3. Value of Data

3. Value of Data

1. Lecture #6

   • Organizations and systems
     • An organization can be looked at as a system.
       • A system is a set of interconnected components with a well-defined interface with its environment.
     • Data is obtained by introducing measurements within the organization (system components, interconnections, interface with environment).
   • Scientific method
     • How to: (1) formulate hypothesis, (2) perform experiments to confirm/inform hypothesis
   • The dangers of measuring everything
     • 3 ways to improve a measurement
       • distorting the data
       • distorting the system
       • improving the system (least likely)

2. (Lecture #7)

   • Data governance
     • It's a challenge to define/enforce languages and norms about data
     • Data governance determines data decision domains and their assign responsibilities
     • Data decision domains (what programs should be defined in terms of data management)
       • Data principles (what data?)
       • Data Quality (Consistency, Accuracy, Timeliness, Completeness, Credibility)
       • Metadata (Structural/Lineage/User metadata)
       • Data access (values and risks)
       • Life Cycle (Accounting for changes in time)

3. Lecture #9

   • Primary use of data
   • Reuse of data
   • Limit to data usefulness
   • The value of data exhausts
   • Towards book value for data
   • Data Marketplaces
   • Open data

4. Lecture #11

   • Correlation
     • Positive correlation
     • Negative correlation
     • Spurious correlation
   • Causation