1. John Massman, Ph.D.
Giving voice to data
Real-life examples creating the
“wow” factor

2. Example
 Organization: Non-profit service provider
 Context: Presenting a cash flow report to the board of
directors.
 Task: Present data, analysis and consequences without putting
people to sleep.
 I prepared an “emphatic graph” that combined the key
information and the foreseeable consequences.
 The board had a pointed discussion, instituted structural
changes, and the organization thrived.

3. Standard Table vs. Emphatic Graph
Data (internal only) Presented to Board
today

4. Case Study
 Organization: Non-profit adult-child mentoring program
 Goal: long-term mentoring relationships.
o Much effort is expended in establishing relationships and initial
management of the relationship.
o Short relationships are ineffective and consume scarce
resources.
 Approach: analyze voluminous data of both adult and child.

5. Results and Outcomes
 Results:
o Identified demographic characteristics of ideal long-term adult-
child matches.
o Quantified benefits of the long-term relationships.
 Outcomes:
o Dramatic increase in effectiveness of matching efforts.
o Quantified benefits reported to external stakeholders including
donors.

6. Making an initial adult-child match
 Analyze 1700+ recent adult-child matches each with dozens
of demographic items.
 Identify key characteristics that correlate with a long-term
relationship.
 All data is non-linear, non-logarithmic, non-parametric.
 Comparison with current practices would be especially
valuable.

7. One graph makes a difference
• Green markers All Matches
are long-term 21
successes.
19
• Red markers are
short-term (low 17
“ROI”).
15
• This plot directly
Age of Child
resulted in a 13 Successes
In Progress
programmatic 11 Misses
change to avoid
pairing older 9
adults with
7
younger
children. 5
15 25 35 45 55 65 75 85 95
Age of Adult

8. Quantifying Outcomes
Raw data Effective Presentation
Social Acceptance Social Acceptance
90 (different groups of children)
40%
80
35%
70
Relative Frequency of children
30%
60
No. of children
25%
50
Guided < 1 yr 20%
40 Under 1 yr
Guided 1+ yrs
30 15% At least 1 yr
20 10%
10 5%
0 0%
1 1.5 2 2.5 3 3.5 4 1.0 1.5 2.0 2.5 3.0 3.5 4.0
Survey Score Survey Score
(similar charts were done for several characteristics)

9. Data Mining and Geocoding
Location of students
served together with
school attendance
areas.
Student color
indicates number of
target demographics
student has.
Results used for
geographically-
concerned purposes.