable
What’s competitively valuable requires non-IT managers’ judgments
Without it, IT excels at collecting more without a clear business purpose
Troves of irrelevant data bog down business decisions
Proverbial needle in the wrong haystack
Insight: Actionable information
e.g., need a coat tomorrow
Unreliable data -> untrustworthy insight
sound reasoning
Business apps transform data 🡪information 🡪knowledge
Apps are “models” translated into software code
Predictions only as good as managerial insight in underlying models
Function-specific e.g., marketing, operations, accounting, or finance
Non-IT managers are the only sources of
Things competitively worth predicting (the DV in stats-speak)
Models in their functional domain