1. Problem
   1. Data has been fetishized
   2. Lack of the right skills
   3. Thinking skills
      1. Domain knowledge
      2. Critical thinking skills
      3. Scientific thinking skills
      4. Statistical thinking skills
      5. Systems thinking skills
      6. Visual thinking skills
      7. Ethical thinking skills
   4. Only focusing on tools
   5. Data sensemaking
      1. Exploratory
      2. Directed
2. Think Critically
   1. Two modes of thinking
      1. System 1
      2. System 2
   2. Logical forms of argument
      1. Deduction
      2. Induction
      3. Abduction
   3. Avoid reasoning errors
      1. Familiarity errors
      2. Statistical errors
      3. Causal errors
3. Applying the Scientific method to Data Sensemaking
   1. Propose and test hypotheses
   2. Conduct experiements
   3. Use representative data
   4. Control the variables
   5. Attempt to disconfirm hypotheses
   6. Search for causes
   7. Publish the findings
      1. Statement of purpose
      2. Hypotheses that we tested
      3. Research design, steps we took
      4. Conclusions and thoughts
      5. Evidence on which our conclusions were based
      6. Assumptions
      7. Lessons learned in addition to conclusions
      8. Limitations of the work
      9. Suggested future work
      10. A means to access the data we examined
   8. Collaborate
4. Question the Data
   1. Required Data
   2. Relevance of the Data
   3. Semantics of the Data
   4. Source of the Data
   5. Accuracy of the Data
   6. Completeness of the Data
   7. Context of the Data
   8. Representativeness of the Data
   9. Causes of the Behaviors Recorded in the Data
   10. Aggregation of the Data
   11. Expression of the Data
5. Proper use of Metrics
   1. Proxies
   2. Measure at the right level
   3. Avoid Common Measurement Errors
      1. Inappropriate Use of the Mean
      2. Sole Reliance on Measures of Central Tendency
      3. Conflating Rankings with Measure of Worth
   4. Monitor Effectively
   5. Respond Effectively
6. Thinking habits
   1. Prevent Distraction
   2. Taking notes
   3. Sleeping on it
7. Develop data-sensemaking culture
   1. Teach others
      1. Focus on Substance Over Style
      2. Provide Context
      3. Emphasize Particular Data with Care
      4. Express uncertainty
      5. Preserve Complexity
      6. Explain the Basics of Statistical Thinking
      7. Encourage Questions
      8. Identify Errors
   2. Encourage Constructive Critique
   3. Create a Risk-Free Zone for Admitting Mistakes
   4. Secure Enough Time and Space
   5. Invest in Data-Sensemaking Skills