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What is the
importance of data?
David Henderson
It has none.
“Data is only useful if it
informs decision
making”
- Every Carnegie Mellon University
professor of public policy
There are lots of cool analytic techniques
• Econometric modeling
• Linear programming
• Machine learning
• Social network...
• Selecting data points
• Assigning value to data
I will focus on two data prerequisites
Selecting data points
Selecting data points is difficult in the social
sector because a lot of data points are
proxies
Example
We trackWe care a...
Tracking the wrong data points leads to the
wrong decisions
Example
We trackWe care about
Arrest ratesCrime reduction
Publ...
People felt safer in higher arrest areas
Focusing on the wrong indicators
can lead to negative (but rational!)
decisions
Funder wanted a workforce development
program to maximize job placements
Assigning value to data
Before diving into data, we need to be
explicit about our own utility frameworks
Likely overly simplistic
A utility framework is how we value data
points, including interactions therein
The vulnerability index is a good example of
a utility framework in homeless services
Prioritizes those most likely to die...
Without establishing an organizational utility
framework, everyone makes decisions
according to their own values
Different...
How do we model a utility framework?
Utility elicitation is common in the
business world
Model a decision maker’s risk reward preferences through
a series of h...
The key is to find points of indifference
Housing one chronically homeless person is equivalent to
placing three people in...
Developing a utility framework enforces
consistent investment decisions
Makes investment priorities clear
Can be tested an...
• Select (likely a series) of data points that approximate
what we really care about
• Build a model that assigns value to...
End
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The importance of data

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The importance of data

  1. 1. What is the importance of data? David Henderson
  2. 2. It has none.
  3. 3. “Data is only useful if it informs decision making” - Every Carnegie Mellon University professor of public policy
  4. 4. There are lots of cool analytic techniques • Econometric modeling • Linear programming • Machine learning • Social network analysis • Etc.
  5. 5. • Selecting data points • Assigning value to data I will focus on two data prerequisites
  6. 6. Selecting data points
  7. 7. Selecting data points is difficult in the social sector because a lot of data points are proxies Example We trackWe care about Graduation ratesEducation Job placementsEconomic independence
  8. 8. Tracking the wrong data points leads to the wrong decisions Example We trackWe care about Arrest ratesCrime reduction Public perceptionFeeling safe
  9. 9. People felt safer in higher arrest areas
  10. 10. Focusing on the wrong indicators can lead to negative (but rational!) decisions
  11. 11. Funder wanted a workforce development program to maximize job placements
  12. 12. Assigning value to data
  13. 13. Before diving into data, we need to be explicit about our own utility frameworks Likely overly simplistic
  14. 14. A utility framework is how we value data points, including interactions therein
  15. 15. The vulnerability index is a good example of a utility framework in homeless services Prioritizes those most likely to die on the street
  16. 16. Without establishing an organizational utility framework, everyone makes decisions according to their own values Different utility functions + same data = Different decisions
  17. 17. How do we model a utility framework?
  18. 18. Utility elicitation is common in the business world Model a decision maker’s risk reward preferences through a series of hypothetical investment scenarios
  19. 19. The key is to find points of indifference Housing one chronically homeless person is equivalent to placing three people into jobs Points of indifference allow us to compare unlike indicators
  20. 20. Developing a utility framework enforces consistent investment decisions Makes investment priorities clear Can be tested and refined through time
  21. 21. • Select (likely a series) of data points that approximate what we really care about • Build a model that assigns value to data points, and update it through time Before diving into data:
  22. 22. End

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