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Human Obsession with Prediction - Coggle Diagram

- - - - 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 doma
    - - Data: Raw facts
        
        – Ex: 24, 32, and 28 are data
      - • Information: Sorted, condensed, and contextualized data
        
        – e.g., temps in your ZIP code
      - • Insight: Actionable information
        
        – e.g., need a coat tomorrow
        
        – Unreliable data -> untrustworthy insight
      - • 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
    - - Predicting a baseball superstar, rain, a box-office hit, pricing insurance, catching
        spam