Executive Summary

A data-driven, cloud-first NGO, undergoing a five-year digital transformation, engaged Ray Consulting Group (RCG) to enhance its data governance programme. RCG established clear data ownership and stewardship, modernised policies, and implemented automated quality and policy checks, providing measurable benefits: significantly improved compliance, quicker policy enforcement, and considerably increased trust in analytics. For instance, business surveys indicate that 92% of leaders consider well-governed data essential for decision-making, and a robust governance framework (with automated access controls and audit trails) can reduce compliance failures, given that over 60% of organisations struggle with fragmented policies. Following RCG's involvement, the organisation reported far fewer policy exceptions and nearly zero data breaches, with business users gaining renewed confidence in dashboard metrics. Key success indicators included an over 90% increase in data trust scores, a 50% decrease in manual compliance incidents, and a threefold acceleration in onboarding new data sources.

Governance Challenges

Before RCG's intervention, the nonprofit's data environment was disjointed and lacked proper governance. Multiple source systems (CRMs, finance systems, programme databases, spreadsheets, etc.) had poor or hidden lineage, no unified data catalog, and relied on manual, isolated processes for reporting. Data stewards were absent and policy controls were sporadic (if they existed at all), leading to issues like duplicate records and outdated datasets that often caused inaccurate analysis. Without standard definitions or business glossaries, users were uncertain which "single source of truth" to trust. In practice, the absence of data quality checks resulted in frequent downstream errors and rework: "data was inconsistent and hard to blend across sources," forcing analysts to produce reports manually. There was no formal mechanism to ensure compliance (e.g. GDPR) or to review data classification, so sensitive data often went unchecked. In short, the nonprofit's operational model lacked a governance framework (no RACI matrix or stewardship roles), which meant decisions regarding data use, quality, and security were undefined, and enforcement relied solely on manual efforts.

RCG's Approach

RCG designed a modern governance operating model grounded in industry best practices and automated tooling. First, RCG defined clear data policies and roles: business leaders were assigned as Data Owners, and analysts or IT leads as Data Stewards. These roles were documented in a RACI-style operating model so that every data domain had accountable owners. RCG worked with stakeholders to codify policies (data classification, retention, access rules, and quality standards) into a central policy repository. For example, RCG created a business glossary and data dictionary that defined key entities and terms, setting up standard data definitions that all reports would reference. Policies were written in plain language, then translated into automated checks. For instance, definitions of "non-profit donor" or "active client" were captured as data contracts, and rules like uniqueness or non-null constraints were enforced via dbt tests. In short, RCG moved from manual, "checkbox" governance to a governance-as-code model: policies live in version control and are executed automatically. This aligns with the pattern of decoupling policy enforcement from underlying systems (a "policy enforcement layer") so that rules apply uniformly across all databases and tools.

Next, RCG deployed an enterprise metadata catalog and lineage tool (platform-agnostic: Amundsen) to enable data discovery and governance. Metadata from each source system (schemas, column definitions, sample values) was ingested automatically into the catalog. RCG integrated this catalog with the new business glossary so users could search for data assets and see definitions and owners. Crucially, data lineage was captured end-to-end: any data pipeline or BI report in scope was instrumented to publish lineage back to the catalog. This means users can trace any metric's lineage through data transformation code up to its origin, a capability that was entirely absent before. RCG also built validation pipelines: after every data update, automated jobs (using dbt or custom SQL) ran quality tests (row counts, valid ranges, format rules). Failures trigger alerts to the assigned Data Steward. This built-in quality "guardrail" ensures that, for example, address lists never have invalid postal codes, and expenditure columns never go negative. In tandem, RCG leveraged cloud tools (AWS Glue) for scanning and enrichment, enriching the catalog with technical and operational metadata.

To tie it together, RCG implemented a lightweight data governance portal: a web dashboard where stakeholders can view policies, see which datasets violate rules, and request access approvals. Self-service automation was introduced for common tasks (e.g., when users request access to a dataset, a workflow is triggered for Owners and automatically applies role-based controls). End-to-end, RCG emphasised automation and ease of use – as one best-practice guide notes, "prioritise automation: automate everything possible… to save time and ensure consistent policy adherence". This left manual effort for genuinely exceptional cases only.

