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Making Data Work For You - The Data Assemblyline

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So you've learned the Results-Based Accountability framework. The next step is to build systems of accountability within the organization? This short course offers the "brass tacks" in building a data collection, presentation and analysis assembly-line with your staff. Michael Moser, from the Vermont State Data Center and Shelagh Cooley from Common Good Vermont provide examples, tools and concrete next steps that you can implement immediately. Watch the video here: http://www.cctv.org/watch-tv/programs/make-data-work-you#

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Making Data Work For You - The Data Assemblyline

  1. 1. Making Data Work for You The Data Assembly Line 5/1/14 Lauren-Glenn Davitian, host Shelagh Cooley, Common Good Vermont Michael Moser, Vermont State Data Center 1
  2. 2. Logistics Watch on your computer. Interaction – We want to hear from you! Comments and Questions – Chat Box Slides and Recording Afterwards. Satisfaction Survey. 2
  3. 3. The Data Assembly Line  Setting the Stage  Data Audit  Data Collection  Data Management  Data Analysis  Data Utilization 3
  4. 4. Setting the Stage Why is data important? What results do you seek? Who is your audience? How will they use the information? 4
  5. 5. Question Cat Who is YOUR audience? 5
  6. 6. QuickTime™ and a decompressor are needed to see this picture. “I can honestly say that not a day goes by when we don’t use those evaluations in one way or another.” 6
  7. 7. Example: Foodshelf Result: Food security for people in the region. Audience: Board, funders, policy makers, staff 7
  8. 8. PERFORMANCE MEASURES Foodshelf # Clients Served lbs. of Food Provided % Nutritious Food % Clients Satisfied with food choice Staff-Client Ratio # Clients not returning -after 6 months - after 1 year % of Clients not returning -after 6 months - after 1 year QUANTITY (#) QUALITY (%) 8
  9. 9. Question Cat Who is YOUR audience? 9
  10. 10. The Data Assembly Line  Setting the Stage  Data Audit  Data Collection  Data Management  Data Analysis  Data Utilization 10
  11. 11. Data Audit - People What people do you have? How are they involved? Do they have “will” & “skill”? Is it in their job description? 11
  12. 12. Data Audit - Data • What data do you already have? • What data do you need? • What data is most useful? 12
  13. 13. QuickTime™ and a decompressor are needed to see this picture. 13
  14. 14. Data Source Tracking Sheet Fremework Evaluation Question Data Source Indicator Food How much food do we serve? Inventoroy # pounds of food/day Clients How many clients do we serve Registration Forms #clients/month Satisfaction How satisfied are clients with the food they receive? Satisfaction Survey % of very satisfied clients/month Quality of Food How nutritious is the food? Receipts to Clients % of Food distributed that is nutritious Food Security How many clients are food secure after 6 months? 1 year? # Clients Receiving less than 5 lbs of food per month 14
  15. 15. Question Cat Where do you collect your data from? 15
  16. 16. Data Sources •Calendars: Events, Meals, Clinics •Internal Databases: Clients •Attendance Sheets: Youth •Log Sheets: Services Received •Surveys: Satisfaction 16
  17. 17. Question Cat Where do you collect your data from? 17
  18. 18. The Data Assembly Line  Setting the Stage  Data AuditData Audit  Data Collection  Data Management  Data Analysis  Data Utilization 18
  19. 19. Data Collection Data You Already Collect Data Development Agenda Data Audit 19
  20. 20. Data Collection  Value of the Performance Measure  System  Buy-in  Standard 20
  21. 21. Question Cat What tools do YOU use to collect dat? 21
  22. 22. Data Collection Tools 22
  23. 23. 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. • It’s EASIER!!! 23
  24. 24. Question Cat What tools do you use to collect data? 24
  25. 25. The Data Assembly Line  Setting the Stage  Data AuditData Audit  Data Collection  Data Management  Data Analysis  Data Utilization 25
  26. 26. Data Management Is your data secure? What is your time frame? Do you have back-ups? Who is responsible? 26
  27. 27. Example of Dashboard 27
  28. 28. Data Management Don’t mix up your data types. 28
  29. 29. PERFORMANCE MEASURES Foodshelf # Clients Served lbs. of Food Provided % Nutritious Food % Clients Satisfied with food choice Staff-Client Ratio # Clients not returning -after 6 months - after 1 year % of Clients not returning -after 6 months - after 1 year QUANTITY (#) QUALITY (%) 29
