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Transcript of The aim / learning outcome of this module is to understand how to gather and use data effectively to...
MODULE 3 – DATA, MONITORING AND EVALUATION
DR DARREN PERRIN
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
MODULE OUTLINE
• 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!!!
WHY IS DATA IMPORTANT TO COLLECT?
• 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
WHAT DATA DO I NEED?
• 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
WHAT WILL WASTE DATA TELL ME?
• 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
SOME CHALLENGES IN DATA COLLECTION
PLANNING EFFECTIVELY?
• 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
DATA LIMITATIONS
• 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
IMPORTANCE OF FORECASTING
FORECASTING A RANGE
• Recycling Rate
• Landfill Diversion Rate
• Dry Recycling Contamination Rate
• Participation Rate
• Capture Rate
• Recognition rate
• Collection Yield
KEY PERFORMANCE INDICATORS (KPI)
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?
DATA STRATEGY PROCESS
MONITORING AND EVALUATION PLANNING
What are you trying to find out? Define
Investigate
Assess
Learn
D
I
A
L
What tools / indicators are you going to use?
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
• 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?
Define D
• 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
Investigate Investigate I
• How are you going to analyse the data?• How are you going report it?
Assess A
Exercise
• Set out Rate
• Participation Rate
• Capture Rate
• Recognition Rate
• Contamination Rate
Remember how to calculate them? What’s new?
RELATIONSHIP BETWEEN 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 ?
EXERCISE – RELATIONSHIP INDICATORS
• Appropriate format to data • Show trend• Compare with baseline
• Data should be clear and consistent
• Tailor reporting to audience
REPORTING
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
Tuesday 62.9% 61.2% 50.7% 81.6%
Wednesday 52.8% 44.3% 43.4% 69.5%
Thursday 51.8% 56.4% 49.0% 72.3%
Week 1 Set Out Week 2 Set Out Week 3 Set Out Participation
• What are you going to differently in response to the monitoring and evaluation data
• Is there further monitoring required?• Continuous improvement • Ongoing
Learn L