Workday Data Cloud: A Strategy for Agentic AI That Works

Authors:

Rakesh Duggal

Andrew Dinin

Narendra Pudipedi

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Deploying Workday Data Cloud is a strategic decision about your analytics operating model—not a feature add-on. Here’s how to make a more-informed choice.

More than a new data layer, Workday Data Cloud (WDC) represents a strategic evolution of the Workday platform. The core concept behind WDC is to make an organization’s trusted Workday data easier to consume for analytics and AI by connecting it to their existing analytics platforms and operational systems with bi-directional access, without the need for complex extraction, transfer, and loading (ETL) processes. The ability to replace complex ETL and integrations with a semantic layer for agents, along with live access to data via Apache Iceberg for enabling big-data analytics and AI, changes the way data is currently procured and governed.

Workday has made this seamless interconnectivity possible by designing an open, fully integrated ecosystem, supported by partnerships with key players in analytics and cloud data: AWS, Databricks, GCP, Salesforce and Snowflake. By simplifying secure and virtual access and enabling bi-directional connectivity with zero-copy capability across this ecosystem, WDC gives organizations the ability to combine their people and money data with external customer, market, and operational data—unlocking valuable insights that may not have been previously accessible in a practical, cost-effective way.

Signs Of A Strong Fit

Despite its capacity to unleash the art of the possible, WDC is not one-size-fits-all. It is better suited for some organizations than others depending on their analytics maturity and AI objectives. Deloitte has identified five characteristics where WDC may be a strong fit. The following characteristics are not mutually exclusive, and they could fit multiple customer scenarios:

Newer Workday customers simplifying data

This includes organizations implementing Workday Human Capital Management (HCM) or Workday Financial Management that lack a mature enterprise data platform and need faster time-to-value for standardized insights.

Limited Workday data engineering capacity

WDC can help organizations facing constraints in building and maintaining pipelines by reducing custom work and ongoing support burdens through lower-lift ingestion and managed data access.

Need governed, reusable Workday datasets

This includes customers that require executive dashboards, domain KPIs, planning/forecasting inputs, operational analytics, and starter AI/ML where consistent definitions and lineage matter most.

Mature data and AI; extraction gaps remain

An organization may already have AWS, Databricks, GCP, Salesforce or Snowflake, or another platform, but seeks governed Workday access and contextualized data to transform what agents can do without duplicative extraction (zero-copy) or heavy custom integration.

Highly custom Workday analytics and AI needs

This includes advanced analytics programs requiring complex multi-source modeling at scale, extensive custom feature engineering, or strict enterprise tooling standards. In this scenario, the enterprise platform would remain the primary analytics and AI layer, with Workday serving as a critical, governed data source. WDC would provide periodic updates of tables, live data access to read the data where it sits, and authorization in zero-copy data access to further extend the model.

Extend Workday reports, dashboards and Prism

WDC provides enhanced data connectivity, transformation and mapping that can amplify existing Workday analytics capabilities, surfacing more insights and providing more reporting options. If the organization is using other business intelligence tools such as Tableau, the reporting strategy can be refined to place all dashboards in one central location.

A Model Decision

The appropriateness of WDC for an organization is fundamentally a decision about the desired data-ecosystem architecture and operating model, not a feature add-on. Organizations will need to determine if WDC can and should be used to:

Create a standalone analytics, data, and integration foundation, where Workday serves as both the system of record and analytics backbone (i.e., a WDC-led model)
Accelerate Workday-focused, HR- or Finance-based, agentic AI use cases that require cross-domain data, while the enterprise platform remains the primary system of insight (i.e., a hybrid model)
Enable Workday to serve as a critical data source for an enterprise data platform, like Snowflake or Databricks, with minimal engineering effort (i.e., an enterprise-led model)
Facilitate data-out and data-in where Workday serves as both a producer and consumer of enriched enterprise data (i.e., a full WDC-led model)

The right answer often depends on speed-to-value, data maturity, and long-term extensibility.

If your goal is to stand up a credible analytics foundation quickly, Workday Data Cloud can be an accelerator. If your enterprise platform is already your analytics backbone, Workday Data Cloud can extend it through governed Workday data access, reduced data duplication, and new cross-system analytics and AI use cases.

- Rakesh Duggal, Workday Alliance Chief Technology Officer

Example High-Value Use Cases

Workforce Performance & Revenue Drivers:
Leaders often lack visibility into how workforce factors like engagement, tenure, and staffing affect performance. Combine Workday workforce data with business metrics to identify which teams and workforce attributes drive the strongest outcomes.

Workforce Plan vs. Business Outcomes:
Finance and workforce plans are often disconnected from performance, limiting the ability to identify variances and adjust quickly. Unify financial and workforce data with planning, sales, or market data to improve forecasting, scenario planning, and decision-making.

Predictive Talent & Business Insights:
HR and business leaders often do not have the ability to anticipate attrition, performance, and business impact across functions. Enrich Workday talent data with operational and business KPIs to power predictive models, forecast outcomes, and proactively drive decisions.

Four Pillars Of Assessment

To assist Workday customers in determining the most beneficial role for WDC within their organizations, Deloitte has identified four pillars of assessment—a series of activities for developing a strategy and laying the foundation for using WDC to support analytics, agentic AI, and integration use cases:

1. Confirm the decision drivers.

Identify and prioritize target analytics use cases and agentic workflows; assess current analytics maturity and skill capacity; and evaluate the data governance model, including definitions, stewardship, and access controls. Establish non-functional requirements, including latency, volume, security, and auditability; examine the existing Snowflake and Databricks footprint, including procurement constraints; and assess the multi-ERP and broader enterprise system landscape, including cross-platform workflow requirements.

2. Assess options.

Explore the “Art of the Possible” and define the WDC strategy including outcomes of data and architectural assessments. Compare WDC-led vs. hybrid vs. enterprise-led models across multiple dimensions such as time-to-value, total cost of ownership, extensibility, data quality and semantic consistency, security and compliance, and operations and ownership.

3. Prove value.

Select the best-fit WDC model and choose 1–2 high-value use cases for assessing and validating the data acquisition effort, modeling and semantic alignment, business intelligence consumption patterns, performance and governance, support team hand-offs, architecture and data readiness, and expected financial impact.

4. Decide and mobilize.

Develop the WDC value case and roadmap. Key outputs typically include a recommended architecture, a suggested governance and operating model, a phased roadmap from quick wins to scale, and a delivery plan with KPIs to measure business benefits and ROI.

Organizations can undertake these activities on their own or through structured workshops facilitated by Deloitte. The latter provides access to Deloitte’s broad range of data and analytics services, cross-functional expertise, and industry leading practices, to accelerate value realization.

WDC is designed to connect Workday and enterprise data, protect sensitive information, and power cross-domain insights with greater consistency than ad hoc extracts or queries. With it, your organization can obtain rich, secure, real-time access to insights—turning workforce and financial information into action faster than previously thought possible. To learn more about how you can build a WDC strategy for agentic AI that works, contact us today.

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