The Data Assembly Line

Size: px

Start display at page:

Download "The Data Assembly Line"

Samuel Jenkins
5 years ago
Views:

1 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

2 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.

3 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.

4 So Why Do We Even Need Data?

5 Stages of the Data Assembly Line Identification Collection & Management Analysis Reporting

6 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

7 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.

8 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

9 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

10 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

11 Data Collection & Management Accessibility & Standardization Standardized, simple data collection and management tools facilitate the process Software

12 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

13 Excel Respondent ID CityTown TotalIncome NumberInHouse FamilyType HousingType FoodStamps DOB Sex Ethnicity Disabled Education IncomeCodes 3658 Barre N F I N 5 B 6537 Barre N F I N 3 ABK 9807 Barre 5689 Barre N F I N 3 AB 9754 Barre 5123 Barre 1244 Barre Y F K N 3 BF 6363 Barre N F I N 4 N 8229 Barre N F I Y 3 AD 9299 Barre 4599 Barre N F I N 3 C 8900 Barre 4321 Barre N F I N 3 CI 6789 Barre 6586 Barre N F I N 3 N 1166 Barre N F I Y 2 C 7864 Barre N M I N 5 A 2738 Barre 7882 Barre 2779 Barre N F I Y 2 D 8764 Barre N M I N 5 A 0086 Barre N M I Y 4 C 5193 Barre N F I Y 3 ACP 2670 BROOKFIELD N M I N 4 A 6298 Chelsea 0417 Danbury N F M Y 5 ABDN 8310 DUXBURY N F I N 5 B 8679 East Montpelier 2385 Graniteville 5780 Montpelier 1696 Montpelier F I N 5 I 3379 Montpelier N F I N 5 A 7895 Montpelier 1679 Montpelier N F I N 3 B Northfield

14 Lime Survey

15 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

16 Data Management Dashboard

17 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?

18 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?

19 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?

20 Data Analysis Technology Respondent ID CityTown TotalIncome NumberInHouse FamilyType HousingType FoodStamps DOB Sex Ethnicity Disabled Education IncomeCodes 3658 Barre N F I N 5 B 6537 Barre N F I N 3 ABK 9807 Barre 5689 Barre N F I N 3 AB 9754 Barre 5123 Barre 1244 Barre Y F K N 3 BF 6363 Barre N F I N 4 N 8229 Barre N F I Y 3 AD 9299 Barre 4599 Barre N F I N 3 C 8900 Barre 4321 Barre N F I N 3 CI 6789 Barre 6586 Barre N F I N 3 N 1166 Barre N F I Y 2 C 7864 Barre N M I N 5 A 2738 Barre 7882 Barre 2779 Barre N F I Y 2 D 8764 Barre N M I N 5 A 0086 Barre N M I Y 4 C 5193 Barre N F I Y 3 ACP 2670 BROOKFIELD N M I N 4 A 6298 Chelsea 0417 Danbury N F M Y 5 ABDN 8310 DUXBURY N F I N 5 B 8679 East Montpelier 2385 Graniteville 5780 Montpelier 1696 Montpelier F I N 5 I 3379 Montpelier N F I N 5 A 7895 Montpelier 1679 Montpelier N F I N 3 B Northfield

21 Data Analysis Technology

22 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?

23 The Story Justification Success Change Over Time Intervention Comparison Need Challenge

24 Story Timeline Example Introduction What is the (pre)existing conditions?- JUSTIFICATION Climax What is the intervention?- CHANGE Ending Where are you going?- NEED

25 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!

26 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

27 Visualizations

28 Visualizations 180,000 Number of Food Insecure People 160, , , ,000 80,000 60,000 40,000 20,

29 Visual Tools

30 Example Report Page

31 Example Report Page

32 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

33 Thank You Questions? Michael Moser- Vermont State Data Center at Center for Rural Studies- UVM

Keeping Score: Best Practices for Risk Management Reporting

Keeping Score: Best Practices for Risk Management Reporting 1/4 Keeping Score: Best Practices for Risk Management Reporting John Schaefer Risk Management Information Systems (RMIS) are designed to capture,