200
apps integrated into the cloud
25
systems unified into one
14
AI readiness initiatives deployed
4
governance principles for scalable AI
Gray embedded data governance into active ISO and KPI initiatives using Informatica’s Cloud Data Governance and Catalog (CDGC) across the enterprise. By providing clear guardrails, Gray scaled governance, improved data trust, and built a solid foundation for enterprise AI.
Shift from traditional governance to enablement
Founded in 1960, Gray is a fully integrated industrial design-builder offering engineering, architecture, construction, automation, specialty equipment manufacturing, and real estate services. Rooted in its construction heritage, the company has grown into a family of specialized businesses that operate as one team, or as individual experts, to deliver integrated, end-to-end project solutions or tailored services, all backed by lasting partnerships, trusted expertise, and a commitment to speed, safety, precision, and innovation.
As a general contractor, Gray's construction data — spanning contracts, vendors, and financials — is highly complex, compounded by a corporate structure of five subsidiaries that once ran separate, uncoordinated software systems. Traditional governance programs often struggle because they ask business teams to adopt new processes and meetings disconnected from day-to-day work. This creates the perception that governance is an administrative burden rather than a business enabler. When governance operates independently from the projects that create and consume data, it's hard to sustain.
Gray, led by Heather Feist, Senior Business Systems Analyst, Data Governance and Quality, turned governance from a bolt-on initiative into a durable capability that scaled across the complex subsidiaries. "Rather than forcing rigid governance policies on teams, we realized we needed to embed governance into existing projects to make it frictionless and driven by actual business needs," says Feist.
Building an embedded enablement model
Gray's operating model relies on trusted business and technology partners who already have established relationships with stakeholders. Rather than requiring a centralized governance team to lead every conversation, the Data Governance Lead establishes strategy, standards, templates, and guardrails, while Business Systems Analysts (BSAs), Data Analysts, and Data Engineers serve as governance ambassadors within active projects.
Business stakeholders remain subject matter experts who define business meaning, ownership, and quality expectations. This strategy took shape with the launch of a new KPI dashboard project, which Feist describes as a "trojan horse" for embedding governance into daily practices.
Establishing a "receipt of trust" with validation packets
To ensure governance is never overwhelming, Gray avoids pursuing perfection from the start, opting instead for a Minimum Viable Governance (MVG) model focused on immediate business value. A major hurdle in the past was a reliance on tribal knowledge and "rogue" spreadsheets, which led to conflicting numbers in meetings and eroded trust in corporate dashboards. For example, different business units held varying definitions for terms as fundamental as "fiscal year net revenue" or "construction start date."
To resolve these discrepancies, Gray established a governed layer of data within its Enterprise Data Warehouse (EDW) on Microsoft Azure rather than pulling raw data directly from source systems. Every metric on the new dashboard, visualized in Power BI, must pass through a strict "KPI validation packet" managed in SharePoint. This validation packet serves as a formalized "receipt of trust," documenting data sources, transformations, DAX calculations, and ownership. "When someone comes with a spreadsheet or an older dashboard and says our numbers don't match, we don't automatically lose trust," notes Feist. "We have a signed-off validation packet, clear gateways, and an escalation path to resolve discrepancies. It establishes a repeatable standard for what ‘done' means."
Leveraging Informatica’s Cloud Data Catalog
Enterprise metadata and governance activities at Gray are documented within Informatica’s Cloud Data Governance and Catalog (CDGC) as part of the Intelligent Data Management Cloud (IDMC), which serves as the system of record for governed information. The platform automatically scans Gray's Azure databases and Power BI reports to capture business definitions, KPI subdomains, ownership, and data quality expectations.
This central repository transforms governance from a conceptual exercise into a measurable capability, tracking metrics such as the number of governed KPIs, assigned data owners, and documented lineage relationships. The ease of use of the cloud-native tool has also accelerated internal enablement and training.
"Going from an on-premise to a cloud version has made our metadata discoverable, allowing us to manage governance domains like project, workforce, and financial data in a unified way," Feist says.
Preparing for AI, integration, and measurable success
By embedding data governance into daily operations and cataloging enterprise metadata, Gray is establishing the foundation required for responsible citizen development, ISO 27001 certification, and future AI adoption. To measure the success of the strategy, Gray has established 13 quantitative and qualitative metrics for the upcoming fiscal year. Quantitative targets include completed AI exposure audits and the percentage of critical data elements classified, while qualitative targets evaluate confidence in AI enablement and the ability of business stewards to explain data classifications, such as PII and sensitivity ratings.
The business impact of this governed, integrated approach is already visible in Gray's payroll systems. When integrating employee data between UKG and the CMIC financial ERP, Gray addressed a complex work location tax discrepancy for mobile employees moving between counties and states. Resolving this data flow prevents W2 correction fees, tax penalties, and administrative delays. Moving forward, Gray is planning a phased rollout through January, launching schedule and team metrics first, followed by financial and customer data.
"Data governance, especially with AI right now, is putting a lot of people who never wanted to do data governance into needing to do it," Feist concludes. "But it is so much about the practice and the behavior rather than just the technology. When they understand the benefit, governance simply becomes the way we do business."
