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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

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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

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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

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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

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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"

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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

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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

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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

IT Architecture Data Discovery

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

Exploration Analysis

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

Analysis Prediction

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

Revenue Source Data

Campaign Tracking System

Campaign costs, channels, messaging, timing

Marketing Data

Financial System

Expenses, revenue records, budgets

Financial Data

Integrated Data Warehouse

Unified repository connecting all revenue sources and contextual data

Donor History
Campaign Metrics
Financial Records

Analysis Notebooks

Jupyter notebooks for data exploration and modeling

Revenue Trend Analysis

Statistical Models

Predictive models for forecasting and optimization

Campaign Effectiveness Prediction

Revenue Intelligence Dashboard

Campaign ROI Comparison

ROI %
Campaign
Email
Social
Events
Print

Donor Retention Rate

78%

Interactive dashboard that highlights revenue drivers and opportunities

Fundraising Director

Decision: Shift budget to email campaigns

CFO

Decision: Approve new donor retention program

Executive Director

Decision: Set new revenue targets by channel
Data-driven decisions flow back to improve future campaigns and revenue generation

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