  30. 30. Data Management Example January 1-15 January 16-31 January Average February 1-15 February 16-28 February Average 2008 258 275 266.5 350 375 362.5 2009 245 272 258.5 324 370 347 2010 242 267 254.5 356 368 362 2011 235 265 250 333 362 347.5 2012 222 260 241 332 360 346 2013 216 262 239 313 345 329 2014 224 252 238 310 370 340 30
  31. 31. Data Management Example 31
  32. 32. Data Management Example January 1- 15 January 16- 31 February 1- 15 February 16- 28 March 1- 15 2008 258 275 350 375 297 2009 245 272 324 370 297 2010 242 267 356 368 279 2011 235 265 333 362 234 2012 222 260 332 360 285 2013 216 262 313 345 262 2014 224 252 310 370 155 32
  33. 33. Data Management Example 33
  34. 34. Data Management Best Practices 1. Training 2. Buy-In 3. File Naming 4. Security Practices 5. Code Book 34
  35. 35. Stretch Break Send in Your Questions! 35
  36. 36. The Data Assembly Line  Setting the Stage  Data AuditData Audit  Data Collection  Data Management  Data Analysis  Data Utilization 36
  37. 37. Analyzing the Data Remember, who is your audience? How often will the data be analyzed? Time Series vs. Non-Time Series Data 37
  38. 38. Example of Survey Monkey 38
  39. 39. Trend Lines 39
  40. 40. The Data Assembly Line  Setting the Stage  Data AuditData Audit  Data Collection  Data Management  Data Analysis  Data Utilization 40
  41. 41. Data Utilization Decision Making Fundraising Community Building Education 41
  42. 42. Data Utilization Convene your audience. What story does the data tell? Are we achieving the results we want? What will we do differently? 42
  43. 43. Why Visualize Data? “A picture is worth a thousand words” Higher Engagement from audience. Broader audience. 43
  44. 44. Examples of Visualization 44
  45. 45. Examples of Visualization 45
  46. 46. Example of Visualization 46
  47. 47. Sources: http://thegrio.com/2013/06/07/hunger-in-america-food- insecurity-disproportionately-affects-african- americans/#s:foodinsecurity2 http://www.vtfoodatlas.com/plan/chapter/4-1-food-security-in- vermont Percentage   Food  Insecurity Very Low  Food  Security 1999-2001 9.1 1.8 2001-2003 8.9 3 2003-2005 9.5 3.9 2005-2007  10.2 4.6 2007-2009 13.6 6.2 2009-2011 12 5.4 Infographic Example: -Simple -Key information is highlighted -Effective Messaging 47
  48. 48. Visualizing Data Tools 48
  49. 49. Rules to Live By Continue to adapt & improve measures Learn from others Be willing to invest if the information has value to you. Keep it Simple. Be realistic & practical 49
  50. 50. Assembly Line Not Automatic 50
  51. 51. PERFORMANCE MEASURES HOW MUCH ARE WE DOING? HOW WELL ARE WE DOING IT? HOW WELL ARE WE DOING IT? HOW WELL ARE WE DOING IT? QUANTITY (#) QUALITY (%) 51
  52. 52. PERFORMANCE MEASURES Afterschool # Youth served # Hours of Programming (academic vs. non- academic) # of Enrichment activities % High Quality Interaction % Youth Satisfaction % Parent Satisfaction Staff-youth Ratio # Youth Honor Roll # Youth 90% School Attendance # Youth leading activities % Youth Honor Roll % Youth with 90% School Attendance % Youth leading activities QUANTITY (#) QUALITY (%) 52
  53. 53. PERFORMANCE MEASURES Mental Health # Clients Served Size of Waiting list Average time to next appointment # of Clients in school or working # of Clients into institutional care # of Clients to less restrictive care % of Clients in school or working % of Clients into institutional care % of Clients to less restrictive care QUANTITY (#) QUALITY (%) 53
  54. 54. Upcoming Events Let’s Talk Shop: Regional Mixers - Brattleboro 6/19 Leadership Vermont Luncheons -Burlington 5/21 -Brattleboro 6/20 -Barnet 10/1 54
  55. 55. Resources Idealware http://www.idealware.org/ Helping Nonprofits Make Smart Software Decisions Youtube Video: How to Add a Trend Line in Excel: https://www.youtube.com/watch?v=svFSKnmAlKQ An Introduction to Regression Analysis: Alan O. Skyes: http://www.law.uchicago.edu/files/files/20.Sykes_.Regression.pd f CGVT Know More, Do More with Data Visualization http://blog.commongoodvt.org/2013/10/video-storytelling-with- data/ CGVT Surveys and Spreadsheets https://www.cctv.org/watch-tv/programs/making-data-work-you- survey-spreadsheets 55
  56. 56. References Trying Hard is Not Good Enough, Mark Friedman http://resultsleadership.org/product/trying-hard-is-not-good- enough-by-mark-friedman/ Essentials of Utilization-Focused Evaluation (Sage, 2012) by Michael Quinn Patton Edward Tufte, Data Visualization: http://www.edwardtufte.com/tufte/index American Evaluation Association – Tools, Tips, Trainings http://www.eval.org/ Also, check out courses offered at area colleges. 56

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