How Johns Hopkins University Achieved 24× Faster Fundraising Analytics with dbt

Key Metrics

Industry	Higher Education — Advancement & Fundraising Analytics
Challenge	Inconsistent fundraising metrics, double-counting risk, and 24+ hour report delivery from ungoverned Salesforce analytics
Solution	dbt-driven analytics engineering with dimensional modeling on Azure Synapse Analytics
Tech Stack	dbt Core / dbt Cloud · Azure Synapse Analytics (Dedicated SQL Pool) · Salesforce · Power BI · Tableau
Results	Consistent metrics across all dashboards · Eliminated reconciliation errors · Trusted, governed analytics layer · Reusable dimensional model for self-service BI

Johns Hopkins University’s Development and Alumni Relations (DAR) team supports fundraising, campaign management, and constituent engagement across a highly complex institutional environment. As reporting needs grew, DAR required a modern analytics foundation built on dbt data modeling that could deliver trusted metrics, support future self-service BI, and scale with evolving data demands.

The Fundraising Analytics Problem: Why Salesforce Alone Wasn’t Enough

DAR’s primary data source was Salesforce, configured for operational workflows rather than analytics. While effective for gift entry and donor management, the source data posed major challenges for reporting and decision-making:

Complex, denormalized data structures with overlapping concepts
Ambiguous grains across payments, gifts, credits, and campaigns
Unstable natural keys and heavy reliance on Salesforce-generated IDs
Inconsistent metric definitions across teams and dashboards
Limited trust in analytics outputs due to reconciliation issues

Without a governed analytics layer, reports risked double-counting gifts, misattributing credit, and producing conflicting totals, an especially serious issue in fundraising and donor reporting. At an institutional level, these inconsistencies undermined leadership confidence in reported outcomes and introduced unnecessary risk into campaign performance tracking and donor accountability.

How Data-Sleek Built a dbt Analytics Foundation on Azure Synapse

The objective was to establish a trusted, scalable analytics foundation that delivered consistent metrics and supported future self-service BI through governed transformations and clear data grains.

Data-Sleek partnered with Johns Hopkins DAR to design and implement a dbt-driven analytics engineering and dimensional modeling framework on Azure Synapse Analytics.

1. Layered dbt Data Modeling: Staging, Dimensions, and Facts for Clean Analytics

We enforced a clear, scalable modeling structure using dbt:

Staging models to standardize raw Salesforce data
Dimension tables representing stable business entities (constituents, campaigns, designations, dates)
Fact tables capturing transactional events at explicitly documented grains

All business logic was centralized in SQL transformations and never embedded in BI tools.

2. Resolving Grain Ambiguity and Surrogate Key Strategy in Fundraising Data

To resolve grain ambiguity and unstable identifiers:

Deterministic surrogate keys were generated for all dimensions
Natural keys were retained for traceability but excluded from joins
Every fact table clearly documented its unit of analysis
Relationships between facts and dimensions were enforced with automated tests

This approach supported multiple analytical perspectives, payment-level, credit-level, and campaign-level, without conflating them.

3. Embedding Data Quality Tests Directly in the dbt Pipeline

Data quality was embedded directly into the pipeline:

not_null and unique tests on primary keys
Relationship tests between facts and dimensions
Layer-specific YAML files for maintainable testing at scale

As Salesforce schemas changed, dbt tests surfaced issues early, preventing them from reaching dashboards.

4. From Code to Shared Truth: dbt Documentation for Non-Technical Stakeholders

To bridge the gap between technical and non-technical stakeholders:

dbt documentation was written in plain business language
Column descriptions explained the meaning, usage, and limitations
Dimensional ERDs were generated from the analytics model itself

Documentation became a shared source of truth across teams.

Results: 24× Faster Reporting and Consistent Fundraising Metrics Across Johns Hopkins

1. Technical Outcomes

A modular, maintainable dbt project aligned with industry best practices
A production-ready cloud data warehouse implementation on Azure Synapse Analytics as the governed analytics serving layer
Simplified SQL and reduced logic duplication in downstream reporting
Faster development through reusable, conforming dimensions
Improved change management via version control and automated testing

2. Business Outcomes

Consistent fundraising metrics across dashboards and reports
Clear, shared definitions for gifts, credits, campaigns, and constituents
Reduced reconciliation effort and fewer ad-hoc clarification requests
Greater confidence and trust in analytics outputs
Reduced report delivery time from 24+ hours to under 1 hour, a 24× improvement in reporting speed

3. Organizational Impact

Analytics assets treated as products, not one-off reports
Governance embedded directly in the data pipeline
Analytics teams shifted from report builders to data stewards

This foundation positioned DAR for sustainable growth in analytics and future self-service BI.

Technology Stack

Analytics Engineering: dbt Core / dbt Cloud
Data Warehouse: Azure Synapse Analytics (Dedicated SQL Pool)
Source Systems: Salesforce (Development and Alumni Relations)
BI and Reporting: Power BI, Tableau
Governance Artifacts: Dimensional models, ERDs, data dictionaries, automated tests

dbt Consulting for Higher Education: Lessons from Johns Hopkins DAR

Higher education institutions face unique analytics challenges: legacy CRMs, complex stakeholder needs, and high expectations for accuracy and auditability. This engagement demonstrates how dbt and dimensional modeling can:

Translate operational systems into analytics-ready data products
Support advancement, alumni engagement, and institutional reporting
Scale governance without slowing innovation

Download this case study as a PDF

Rather than requiring a full system replacement, this approach modernizes analytics incrementally and pragmatically, a critical advantage in higher-education environments.

Modernize Advancement Analytics with Confidence

Data-Sleek helps higher education advancement teams build trusted, scalable analytics foundations.

Book a free consultation with Data-Sleek to discuss your advancement analytics strategy.

Data-Sleek® helps mid-market companies build the data foundation that makes AI projects succeed. In 8 of 10 engagements, we’ve seen AI initiatives stall not because the AI was wrong, but because the data underneath was never built for it. We fix that — owning the full path from data strategy and warehousing through AI implementation, so you don’t need an internal data team to get there.

With a distributed U.S. team and clients across healthcare, finance, insurance, and logistics, Data‑Sleek turns complex data into measurable business results.

Is your data AI-ready? Book a free consultation →

“The ability to scale a team of data engineers and data scientists in a small amount of time was impressive. Data-Sleek did a great job of onboarding each new candidate.”

Riti Chrea

CEO of Zappo Track