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The Data Assembly Line
Presented by Michael Moser from
The Vermont State Data Center at the University of Vermont
Developed in partnership with
Shelagh Cooley of CommonGood Vermont
About the Vermont State Data Center
We seek to identify and make accessible a wide array of data “indicators” useful to Vermont data users.
These data range from traditional social and economic indicators to newer, natural resource and quality of life indicators that together, can enable holistic analysis of Vermont conditions.
About Common Good Vermont
Common Good Vermont is a statewide network of nonprofits, consultants, funders and allies that enables community and nonprofit leaders to access their collective knowledge, build partnerships, solve problems and achieve long-term social benefit for the people of Vermont.
Connect. Learn. Make a Difference.
So Why Do We Even Need Data?
Stages of the Data Assembly Line
Identification
Collection & Management
Analysis
Reporting
Identifying Data
Data Characteristics
Data is: Any information you can collect that pertains to what it is you do.
Qualitative and Quantitative
Program Level
Population Level
Helps Tell a Story
Identifying Data Sources
Internal Data Examples
Client numbers & characteristics, program participation rates, satisfaction levels, dollars spent per program
External Data Examples
Population demographics such as poverty rate, educational attainment, etc.
Identifying Your Data
What data do you already collect?
What data isn’t being collected?
What other data sources exist?
Which data are relevant?
The Use Case for Prioritization
Data Collection & Management
Develop a system that takes into account:
Roles and Responsibilities
Who’s job is it?
Accessibility and Standardization
Data Collection Tools
Reducing Opportunities for Error
Redundancy and Security
Data Collection & Management
Roles and Responsibilities
Develop buy-in during the process
Make expectations clear
Provide adequate training & resources
Duplicate responsibility- Crosstrain
Revisit, revise (as needed), retrain
Data Collection & Management
Accessibility & Standardization
Standardized, simple data collection and management tools facilitate the process
Software
Data Collection & Management Tools
Surveys Lime Survey
Survey Monkey
Google Forms
Donor Databases Donor Perfect
RSVP Tools Eventbrite
Client Databases CiviCRM
MySQL
Microsoft Dynamics CRM
12
ExcelRespondent ID CityTown TotalIncome NumberInHouse FamilyType HousingType FoodStamps DOB Sex Ethnicity Disabled Education IncomeCodes
3658 Barre 0 2 1 1 N 22677 F I N 5 B
6537 Barre 2400 2 1 2 N 19378 F I N 3 ABK
9807 Barre
5689 Barre 0 3 3 1 N 22782 F I N 3 AB
9754 Barre
5123 Barre
1244 Barre 8208 2 1 2 Y 28143 F K N 3 BF
6363 Barre 0 1 4 1 N 36161 F I N 4 N
8229 Barre 8604 3 6 6 N 29532 F I Y 3 AD
9299 Barre
4599 Barre 12000 2 5 2 N 16723 F I N 3 C
8900 Barre
4321 Barre 13200 1 1 1 N 11614 F I N 3 CI
6789 Barre
6586 Barre 36000 4 3 1 N 39596 F I N 3 N
1166 Barre 12000 4 3 1 N 18124 F I Y 2 C
7864 Barre 48000 1 2 1 N 21577 M I N 5 A
2738 Barre
7882 Barre
2779 Barre 10704 3 3 2 N 27165 F I Y 2 D
8764 Barre 48000 1 2 1 N 24948 M I N 5 A
0086 Barre 21600 2 5 1 N 28685 M I Y 4 C
5193 Barre 13848 3 1 1 N 24135 F I Y 3 ACP
2670 BROOKFIELD 23736 2 2 2 N 28053 M I N 4 A
6298 Chelsea
0417 Danbury 46608 1 4 2 N 15017 F M Y 5 ABDN
8310 DUXBURY 0 3 3 1 N 25278 F I N 5 B
8679 East Montpelier
2385 Graniteville
5780 Montpelier
1696 Montpelier 0 1 4 1 15300 F I N 5 I
3379 Montpelier 36000 1 4 2 N 29299 F I N 5 A
7895 Montpelier
1679 Montpelier 2400 3 4 1 N 32911 F I N 3 B
Northfield
Lime Survey
Benefits of Using Tools
• Reduce human error, increase consistency.
