1. Dorothy A. Johnson Center for Philanthropy
The Potential of Community Information Systems (CIS) to Deepen Grantmaker Impact
Welcome and Introductions-
Intro Team
James Edwards, Ph.D.
Executive Director
E. Miles Wilson, MA
Director, Philanthropic Services
Dorothy A. Johnson Center for Philanthropy

2. Agenda Johnson Center Overview The Grantmaking Challenge
Community Information Infrastructure
Measuring/Monitoring Community Impact
Completing the Cycle: Data Driven Decision
Discussion/Questions
Review workshop agenda topics
Ask if there is anything else that attendees want to make sure are covered
Fun Fact: Origin of the word Data
Data  is a plural of datum,  which is originally a Latin noun 
meaning “something given.”
Today, data  is used in English both as a plural noun meaning 
“facts or pieces of information” 

3. Dorothy A. Johnson Center for Philanthropy
Welcoming Comments
Established in 1992 as an academic center for advancing philanthropy and nonprofit leadership. The Center is a Center for Excellence at Grand Valley State University and housed in the College of Community and Public Service.

4. OUR MISSION
The Johnson Center for Philanthropy is a university-based academic center serving nonprofits, foundations, and others that seek to transform their communities for the common good, and is adapting constantly to changing conditions.
We do this through applied research, professional development, and the advancement of social technologies.
Pause on this slide so they can read the cartoon.
Explain that we are going to take a few minutes to reflect on some thoughts about data

5. Why a Philanthropy Center?
One of 44 members of Nonprofit
Academic Centers Council
Pause on this slide so they can read the cartoon.
Explain that we are going to take a few minutes to reflect on some thoughts about data
Only comprehensive nonprofit and philanthropy center in Michigan
Second largest number of staff
Second largest operating budget
Third largest endowment (15 centers – endowments)
Second largest philanthropy library in the nation

6. The Johnson Center Services
Philanthropic and Nonprofit Services
Nonprofit Services
The Grantmaking School
Community Research Institute
Research & Evaluation
Data Systems & Solution
Thought Leadership Programming
The Foundation Review
Frey Chair for Family Foundations & Philanthropy
The Johnson Center Philanthropy Library & Archives
Pause on this slide so they can read the cartoon.
Explain that we are going to take a few minutes to reflect on some thoughts about data

7. Philanthropic and Nonprofit Services
Herbert H. and Grace A. Dow Foundation
Nonprofit services
Enhancing performance through education and technical assistance
Multiple resources available to nonprofit organizations:
Executive and Team Coaching
Technical assistance for organizations
Training and workshops on a variety of topics
Leadership development and organizational assessments
One of the nation's largest library collections of books, periodicals, and databases on philanthropy and nonprofit management.
Pause on this slide so they can read the cartoon.
Explain that we are going to take a few minutes to reflect on some thoughts about data

8. Philanthropic and Nonprofit Services
THE GRANTMAKING SCHOOL
Advancing impact through professional education
Our Courses:
Advanced Proposal Analysis: A Critical Explanation of Complex Issues
Advanced Grant Portfolio Management: A Framework for Strategic Grantmaking
Grant Financial Analysis
Evaluation for Grantmakers
Pause on this slide so they can read the cartoon.
Explain that we are going to take a few minutes to reflect on some thoughts about data
Customized Grantmaker Education
National Open Courses

9. Community Research Institute
Empowering communities with quality research and data
Pause on this slide so they can read the cartoon.
Explain that we are going to take a few minutes to reflect on some thoughts about data
The Community Research Institute can help you with:
Applied social research and data
Program and service evaluation
Obtaining and cleaning community data
Organizing, securing, and making data available
Standardizing community measurement and indicators
Mapping data and multiple data sets

10. Thought Leadership Programming
The Foundation Review
The first peer-reviewed journal of philanthropy
Pause on this slide so they can read the cartoon.
Explain that we are going to take a few minutes to reflect on some thoughts about data

11. The Frey Chair for Family Foundations & Philanthropy
The first peer-reviewed journal of philanthropy
Pause on this slide so they can read the cartoon.
Explain that we are going to take a few minutes to reflect on some thoughts about data
Michael Moody, Ph.D.
Frey Chair for Family Foundations & Philanthropy

12. Pause on this slide so they can read the cartoon.
Explain that we are going to take a few minutes to reflect on some thoughts about data

13. Future Challenges for Grantmakers
Pause on this slide so they can read the cartoon.
Explain that we are going to take a few minutes to reflect on some thoughts about data

14. How will Federal and State Budget Cutting Impact the Work of Charitable Foundations?
Re-examination by grantmakers of strategic versus responsive grantmaking.
Extreme shift to high leverage grant opportunities including collaborative and multi organization grants.
An expansion of grantmaker tool boxes to include diverse financial support and grant analysis models.
Increased funding for nonprofit mergers and reduced funding for new nonprofit efforts.
Pause on this slide so they can read the cartoon.
Explain that we are going to take a few minutes to reflect on some thoughts about data

