Module 3 – Data, Monitoring and Evaluation Dr Darren Perrin

1. Module 3 – Data, Monitoring and Evaluation Dr Darren Perrin

2. Module Outline • The aim / learning outcome of this module is to understand how to gather and use data effectively to plan the development of recycling and composting schemes; identify improvements and then use data effectively to monitor and assess the performance of these schemes. • Understand the different types of data and why it is important to collect data • Understand the limitations of data • Explain why monitoring and evaluation is important • Understand how to translate data into action

3. Why is data important to collect? • Provides the basis of any sound decision making • “If you can’t measure it ... You can’t manage it” • Commercial / Public sector - Increased efficiency = saves money! • Reduces risk, increases certainty, subject to: • Understanding the limitations of no / poor data: • Inaccurate estimates • Incompatibility of infrastructure and markets • Poor planning and missed opportunities • No data better than poor data!!!

4. What Data do I Need? • Type of Data • Waste Generation & Flows • Waste Composition • Financial • Social Profiling • Capacity and Infrastructure • End Markets • Performance Assessments • Fit for purpose? • Affordability and Priorities • Be aware of poor data / Data gaps

5. What will waste data tell me? • Current position on waste material flows • Issues and Opportunities • Inefficiencies • Ability to track changes and impact of new policy, strategy objectives / targets • Performance against Key Performance Indicators (KPI) • Ability to plan and forecast

6. SOME Challenges in data collection • How to Prioritise • Weight versus volume • Data not static • Composition • System performance • Population / household • Financial • ……? • External influences over time • Change in material revenue • Change • Material Properties • Bulk density • CV • Chemical properties

7. Planning effectively?

8. Data Limitations • Data not always available / affordable • No data sometimes better than poor data • Gaps in data may require assumptions to be made: • Waste composition • Number of households or business waste generation rate • Potential performance e.g. Material capture rates • Where assumptions are critical to outcomes, sensitivity analyses can be used to: • Provide range of values on which to base decision • Highlight potential areas of risk

9. Importance of forecasting • Need to forecast the quantities of waste to help in planning • Future quantities of waste dependent on: • - Waste generated • - Households (rather than population) • - Business activity • - Economic Activity • - Specific elements of waste stream (e.g. recycled content) • - Waste prevention activities • Predict a range not single line growth • Use previous trends to inform assumptions • Dependant on future workload, business expansion, type of activities/production

10. Forecasting a Range

11. Key Performance Indicators (KPI) • Recycling Rate • Landfill Diversion Rate • Dry Recycling Contamination Rate • Participation Rate • Capture Rate • Recognition rate • Collection Yield

12. Data Strategy • Document which clearly sets out data requirements and approach to obtaining it • What data is required and priorities ? • Why is the data required (Mandatory, Required, Useful, “Nice to have”) ? • When will the data be collected and at what repeat frequency ? • Who will collect the data ? • How will the data be collected ? • Units of measurement • How will the data be reported? • How much will the data cost to collect? ROI?

13. Data Strategy PRocess

14. Monitoring and Evaluation Planning D Define What are you trying to find out? I Investigate What tools / indicators are you going to use? A Assess What are you going to do with the information? What have you learnt and what is going to change as a result of the new information L Learn

15. D Define • What question are you trying to answer? • What would the answer look like? • Is it SMART? • What are you going to measure? • Do you need to compare data and is this data available?

16. I Investigate Investigate • What indicators are you going to use? • Will your indicators selected answer your question? • How are you going to use them? • Quantitative or qualitative data? • Single or multiple sources of data? • Plan to collect data • Costs • Audits • Field data

17. A Assess • How are you going to analyse the data? • How are you going report it?

18. Relationship Between Indicators • Exercise • Set out Rate • Participation Rate • Capture Rate • Recognition Rate • Contamination Rate • Remember how to calculate them? What’s new?

19. Exercise – Relationship Indicators • Low set out, low participation, high recognition • High set out, low contamination, high participation, low recognition • High participation, low set out, high recognition, High contamination • Low capture, high set out • Consider: • Describe scenarios and implications • How would you resolve each situation ? • What would be preferred ?

20. Reporting • Appropriate format to data • Show trend • Compare with baseline • Data should be clear and consistent • Tailor reporting to audience

21. L Learn • What are you going to differently in response to the monitoring and evaluation data • Is there further monitoring required? • Continuous improvement • Ongoing