Podcast
Analytics for Nonprofits: Build Data Maturity, Teams, Tools & Optimization Strategies
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Analytics for Nonprofits: Build Data Maturity, Teams, Tools & Optimization Strategies
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Episode Overview
How can nonprofits move from basic reporting to optimization using analytics while building the right teams, tools, and governance? In this episode, Parvathy Krishnan, CTO at Analytics for a Better World and professional doctorate in data science, walks through practical steps for building data maturity in the social sector. Drawing on discovery workshops, fellowship pilots (including a waste-collection optimization project in Nairobi), and partnerships with academic and industry groups, Parvathy explains how to.
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Chapter Summary
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- 0:00 - Podcast Introduction
- 1:10 - Overview: Analytics for a Better World mission and guest intro
- 1:54 - Career Path: From renewable energy to data science and CTO role
- 4:38 - CTO Responsibilities: Connecting nonprofits with research and tech capacity
- 6:20 - Discovery Workshops: Assessing nonprofit needs and data maturity
- 9:29 - Fellowship Case Study: Waste-collection optimization pilot in Nairobi
- 12:33 - Data Maturity Comparison: Nonprofit vs. private-sector analytics
- 15:23 - Talent & Purpose: Motivating data professionals to join the public sector
- 17:53 - Academy Structure: Programs for practitioners, analytics translators, executives
- 20:14 - Open Resources: YouTube lectures, GitHub, and open-source deliverables
- 22:26 - Curriculum Focus: Descriptive → diagnostic → predictive → optimization
- 25:36 - Audience Profile: MBA, business analytics, and technical students
- 28:19 - Student Engagement: Thesis collaborations and researcher pathways
- 30:47 - Maturity Roadmaps: Scans, short/long-term goals, and cost optimization
- 34:06 - People Dimension: Roles for data collection, analysis, and app development
- 36:34 - Process Dimension: Data governance, privacy, SOPs, and workflows
- 38:22 - Technology Dimension: Centralized data, version control, and tech selection
- 39:28 - Tool Recommendations: Dashboards, Python/R, and cloud deployment options
- 44:18 - Data Platforms: KoboToolbox, PostgreSQL, and Digital Public Goods guidance
- 45:51 - Team Profiles: Analysts, data scientists, engineers, and blended roles
- 49:15 - Data Engineering Needs: Moving from research to deployed applications
- 50:06 - Optimization Use Cases: Healthcare access and COVID testing lab placement
- 52:50 - Partnerships & Staffing: Ortec, academic partners, and on-demand talent network
- 54:07 - Organizational Model: Small core team and large extended research network
- 55:38 - Becoming Data-Driven: Strategy plus investments in people, processes, technology
- 58:22 - Recommended Reading & Daily Resources: Culture Map, 7 Habits, Towards Data