The document discusses various topics including transportation, infrastructure, healthcare, education, sustainability, and community development. It outlines goals for improving these areas over the next 5 years through strategic planning and allocating resources, with the overall aim of enhancing quality of life for residents.
Training material data validation and internal auditopportunityspm
Data validation is important for ensuring quality data analysis, reporting, and decision making. There are three key points for validating data: (1) during data collection by designing surveys with quality controls and training staff, (2) automatically through business rules when entering data to check for errors or inconsistencies, and (3) manually checking data forms. Validating data helps verify social goals are achieved and leads to a quality, end-to-end process for collecting, analyzing, and using data for reporting and decisions.
This document provides guidance on collecting social performance data. It discusses the importance of data collection, key ideas to consider, and how to improve efficiency, accuracy and consistency. Data collection is important to improve operations, services, decision making and understanding of social impact. Some tips for efficient collection include incorporating it into regular processes, avoiding duplication, and using technology like electronic data capture. Accuracy relies on staff training, buy-in and incentives to collect data properly.
The document discusses various topics including transportation, infrastructure, healthcare, education, sustainability, and community development. It outlines goals for improving these areas over the next 5 years through strategic planning and allocating resources, with the overall aim of enhancing quality of life for residents.
Training material data validation and internal auditopportunityspm
Data validation is important for ensuring quality data analysis, reporting, and decision making. There are three key points for validating data: (1) during data collection by designing surveys with quality controls and training staff, (2) automatically through business rules when entering data to check for errors or inconsistencies, and (3) manually checking data forms. Validating data helps verify social goals are achieved and leads to a quality, end-to-end process for collecting, analyzing, and using data for reporting and decisions.
This document provides guidance on collecting social performance data. It discusses the importance of data collection, key ideas to consider, and how to improve efficiency, accuracy and consistency. Data collection is important to improve operations, services, decision making and understanding of social impact. Some tips for efficient collection include incorporating it into regular processes, avoiding duplication, and using technology like electronic data capture. Accuracy relies on staff training, buy-in and incentives to collect data properly.
This document compares the implications of a census approach versus a sample approach to data collection. A census approach involves collecting data from all individuals and becomes part of regular business operations. It has higher initial costs but reduces costs over time. It ensures continuity and robustness for trend analysis. A sample approach collects data from only a few individuals and is seen as a separate activity. It has lower initial costs but requires constant attention. Sample data is less robust for trend analysis and continuity may be easier to stop. Overall, a census approach is more effective as data collection is embedded in regular business operations.
The document outlines a process for developing a social program including defining social goals, breaking goals down into measurable objectives, defining indicators to measure progress, setting targets, and listing activities. The process is numbered with 5 main steps: 1) define indicators, 2) break down into SMART objectives, 3) define social goals, 4) set targets, and 5) list activities.
1. The document provides guidance on planning stage 1 of standard 1B, which involves mapping indicators to outline data collection activities. It discusses choosing data sources, collection approaches, frequencies, and personnel.
2. Key steps in indicator mapping include choosing existing or new data tools, determining census or sampling methods, setting collection schedules, and assigning responsible staff. Integrating data collection into regular processes can minimize costs and burdens.
3. Decisions should consider resource constraints, existing data, stakeholder needs, impact on clients, and staff roles. Field staff are suited to most routine data while specialized studies may involve third parties. Proper planning leads to effective long-term social performance management.
This document provides guidance on planning stage 1 of standard 1B, which is indicator mapping. It discusses 8 steps to map each indicator's data cycle: 1) choosing the data source, 2) choosing a census or sampling approach, 3) deciding the frequency, 4) who will collect data, 5) embedding verification, 6) where data will be stored, 7) who will manage/analyze, and 8) reporting requirements. Going through this process will effectively plan the end-to-end process for collecting, processing, analyzing, and using social performance data for each indicator. An example mapping is also provided. The overall aim is to integrate social data collection into existing operations to minimize costs and maximize use of the data.