The document discusses how stories are more effective than data at moving people to care, remember, and act. It notes that the analytical, non-emotional nature of data makes it less likely to inspire these responses. However, combining data with stories can help people both understand issues and feel compelled to address them. As an example, the document presents data on inequities in gifted student identification rates among gender, racial/ethnic, and socioeconomic groups in American schools, showing imbalances that could be addressed by focusing on underrepresented populations.

1. Moving People with Data
DataCon LA 2020 Keynote
Jenny Grant Rankin, Ph.D.
www.JennyRankin.com
jennygrantrankin
jennygrankin
jennygrantrankin
@JennyGRankin

2. = Analytical
Non-Emotional
Cerebral Cortex
Heath & Heath, 2018
Data

3. Less likely to
n care about
n remember
n act on
Data
Heath & Heath, 2018

4. n care about
n remember
n act on
likely toMore
Stories
Heath & Heath, 2018

5. Image Credit: National Geographic
2015

6. Image Credit: National Geographic
2015
22 million

7. Image Credit: National Geographic
2015
22 million
13.5 million
needed
humanitarian
assistance

8. Image Credit: National Geographic
2015
22 million
13.5 million
ý
6
5

9. (Heath & Heath, 2018)

11. Students Recognized as Gifted
55%
45%
17%
9%
92%
8%
(Callahan, Moon, & Oh, 2014; Card & Giuliano, 2015; Civil Rights Data Collection, 2012; Grissom & Redding, 2016; NCES 2017)
Gender Race/Ethnicity Socio-Economic
Male Female White Latinx Black
Poor
Not
Poor Poor
Lines indicate % of total student population
61%

12. Gail

13. Gail Thompson, Ph.D.

14. Gail Thompson, Ph.D.

15. Students Recognized as Gifted
55%
45%
17%
9%
92%
8%
(Callahan, Moon, & Oh, 2014; Card & Giuliano, 2015; Civil Rights Data Collection, 2012; Grissom & Redding, 2016; NCES 2017)
Gender Race/Ethnicity Socio-Economic
Male Female White Latinx Black
Poor
Not
Poor Poor
Lines indicate % of total student population
61%

16. Students Recognized as Gifted
55%
45%
17%
9%
92%
8%
(Callahan, Moon, & Oh, 2014; Card & Giuliano, 2015; Civil Rights Data Collection, 2012; Grissom & Redding, 2016; NCES 2017)
Gender Race/Ethnicity Socio-Economic
Male Female White Latinx Black
Poor
Not
Poor Poor
Lines indicate % of total student population
61%

17. Students Recognized as Gifted
55%
45%
17%
9%
92%
8%
(Callahan, Moon, & Oh, 2014; Card & Giuliano, 2015; Civil Rights Data Collection, 2012; Grissom & Redding, 2016; NCES 2017)
Gender Race/Ethnicity Socio-Economic
Male Female White Latinx Black
Poor
Not
Poor Poor
Lines indicate % of total student population
61%

18. Students Recognized as Gifted
55%
45%
17%
9%
92%
8%
(Callahan, Moon, & Oh, 2014; Card & Giuliano, 2015; Civil Rights Data Collection, 2012; Grissom & Redding, 2016; NCES 2017)
Gender Race/Ethnicity Socio-Economic
Male Female White Latinx Black
Poor
Not
Poor Poor
Lines indicate % of total student population
61%

19. Students Recognized as Gifted
55%
45%
17%
9%
92%
8%
(Callahan, Moon, & Oh, 2014; Card & Giuliano, 2015; Civil Rights Data Collection, 2012; Grissom & Redding, 2016; NCES 2017)
Gender Race/Ethnicity Socio-Economic
Male Female White Latinx Black
Poor
Not
Poor Poor
Lines indicate % of total student population
61%

21. Moving People with Data
DataCon LA 2020 Keynote
Jenny Grant Rankin, Ph.D.
www.JennyRankin.com
jennygrantrankin
jennygrankin
jennygrantrankin
@JennyGRankin