MIS 0855 Lecture Notes - Lecture 1: Big Data, Theory-Theory, Falsifiability
Document Summary
Information- data that is processed to be useful. Data and big data don"t really matter unless you can turn them into information and knowledge. The dangers of (big data) analytics: something to be what it is) Start with a hypothesis users), falsifiable (there are no vampires living in louisiana), grounded in a rationale (students who attend class more often get better grades) It"s easy to find what"s not really there. The direction of causality can be tricky (what is the factor that influences. Dirty data is everywhere (errored, incomplete, biased, etc. ) Must be testable (iphone users download more apps each month than android. Different from a theory theory - accepted knowledge. Why are certain things better to use as kpi than others (things that cannot be tracked, too qualitative are not good) What is a scorecard and what does it measure. What is metadata and what is it used for.