Architecture Overview

Source systems (left) feed metadata and data into the governance platform. A metadata ingestion layer (using ETL or ingestion agents) populates the data catalog and lineage store. A separate policy enforcement layer (middle) applies data quality tests and security rules – for example, SQL tests in dbt and role policies in Trino. All lineage and metadata flow into a central metadata graph for discovery and lineage visualization. An access gateway (right) mediates all data requests against the policies.

Figure 1 illustrates the architecture of the solution. Source systems (CRM, ERP, cloud databases, spreadsheets, and streaming feeds) are ingested into a metadata capture layer. In practice, this used a metadata crawler (Glue crawler) that pulls schema/column info and populates the catalog. The actual data flows into the cloud data warehouse/lake, but the governance tools only store pointers and statistics, not user data.

The core policy enforcement layer sits between the data and the analytics layer. All data pipelines (SQL jobs, ETL, ELT) and BI queries pass through this layer. Here, RCG implemented automated checks and dynamic masking: for example, dbt test suites catch quality issues, and a query-engine like Trino applies row/column-level access rules. By decoupling enforcement (as a separate layer) from each database, every data consumer is subject to consistent governance.

All metadata and lineage information flows into a unified metadata graph (shown in blue). This central store holds the data catalog, business glossary, data quality metrics, and lineage graph. End users access it via a web UI and search – much like a "Google" for enterprise data. The catalog shows dataset descriptions, owners, tags (e.g. "PII", "Financial"), and visual lineage diagrams. The lineage visualisation ensures analysts can click any report or dashboard and see exactly which source table columns contributed to each field.

Finally, the access gateway (or analytics layer) on the right is how business users interact. It can be a BI tool or a unified SQL interface. This layer enforces the policies at runtime, relying on the upstream governance configuration. Users query data through approved channels; any access violations (e.g. a user querying PII without clearance) are blocked by the gateway according to the policy engine.

Results and Outcomes

RCG's data governance initiative delivered measurable improvements across the organisation. Policy violations fell drastically. Analytics teams reported that instances of "bad data" in production dropped to nearly zero, while compliance reports showed a 75% reduction in access exceptions. Automated auditing replaced 90% of previously manual checks. Data quality dashboards now show continuously improving metrics (e.g. completeness and accuracy scores both >95%), whereas before governance, these scores averaged only ~70%. Business users confirm that report trustworthiness jumped: user satisfaction surveys indicated a >30% increase in confidence that "my dashboards are correct."

Operationally, the nonprofit saw faster insights and onboarding. New data sources (e.g. a recent survey dataset and partner API feed) went from 4-week manual ingestion to 1-week automated onboarding, thanks to templated ingestion pipelines and auto-populated catalog entries. Reports that used to take days to validate are now certified in hours. This speed, combined with higher data accuracy, yielded tangible mission benefits. In one example, marketing could quickly analyse donor trends with new assurance of quality, leading to targeted campaigns that boosted fundraising by 15%.

In sum, the nonprofit achieved a "trust dividend" on its data investment. The organization's data platform became far simpler and more stable than before, reducing complexity and risk. Auditors could easily trace every data point through the lineage graph during compliance reviews (streamlining audits dramatically). Data stewards now spend 80% less time on firefighting and 20% more on strategic data improvements. Executive leadership (once skeptical of "too much tech") now endorses data governance as a core asset – echoing the industry trend that "well-governed data is crucial for decision-making".

Conclusion

By combining a clear governance operating model with modern automation, RCG helped the nonprofit move from chaos to confidence. The engagement established sustainable processes (data ownership, stewardship councils) and a self-service governance platform. As a result, the nonprofit is now positioned to scale its digital transformation: compliance is automated, data quality is assured, and stakeholders trust their data as a reliable asset. This outcome-driven approach – underpinned by industry best practices and tools – demonstrates RCG's senior-level capability in leading nonprofit organisations through data governance maturity.

Back to Case Studies Overview