3. Know how a data management project can help:  Improve program design  Demonstrate effectiveness  Highlight the best work being done  Compete for funding, and  Mobilize public support.

4. Learn about:  Identifying expected outcomes, and  defining objectives and incremental indicators of success consistent with your mission.

5.  Understand the elements and stages of a data management planning process  Be familiar with common barriers and costs associated with data management

6.  Learn how quality data can influence and inform the strategic planning process.  Explore options for tracking and using data efficiently at reasonable cost.

7.  City of Pittsfield Neighborhood evaluation ◦ Combining & analyzing data from multiple sources  DIAL/SELF (Greenfield, MA) Transitional Living Program housing outcomes ◦ Sorting and interpreting data from a single collection source (Lets go to visit source tables in Excel then come back to PowerPoint to review graphs)

9. 

12.  Bring key people together at each stage of planning process  Administration, program directors and supervisor participation is critical in early stages, but direct care staff can be helpful too (ask questions!)

13.  Initial planning stages require a deep understanding of the resources (funding, technology/equipment, and staff time) required to plan, implement and maintain a data management project/data driven culture.

14.  It may be worthwhile to invest in a consultant or devote substantial administrative time to produce useful estimates of the time and cost involved in implementing and maintaining a data driven culture

15.  Understand the purpose of your project - what will this data do for your organization?  Identify data priorities  Plan to start small and efficiently – you can grow as you learn and achieve - look for the intersection of what data you can easily obtain and what you would want to know in an ideal world! (go to flip chart )

16.  As you move into more detailed planning, direct care staff input becomes extremely important.  Involve staff in a formal way and carefully assess what support they will need to succeed!  Design formal systems for Training, Support and Accountability

17.  Reports/data you already need for funders  Identify information for internal evaluation and improvement (even if it isn’t currently required by funders)  Develop a functional draft of outcomes, objectives and indicators (your dataset) prior to shopping for a database or building a data collection system

18. A Brief Summary

19. OObjectives = desired participant changes or achievements IIndicators = measurable events OOutcomes = level of achievement

20. Inputs resources Outputs actions Outcomes achievements Basic Logic Model Inputs resources Outputs actions Indicators events Objectives expectations Outcomes achievements ImProve Outcomes SM Model *Identify tracking method

21.  Extension of logic models  Based on incremental change  Means of prioritizing information  Method of categorizing information

22. Levels of Learning Mastery ◦ Knowledge/Comprehension  (learn about it) ◦ Application  (use it, try it out) ◦ Synthesis  (integrate with other knowledge)

23.  S pecific  M easurable  A chievable  R elevant  T imely

24.  Use active verbs to describe indicators  Look for achievement opportunities at levels that are relevant to the services, time frame or intervention level of your program  Indicators reflect participant capacity for positive change and choices that indicate forward movement

25.  Web/Cloud Based ◦ Require reliable, high speed internet connection(s) ◦ Each user has own license – can access from anywhere ◦ Easy to monitor data entry ◦ Evaluate capability and cost of compilation, sorting and reporting ◦ Carefully evaluate ownership of data and “worst-case scenarios” (e.g., you or the provider go out of business?)  PC-Based ◦ You own software and data that is on your computer ◦ Speed depends on speed of machine ◦ May require additional software to run the database ◦ Can be difficult to synchronize data from multiple sources. ◦ Ease of data retrieval depends a lot on initial design and software used.

26.  1. Surveys:  Useful to capture information from participants  You have to ask the right question(s). That takes planning and some experimentation to gather aggregateable data.  Results can be compiled in Excel – but consider using Survey Monkey where you can get reports and export to excel.  2. Microsoft Access:  Good for demographic data and tracking objectives and indicator completion – data that changes or needs to be cross-referenced.  Inexpensive, but requires expertise to develop functional applications  Easy to retrieve data through queries

27. 3. Daily Logs (paper or software ) ◦ Most useful if data is aggregated and entered into a database or spreadsheet regularly (daily, weekly or monthly) ◦ Like surveys, the right questions have to be asked to get useful, accessible information ◦ With proper planning, could be used to track a variety of participant achievements. 4. Exit interviews! ◦ Build some of the questions to have aggregateable answers (e.g., multiple choice, name at least one xxx, etc.)

28. Please Contact: Doug Tanner dtanner@youthcatalytics.org 978-544-2067

29. Please Contact: Cindy Carraway-Wilson cwilson@youthcatalytics.org 203-561-6099