Case Study

Data Strategy identifies up to $30M in revenue uplift and over $250k in annual OpEx savings for a Financial Services Leader

Snowflake

Customer's Challenge

A leading financial services company, known for helping small businesses access the funding they need, was preparing for its next stage of growth. To support big goals like an IPO/Acquisition and new partnerships, leadership recognized the need to strengthen its data foundation.

While the company had built tremendous success, it faced challenges common to fast-growing organizations: inconsistent data quality, little trust in data, unclear ownership, and a lack of governance processes. Heavy (ungoverned) reliance on Alteryx workflows and an aging legacy platform also limited auditability and risk modeling. Left unaddressed, these challenges would jeopardize not only audit-readiness but also the company’s ability to scale operations, attract capital, and comply with emerging regulatory mandates.

In an effort to restore trust and build a roadmap for transformational success, the client was looking for a partner to help take them through the next phase of growth.

phData's Solution

phData provided a comprehensive 12-week Data Strategy assessment that delivered actionable recommendations across organizational structure, technology architecture, AI/ML capabilities, and data governance implementation. The engagement produced a detailed 2-year roadmap for transformation while identifying immediate quick wins to build momentum and demonstrate value.

The Full Story

A rapidly growing financial services company had built a successful business providing critical financing to small businesses nationwide, but its data infrastructure had not evolved to match its ambitions.

The organization faced a perfect storm of data challenges that threatened its strategic objectives. Leadership had lost confidence in the data due to inconsistent definitions, manual processes, and ungoverned workflows. The Legal and Compliance teams struggled with inadequate data support, while Risk teams relied on manual Excel processes that couldn’t scale. 

Most critically, their heavy reliance on Alteryx without proper governance created audit and compliance risks that could derail IPO plans or partnership opportunities.

The company’s core legacy platform limited its ability to implement sophisticated risk modeling, while inconsistent use of critical fields like “product type” and “blacklist” led to inaccurate regulatory reporting. Without proper data retention policies and with sensitive documents overexposed through SharePoint, the organization faced significant legal and compliance exposure.

phData stepped in as a strategic partner, bringing deep expertise in financial services data transformation and regulatory compliance. The engagement followed a structured approach across three phases.

Phase 1 

Assessment

During the Assessment phase, phData conducted comprehensive stakeholder interviews across Finance, Risk, Legal, Technology, and Operations teams. The team performed maturity assessments, capability gap analyses, and architectural deep dives to understand root causes rather than just symptoms.

Phase 2

Design

phData did an analysis of reporting speed times correlated to warehouse size. The customer was presently utilizing a 2XL warehouse. Because there were no cases of excessive spillage to a remote disk, and processing speeds were nearly the same, phData recommended that the customer decrease their warehouse size and save the difference.

Phase 3

Mobilization

In the Mobilization phase, phData created detailed implementation roadmaps for specific initiatives, dependencies, and sequencing. The team developed comprehensive tearsheets for the first six months of execution, complete with resource requirements, costs, and timelines.

phData Blue Shield
phData Blue Shield

Why phData?

The client selected phData for its deep expertise in data tools, proven success with Fortune 500 companies, and strong track record in financial services. They valued phData’s ability to deliver tailored solutions, meet strict regulatory requirements, and translate complex technical concepts into clear, actionable business strategies.

Results

phData’s comprehensive strategy delivered four key pillars of transformation that directly addressed the client’s strategic objectives:

Transaction and Audit Readiness

phData recommended migrating critical financial data to governed, version-controlled pipelines using dbt and Snowflake, enforcing historical data preservation and traceability. This approach would provide the audit-ready financial systems essential for IPO preparation or acquisition scenarios.

Legal and Compliance Risk Mitigation

The strategy included implementing robust access controls, data classification frameworks, retention policies, and a centralized metadata catalog and governance platform using tools like Atlan. These measures would significantly reduce regulatory exposure and ensure compliance with requirements like Dodd-Frank 1071 and securitization standards.

Trusted Performance Metrics for Growth

phData recommended standardizing key business metrics, including conversion rates, customer acquisition costs (CAC), and renewal rates within Snowflake, then exposing them through governed Tableau dashboards for executive visibility. This would enable data-driven decision-making across all departments.

AI Value Acceleration

The strategy established a roadmap for implementing a feature platform, starting with Snowflake Feature Store and broader MLOps practices, to reduce time-to-model development and improve consistency across data science initiatives.

Business Value

The business case demonstrated significant financial impact, with phData calculating $1.2 million in annual savings from migrating away from ungoverned Alteryx workflows to a modern dbt-based architecture, which costs only $60-70,000 annually. 

Additional value included $30 million in potential revenue uplift from asset-backed securitization improvements and a projected $250k+ in operational efficiency savings.

Reference Diagram

Future state architecture diagram showing the transformation from fragmented legacy systems to a unified, governed data platform built on Snowflake and dbt, with clear data lineage and governance controls.

Meet the Team

Linda Lokkesmoe

Principal Program Manager

10+ Years of Experience

Key Skills

Data Strategy Development, Financial Services Expertise, Executive Stakeholder Management

Responsibilities

Led the overall strategy engagement, coordinated cross-functional teams, and delivered executive presentations to drive alignment on the transformation roadmap.

Ben Limegrover

Ben Limegrover

Principal Business Architect

8+ Years of Experience

Key Skills

Business Process Analysis, Use Case Development, Governance Framework Design

Responsibilities

Conducted stakeholder discovery sessions, developed prioritized use cases, and designed future-state governance programs aligned with business objectives.

William Sell

William Sell

Senior Solutions Architect

12+ Years of Experience

Key Skills

Data Architecture Design, Technology Assessment, Integration Planning

Responsibilities

Assessed current state technology landscape, designed future-state data and integration architecture, and identified technical capabilities required for transformation.

April Fleming

April Fleming

Principal Business Architect

10+ Years of Experience

Key Skills

Organizational Design, Change Management, Process Optimization

Responsibilities

Designed future-state operating models and organizational structures, developed governance rollout plans, and created detailed implementation tear-sheets.

Abraham Reddy

Abraham Reddy

Machine Learning Solutions Architect

8+ Years of Experience

Key Skills

AI/ML Strategy, Feature Engineering, MLOps Implementation

Responsibilities

Assessed current ML capabilities, designed feature store architecture, and developed recommendations for AI value realization and MLOps practices.

phData is proud to fuel the client's next stage of growth—turning trusted data into faster audits, smarter decisions, and real AI value.

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