15. IMPRESSIONS ABOUT DATA
We Got A Map for That
Pause on this slide so they can read the cartoon.
Explain that we are going to take a few minutes to reflect on some thoughts about data
IMPRESSIONS ABOUT DATA

16. “Not everything that counts can be counted, and not everything that can be counted counts” Albert Einstein (Physicist b d. 1955)

17. Charles Babbage (Mathematician b. 1792 d. 1871)
Errors using inadequate data are much less than those using no data at all
Charles Babbage (Mathematician b d. 1871)
Prompt Q’s
Do you agree or disagree? Why? Can you think of an example? Is this quote more or less relevant today?
Bad data (WMD), incomplete data (Pearl Harbor 911) and good data (Google People Finder in Japan)

18. How Community Information Systems Can Help?
We Got A Map for That
What community questions do you have about
How Community Information Systems Can Help?

19. What are your grantmaking priorities and strategies
What are your grantmaking priorities and strategies? How do you define community? What questions do you have about your community?

20. Potential sources of data

21. ACCESSING COMMUNITY DATA
We Got A Map for That
ACCESSING COMMUNITY DATA

22. What is a Community Information System (CIS)?
A repository (one stop shop) of multiple topics and data sources that is:
Organized
Integrated (GIS)
Up-to-date
Enriched
Displayed
Local-based content (community specific) but also state and national
This kind of development requires three kinds of innovations:
Technology and data
Institutional
Using information for change
Source: Kingsley, 2005

23. Conceptual View Questions Infrastructure Financial Support Community
Information Consumers
Education & Research
Application Development
Community Driven
Information Providers
Community
Questions
Infrastructure Financial Support

24. Community Information System Options
Type of Number of Data Sets
Type of Interface (Customization)
Frequency of Data Updates
Local Data Quality & Availability
Evaluation & Research
Education & Training
21/22
The Coordinating Council: CIS

25. Research Evaluation Services
Example:
Community View
Government
Private/
Christian Schools
Health
Human Services
CRI Data Standards Translator
CRI Data
Warehouse
School
Student Records
One structural option
Research Evaluation Services
Data Bridge
Data Bridge
Case Management
System
GRPS
Dashboards
Quarterly Reports,
Training
Dissemination & Scholarships, Online tools (i.e, MAPAS)
United Way
OST
Sites
Churches
Other
Key:
Future/Potential Contributors
Key System-Data Providers

26. Community Data Sharing Approach Measuring & Monitoring Community Impact

27. Grand Rapids Juvenile Offense Index
Community Questions

28. What is the youth involvement by age and type of offense?
Community Question:
What is the youth involvement by age and type of offense?

29. What times are juveniles more likely to commit offenses?
Community Question:
What times are juveniles more likely to commit offenses?

30. What times are juveniles more likely to commit offenses?
Community Question:
What times are juveniles more likely to commit offenses?

31. Geography Matters

32. Voting Patterns

33. Garfield Park Neighborhood Grand Rapids, MI
Concentration of
Juvenile Offenses
Garfield Park Neighborhood
Grand Rapids, MI
Community Information Systems

34. Community Information Systems
Concentration of
Juvenile Offenses
West Grand Neighborhood
Grand Rapids, MI
Community Information Systems

35. Community Indicators Approach Measuring & Monitoring Community Impact

36. COMMUNITY DATA AS A TRUST
We Got A Map for That
COMMUNITY DATA AS A TRUST

37. Grand Rapids Community Foundation
Foundation Dashboard
Example:
Novah.Info
Grand Rapids Community Foundation
18/22

38. Kent County Program/Initiative Shared Indicators Examples
1st Steps
United Way
GRCF
KSSN
CFP
B2B
Prevention Initiative
Talent 2025
GR Youth Master Plan
HEALTH & Behavioral Health
Substance Use/Abuse X *
Emergency room use for Primary Care
Abuse/Neglect
Access to Mental Health Services
PARENT/ADULT INVOLVEMENT
Treatment/Services
Parents can support social, emotional, physical and academic development of their children
EDUCATION
Positive social competencies among youth
Children ready for kindergarten/life
Academic achievement
School progressions
Graduation rates
Attendance rates
Suspensions/Expulsions
WORK/EMPLOYMENT
Trained & Ready Workforce
CRIME/CRIMINAL JUSTICE
Juvenile Offense

