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

Facebook

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

mmoser@uvm.edu