• Standardizing data reduces time “cleaning data”
• Online (remote access for multiple sites).
• Automatic reports and organized data.
• Automatically backed up.
• They are user-friendly.
15
Data Management Dashboard
Data Collection & Management
Reducing Opportunities for Error
AKA- Uh Oh- the computer crashed!
AKA- Johnny just quit!
AKA- I didn’t mean to push “delete”!
AKA- Were all our Social Security
numbers on that Computer that was
just stolen?
Data Analysis
Key Considerations:
Who’s job is it to analyze?
Are the skills in place?
How often will you analyze?
This might be variable.
What story are you telling?
Who is the audience? What is the application?
What tech do we use?
Data Analysis Technology
What technology is right for us?
What technology do you have in place now?
Is that technology adequate for your needs?
Do multiple staff know how to use that technology?
Is the technology easily adapted, replicated, imparted to others?
Data Analysis TechnologyRespondent ID CityTown TotalIncome NumberInHouse FamilyType HousingType FoodStamps DOB Sex Ethnicity Disabled Education IncomeCodes
3658 Barre 0 2 1 1 N 22677 F I N 5 B
6537 Barre 2400 2 1 2 N 19378 F I N 3 ABK
9807 Barre
5689 Barre 0 3 3 1 N 22782 F I N 3 AB
9754 Barre
5123 Barre
1244 Barre 8208 2 1 2 Y 28143 F K N 3 BF
6363 Barre 0 1 4 1 N 36161 F I N 4 N
8229 Barre 8604 3 6 6 N 29532 F I Y 3 AD
9299 Barre
4599 Barre 12000 2 5 2 N 16723 F I N 3 C
8900 Barre
4321 Barre 13200 1 1 1 N 11614 F I N 3 CI
6789 Barre
6586 Barre 36000 4 3 1 N 39596 F I N 3 N
1166 Barre 12000 4 3 1 N 18124 F I Y 2 C
7864 Barre 48000 1 2 1 N 21577 M I N 5 A
2738 Barre
7882 Barre
2779 Barre 10704 3 3 2 N 27165 F I Y 2 D
8764 Barre 48000 1 2 1 N 24948 M I N 5 A
0086 Barre 21600 2 5 1 N 28685 M I Y 4 C
5193 Barre 13848 3 1 1 N 24135 F I Y 3 ACP
2670 BROOKFIELD 23736 2 2 2 N 28053 M I N 4 A
6298 Chelsea
0417 Danbury 46608 1 4 2 N 15017 F M Y 5 ABDN
8310 DUXBURY 0 3 3 1 N 25278 F I N 5 B
8679 East Montpelier
2385 Graniteville
5780 Montpelier
1696 Montpelier 0 1 4 1 15300 F I N 5 I
3379 Montpelier 36000 1 4 2 N 29299 F I N 5 A
7895 Montpelier
1679 Montpelier 2400 3 4 1 N 32911 F I N 3 B
Northfield
Data Analysis Technology
Data Reporting
Key aspects for consideration:
Who has the skills?
What is the story?
Who is the Audience?
What is the Application/Format?
What type(s) of data do you have?
How can you best present the data?
The Story
Justification
Success
Change Over Time
Intervention
Comparison
Need
Challenge
Story Timeline Example
Introduction
What is the (pre)existing conditions?-JUSTIFICATION
Climax
What is the intervention?- CHANGE
Ending
Where are you going?- NEED
Audience & Applications
Who is the Audience?
Legislators, Funders, Clients, Staff, Executive Board, etc.
Probably all of the above!
What is the Application/Format?
Annual Report, Executive Summary, Monthly Report, One-pager, etc.
Perhaps also: All of the Above!
Presentation
Text and Visuals make a great mix
Qualitative and Quantitative Data-
Mixed Methods Reporting
Keep it simple
Don’t push too much information into any one space- chart, table, graph, or page even
Visualizations
Visualizations
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Number of Food Insecure People
28
Visual Tools
Example Report Page
Example Report Page
Always Keep in Mind
Continue to adapt & improve process
Learn from others
Be ready to invest in the process
Be realistic & practical
Keep it simple
Don’t be afraid to seek help
Thank You
Questions?
Michael Moser- Vermont State Data Center at Center for Rural Studies- UVM