39. User Defined Online Tools Measuring & Monitoring Community Impact

40. Community Data Demonstration
Census
CRI Community Profiles
MAPAS Interactive Mapping Site
Census
a. MI loses population overall but geography matters
b. Demographic shifts
c. Housing shifts
d. Research briefs
2. Community Profiles
a. Explain the purposes and target audience for the tool: “for users that know a geo they want to get info for”
b. Explain data structure/sources: how is the info organized? define what info is available in community profiles nationally (first two tabs) vs what is available at the state, county, city, neighborhood levels. Emphasize our intent was to build a tool for local users.
c. Demo the functions: Use an interactive scenario to illustrate how to navigate through the tool. (Example: Lets say you are want to start a teenage pregnancy prevention program and you are looking for data to identify where you will offer program services based on need, or support your case statement for a grant you are writing. What kind of data might you need? Demo how they could find and compare data on teenage pregnancy. Explain difference b/w compare and trend. Demo one state to state comparison using a state from one of the participants (your call-population, race, ed attainment?).
d. Reflection Qs: What else might you use the system for? What other features would be helpful to see?
3. MAPAS
a. Explain the purposes and target audience for the tool: “for users that want the spatial distribution (mapping) to visualize information about a geo area”
b. Explain the data structure/sources: how is the info organized? emphasize what data is available for mapping, at what levels (ie-nbhd to national)
c. Demo the functions: Use interactive scenarios to illustrate how to navigate through the tool. (Example 1: Census data 1990, 2000, Highlight the major differences. Ask participants to consider the implications of these shifts. Why is this data important? Why else? What other Q’s does this raise for you? What else would you like to know? Example 2: You are working on a Get Out the Vote campaign and want to look at historical data for the last election so you know who has not participated in the past so you can tailor an awareness strategy to under-represented populations. Have them pick a neighborhood or use the voter turnout data from Garfield Park as an example. Ask what does this data tell us? How would we use this. What else would you like to know)

41. Census Highlights 2000-2010 Population Demographics Households
Grand Rapids/Under 18
Kent County Total Pop.
Demographics
Hispanics Pop.
African American Pop.
Households
Size
Units and Vacancies

42. Community Profiles CIS-intersection b/w topical content and geography
National
State
Regional
County
City
Neighbor-hood
Block
Individual
Community Profiles
Community Questions
Demographics
Economics
Education
Housing
Birth Statistics
Crime/Safety
Civic Engagement
CIS-intersection b/w topical content and geography
The visual behind the center graphic is a visual of the internet
Geographies in red are unique attributes of CRI
Not all content is available at all geographies
Analytics:
Mathematics
Algorithm
Statistics
Databases
Data

43. We Got A Map for That
Handoff to Jay
USING COMMUNITY DATA

44. Using Community Data to Understand and Improve Conditions
Poverty
Home Foreclosure
Prenatal Care
Juvenile Offense Index
Chronic Absenteeism
Achievement Gap
Transportation
Community Indicators
Voting Patterns
Food security/distribution
Earned income tax credits
Summer Learning Loss

45. Case Example #1 Using school data to define the scope of the problem and begin the conversation about solutions

46. Early Chronic Absenteeism
This is a 4 year study that counts the number of juvenile crime offenses in Grand Rapids (y axis) by year (x axis).
At a glance, what does this data illustrate?
-Fewer offenses are being committed: unique offenses decreased from 1,017 to 723
-Fewer youth are involved in offenses: youth involved in offenses decreased from 1,532 to 847
-The gap between the number of youth involved in offenses and the total number of offenses decreased
-Status offense rate decreased from 42.2 to 28.4 per 1,000 youth
-The number of youth involved in offenses decreased at a faster rate than the total number of offenses committed
The idea is to practice making meaning from data.

47. Early Chronic Absenteeism
The number of juvenile crime offenses in Grand Rapids (y axis) by time of day (x axis). Point out 2006 is yellow line, 2009 is blue line. What do you see? What does this chart tell you? What else? What other data might help you understand what is happening here?
Data shows increased youth involvement in offenses in the hours immediately after the school day ends and beginning again in the early evening-trend is consistent across 4 years.
2009 shows an overall decrease in the frequency of school day offenses-trend is consistent across 4 years
What happened? What might have contributed to the decrease? Who would find this data valuable?
Who do you think commissioned this study and why? ELO to make the case for afterschool programs. Does that surprise you? Who else might benefit from this data?

48. Case Example #2 Using local data to identify priority geographies and service populations
Kent County
Early Childhood Indicators

49. The indices are comprised of multiple SES variables
All the factors suggest that children will not be prepared to learn

51. Completing the Cycle
Data Driven Decisions

52. Awareness, Interpretation, Integration
Data driven decision making begins with awareness (visual on the left)
During the interpretation stage, we create meaning to the data and so it can become informative. It can be valuable to get multiple perspectives when interpreting data.
Integration is often a cyclical process like the visual on the left, and will be influenced by the factors as outlined in the visual on the right

53. Data Driven Disruption
Data driven decision making requires change. Change is hard in part because it is disruptive and uncertain. Polarity management-there will always be forces for and against change, so the ability to lead change in yourself and others is an important skill.

54. Managing Change

55. We Got A Map for That
What does the future hold?
COMMUNITY DATA TRENDS

56. Community Indicator Resources
National Neighborhoods Indicator Partnership (NNIP)
Open Indicators Consortium (OIC)
Urban Institute
Lots of Vendors
Key is sustainability

57. Questions? Think of data like pieces of a jigsaw puzzle
Data helps us make meaning, see the picture and understand our world
It can help us prove or disprove a point, a hypothesis or a position
So What, Now What? Ask P’s to write down one take away they learned and one thing they are going to do in the next 24 hours as a result of this workshop

58. THANK YOU