1. IT Architecture
The technical infrastructure that enables data analytics
Data Sources
Program databases, donor management systems, spreadsheets
Data Sources
Various systems where raw data is initially collected and stored.
Key Questions:
- What data are we collecting across all our programs?
- Where is our donor information stored?
- Are we tracking finances in multiple systems?
Costs to Consider:
Database licenses, maintenance, storage costs
Value:
Foundation for all analytics - data quality here impacts everything downstream
Data Warehouse
Central repository that integrates all data sources
Data Warehouse
A unified storage system that brings together data from all your different sources.
Key Questions:
- How do we bring together program data from 31+ countries?
- Can we link donor activity to program outcomes?
- How much historical data should we maintain?
Costs to Consider:
Cloud storage costs, ETL tools, integration consultants
Value:
Single source of truth that enables cross-functional analysis
Analytics Tools
Software that processes and analyzes data
Analytics Tools
Software that helps transform raw data into meaningful insights.
Key Questions:
- Do we need sophisticated statistical tools or simpler options?
- Should we use open-source or commercial analytics packages?
- How will staff access and use these tools?
Costs to Consider:
Software licenses, training, server costs for computation
Value:
Convert raw data into actionable insights that inform decisions
Visualization Tools
Dashboards and reports that display insights
Visualization Tools
Software that presents data in visual formats like charts, maps, and dashboards.
Key Questions:
- What metrics do program directors need to see daily?
- What visualizations will help donors understand impact?
- How will we share dashboards with different stakeholders?
Costs to Consider:
Dashboard software, design time, user licenses
Value:
Makes complex data understandable for better, faster decisions
2. Data Analytics Process
The step-by-step journey from raw data to actionable insights
Data Discovery
Finding and inventorying available data
Data Discovery
Identifying and cataloging all relevant data sources across your organization.
Key Questions:
- What data do we collect but never use?
- Where are there gaps in our data collection?
- Who owns each data source?
Costs to Consider:
Staff time for inventory, data cataloging tools
Value:
Uncovers hidden data assets and identifies critical gaps
Data Cleaning
Correcting errors and standardizing formats
Data Cleaning
Detecting and correcting errors, inconsistencies, and missing values in your data.
Key Questions:
- How consistent is our data collection across programs?
- What data quality issues are most common?
- How can we improve data entry processes?
Costs to Consider:
Data cleaning tools, staff time, potential consultants
Value:
Ensures analysis is based on accurate information - "garbage in, garbage out"
Exploratory Analysis
Investigating patterns and relationships
Exploratory Data Analysis
Initial investigation of data to discover patterns, anomalies, and relationships.
Key Questions:
- What trends exist in our program outcomes over time?
- Are there correlations between funding and impact?
- Which variables seem most important to investigate?
Costs to Consider:
Analyst time, visualization tools, computing resources
Value:
Generates initial insights and guides deeper analysis
Detailed Analysis
In-depth examination of specific questions
Detailed Analysis
Focused investigation of specific questions using statistical methods.
Key Questions:
- Which programs deliver the most impact per dollar?
- What factors drive donor retention?
- How do regional differences affect program outcomes?
Costs to Consider:
Data analyst time, specialized software, technical expertise
Value:
Delivers concrete answers to critical business questions
Prediction
Forecasting future outcomes and trends
Prediction & Forecasting
Using models to forecast future outcomes and guide strategic planning.
Key Questions:
- How will program outcomes change with increased funding?
- Which donors are at risk of not renewing?
- What will be our resource needs next year?
Costs to Consider:
Advanced modeling expertise, AI/ML tools, validation time
Value:
Enables proactive planning and optimization of resources
3. Data People
The roles and responsibilities needed at each stage
Data Engineers
Build and maintain data infrastructure
Data Engineers
Technical specialists who build and maintain data pipelines and infrastructure.
Key Responsibilities:
- Connect data sources to central warehouse
- Automate data flows between systems
- Ensure data security and reliability
For a Nonprofit:
Consider fractional or consultative data engineering support to build initial infrastructure without full-time cost.
Alternative:
Utilize cloud-based ETL tools with lower technical requirements.
Data Analysts
Analyze data to extract insights
Data Analysts
Professionals who transform data into actionable insights through analysis.
Key Responsibilities:
- Create reports and dashboards
- Perform statistical analysis
- Answer specific business questions
For a Nonprofit:
Train existing program staff with analytical skills; this builds capacity and ensures domain expertise.
Time Investment:
Plan for 20-30% of analyst time to be spent on training/upskilling team members.
Data Scientists
Build models that predict outcomes
Data Scientists
Advanced analytics professionals who develop predictive models and algorithms.
Key Responsibilities:
- Develop machine learning models
- Forecast future outcomes
- Find complex patterns in data
For a Nonprofit:
Consider project-based consultants or academic partnerships instead of full-time hires at early stages.
AI Alternative:
Utilize pre-built AI solutions that require less technical expertise to get started.
Tech Stack Visualization: Revenue Analysis
A concrete example of how data flows through the system to provide actionable insights
Donor Management System
Donation amounts, frequency, donor demographics
Campaign Tracking System
Campaign costs, channels, messaging, timing
Financial System
Expenses, revenue records, budgets
Integrated Data Warehouse
Unified repository connecting all revenue sources and contextual data
Analysis Notebooks
Jupyter notebooks for data exploration and modeling
Statistical Models
Predictive models for forecasting and optimization
Revenue Intelligence Dashboard
Campaign ROI Comparison
Donor Retention Rate
Interactive dashboard that highlights revenue drivers and opportunities
Fundraising Director
CFO
Executive Director
Revenue Optimization Outcomes
Optimized Marketing Spend
Reallocated $50K to highest-performing email campaigns, increasing ROI by 28%
Improved Donor Retention
Personalized outreach to at-risk donors increased retention rate from 67% to 78%
Revenue Impact
Overall 15% increase in annual donations with only 3% increase in fundraising costs