Embedding Decision-Ready Intelligence into Revenue Operations for a Global Identity Leader

The Challenge

A global identity and access management leader had invested heavily in building a mature, well-governed data organization centered on Snowflake. Hundreds of trusted reports, dashboards, and models were available across the business, supported by strong data governance and broad internal access.

As the organization scaled, the growing volume of available insight increased the effort required to plan, prioritize, and act. Sales and customer success teams spent more time navigating reports and assembling signals across tools, slowing day-to-day execution.

Key challenges included:

Insight Discovery Required Tool Fluency

The data organization produced a large and growing body of high-quality, role-secured reports. Analysts and data teams navigated this environment with ease, while sales and customer success leaders relied on familiarity with specific tools, report structures, and navigation paths to access insight. As volume increased, locating the right information quickly enough to support action required operational fluency that many teams did not have.

Decisions Assembly Lived with the User

Critical signals like churn risk existed across reports and machine learning models, but users were responsible for pulling information together and interpreting it themselves. Decisions depended on individual synthesis rather than a system that delivered a unified, decision-ready view.

Insight Delivery Was Entirely Pull-Based

Insights reached teams only when users actively searched for them. The system delivered information when requested, rather than distributing insight automatically as conditions changed. Consistency varied based on who searched, when they searched, and how familiar they were with the data environment.

Revenue-Critical Signals Arrived Late

Sales and customer success teams experienced the downstream impact most directly. Signals tied to account health, adoption, and renewal risk existed within the data environment, yet arrived unevenly across workflows. As a result, daily planning and outreach operated without consistent, timely intelligence at scale.

The Solution

Sparq was brought in to re-engineer how insight moved from Snowflake into daily execution, without rebuilding the data platform or replacing existing investments. The focus was to establish a decision-ready intelligence foundation that could scale with volume, complexity, and change.

Key elements of the solution included:

Natural-Language Access to Enterprise Data

Sparq implemented an AI-enabled interface that allowed users to ask questions in plain language and receive direct, contextual answers, eliminating the need to hunt through multiple reports or tools. Under the hood, the system translated questions into executable queries against Snowflake and returned both the results and a clear explanation of how the answer was derived.

Semantic Modeling Aligned to How the Business Thinks and Talks

Sparq worked with business stakeholders to define a semantic layer that mapped internal terminology, metrics, and relationships to underlying data structures. This ensured the system interpreted questions using the same language and meaning employees used in their daily work.

Transparent, Governed Insight Generation

The system exposed how answers were generated, showing the query logic and underlying data, so users could validate results rather than treating the output as a black box. This transparency was critical in a highly secure, tightly governed environment.

Intelligence Designed to Evolve

The system captured every question asked and every interaction with the interface. This created a feedback loop that revealed which insights users sought most frequently and which questions went unanswered, enabling the organization to continuously refine data models and prioritize enrichment.

Rapid Foundation Build with Embedded Knowledge Transfer

Sparq delivered a hardened prototype and foundational architecture within weeks, absorbing early technical and security complexity. Client team members were embedded throughout the process, allowing them to continue development without disruption once Sparq rolled off.

The Results

The engagement centered on establishing a durable intelligence foundation. Confirmed outcomes included:

Faster Access to Actionable Insight

Teams could retrieve answers directly using natural language, reducing the time and effort required to move from question to action in daily workflows.

Evidence-Driven Data Roadmapping

The system captured 100% of user questions and interactions, creating a clear, data-backed signal of what information teams needed most. This enabled the data organization to prioritize enrichment and development based on real demand.

Broad Applicability Across the Business

More than 40 distinct use cases were identified during early exploration and usage, demonstrating the reach of decision-ready intelligence once insight delivery aligned with how work was performed.

A Scalable Path Toward Proactive Revenue Intelligence

The system established the architectural and semantic groundwork required to support proactive delivery of churn risk indicators, adoption signals, and next-best-actions for sales and customer success teams.