“Making data work for you, not working on your data”

“Making data work for you, not working on your data” Jim Gibson Steve Wright

Session Agenda • Introduction • New ways of thinking about data collection in your organization • Case Example (Short Video): gapminder.org • How does data become wisdom? • Basics of data analysis • Example: Shelter Housing at LSYS • Moving to an information driven organization • Q&A

Data Demands in 2008 More than ever before, non-profits are being called upon to collect massive amounts of raw data and quickly convert it into useful and measurable information. This information must be analyzed and presented to funders and management in a variety of forms to show the impact of contributions, the success of programs, and the opportunities for growth.

Expanded Technical Opportunities • Significant amounts of data storage accessible to even the smallest of non-profits • New vendors in the database software field offering sophisticated applications at price points that fit typical non-profit budgets • Software integration facilitating data communications between multiple platforms and entities

With Expanded Technology Comes New Challenges • Government agencies, foundations, and individual donors increasingly rely on detailed data analysis and outcome-based reporting in their decision making for competitive funding • Non-profit boards, executive staff, and auditors increasingly look to comprehensive data reporting to measure success, efficiency, and compliance • Non-profits typically must direct more resources toward mission-based programs rather than database infrastructure and technological training of staff

“If We Only Knew What We Know”*… • How do you decide what data to collect? • How do you incent the data collectors? • How do you connect your impact (data) to a broader pool? Source: Carla O’Dell

A Virtuous Circle Nonprofit Intermediary Research common outcomes by programmatic area Apply research to data design Iterate Performance Reporting Annonymize and Publish Aggregate Aggregator

Incidental Data Collection

Payoff For All Stakeholders

Are We Any Good?

Knowing What Has Value • Social Return on Investment (SROI) • Cash has value • Happiness, Peace, Love are valuable • Contributing to a global solution • Are we willing to publicly expose the data that describes both our successes and our failures.

gapminder.org video

“If We Only Knew What We Know”*… • How do you take the extensive data you collect on a daily basis and turn it into the information that you need to make key decisions? • How do you know that your programs are successful? • How do you cultivate the wisdom required to fulfill your mission? Source: Carla O’Dell

From Data to Wisdom “Know Why” WISDOM Understanding Principals “Know How” Understanding Patterns “Know What, Know Who, Know Where, Know When” Understanding Relations “Know Nothing” Source: “Data, Information, Knowledge and Wisdom” by Bellinger, et al. 2004

Example of DIKW Framework • Data: “Joe Smith has exited services.” • Information: “Joe Smith transitioned from TLP housing to living independently on March 18, 2008 after 20 case management sessions and completing his individual service plan.” • Knowledge: “Of the 20 clients that exited services from TLP housing in FY08, 18 were successful transitions. Of those 18, all 18 completed 10 or more case management sessions and completed 75% of the goals in their individual service plans.” • Wisdom: Case management and goal setting is an important part of the experience of LSYS clients and there is a strong correlation between these services and successful exits from services.

Questions to Consider Back Home • Where does your organization spend most of its time and resources? • If your organization isn’t functioning from a place of wisdom, what are some of the obstacles that might be in place preventing you from moving to a higher level? • Are there adequate resources to move away from data to information? From information to knowledge? From knowledge to wisdom?

Basics of Data Analysis • Organize the raw data into meaningful groups • Describe the data in its groupings • Interpret the data • EXAMPLE: Shelter Housing, Nov. 2007

Shelter Occupancy, November 2007

Simple Analysis Using Excel • Arranging Data From Lowest Occupancy to Highest Occupancy: • Lowest Occupancy: 26 youth • Highest Occupancy: 37 youth (4 Days) • Median (Midpoint): 33 youth • Mode (Most Common Value): 33 & 35 (5) • Using built-in functions in Excel: • Average (Mean): 33.2 • Standard Deviation: 68% of the values fall between 30.2 and 36.2 (Standard Deviation of 3)

Data Interpretation • Present the data in several different formats… • Numerical Form • Visual Form • Narrative Form • Always remember to document your original data set and analytic process!

Shelter Occupancy Mean = 33 During the month of November, the shelter had an average of 33 youth housed. For the majority of the month, the shelter housed between 30 and 36 youth. The program is performing near its functional operating capacity of 35 youth.

Shelter Occupancy, November 2007

Shelter Occupancy by Day,November 2007 • MONDAYS: 35, 31, 35, 34 [AVG: 33.75] • TUESDAYS: 35, 37, 33, 33 [AVG: 34.5] • WEDNESDAYS: 35, 35, 33, 29 [AVG: 33] • THURSDAYS: 36, 31, 36, 30, 33 [AVG: 33.2] • FRIDAYS: 33, 28, 32, 36, 29 [AVG: 31.6] • SATURDAYS: 34, 26, 37, 31 [AVG: 32] • SUNDAYS: 37, 30, 36, 37 [AVG: 35]

Shelter Occupancy by Day, November 2007 During the month of November, the shelter occupancy varied significantly based on the day of the week. The weekend nights, Friday and Saturday, had to lowest utilitization rates. On “school nights” the shelter was consistently housing more than 30 youth. Sundays were on average the most popular evening at the shelter. To improve utilization, the curfew policies regarding weekend nights should be reviewed.

Getting from Information to Knowledge“Understanding Patterns” • Talk with the people doing the work: anecdotal information from program staff can provide launching point for investigation • Review data and look for patterns • Compare data sets over time to uncover trends • How you group the raw data and analyze it can lead to different insights, so trial and error can be important part of the process • Staying at the knowledge level requires continual reexamining of information and organizational review

Getting from Knowledge to Wisdom“Understanding Principals” • What are the factors that are contributing to the knowledge building activities? • Correlations and Cause/Effect • Letting knowledge influence practices • Disseminating knowledge • Staying at the wisdom level requires continual reexamining of the organization’s knowledge base

Becoming an Information Driven Organization: Data Components • Strong Data Collection Systems • Knowledgeable Staff/Consultants • Information Systems Investment • Servers/Software? • Outsourcing for Monthly Fee? • Staff Training and Resources • Quality Assurance Process • Culture Changes • Buy-in from Data Collectors • Performance Evaluation Component – Making adherence with data collection policies measureable and holding staff accountable

Becoming an Information Driven Organization • Systemize data processing into information • Get information into the hands of the people who need it • Feedback loop for additional QA work

Information Reporting at LSYS • Monthly Reporting for Management • Program Summary • Clients Served • Total Groups Held • All Services Provided • Staff Detail • Percentage of Intakes Completed • Client Reassessments Due • Program Exits

Information Reporting at LSYS • Monthly Reporting for Senior Team • Grant Dashboard • Every grant with goal vs. actual • Amount of time period that has elapsed • Variance based on percentage of goal relative to percentage of time that has elapsed • Conditional formatting • Green: 10% or Greater Variance • Yellow: -9.99% through less then 10% • Red: -10% or Less Variance

Information Reporting at LSYS • Quarterly Reporting for Management • Summary Statistics Report • Side by Side Comparison of Same Data Set with 4 Different Periods • Percentage Change for the most current 1 year of data and the most recent full fiscal year of data • Same conditional formatting scheme as Dashboard

Information Reporting at LSYS • Annual “State of the Agency” Presentation for All Staff Meeting • 2 hour presentation giving fiscal year in review and compares to fiscal years past (6 years data in dB) • Opportunity to see results of all data collection efforts for direct service staff • Celebrate success and discuss best practices • Review areas for growth and brainstorm solutions • Annual data awards to positively reinforce programs that are implementing data protocols successfully

Question and Answer

“Making data work for you, not working on your data”