Use Case — Financial Services
Enterprise Data Strategy
Build the unified data foundation your financial institution needs for confident, compliant, real‑time decision‑making.
We help financial services organizations eliminate conflicting metrics, modernize reporting, and establish governed, AI‑ready data foundations that accelerate insight generation and strengthen executive decision‑making.
The Challenge
Financial institutions operate in a high‑stakes environment where reporting accuracy, regulatory compliance, and executive visibility are non‑negotiable. Yet this organization struggled with fragmented data, inconsistent KPI definitions, and slow reporting cycles that left leadership without a reliable view of performance.
These issues created:
- Conflicting financial and operational metrics across business units
- Manual reporting processes that delayed decision‑making
- Limited trust in dashboards and analytics outputs
- No unified governance model to ensure data quality and accountability
The result was a leadership team forced to make strategic decisions without a single, trusted source of truth.
Industry Pressure:
Financial services organizations face increasing regulatory scrutiny and rising expectations for real‑time insight. Fragmented data makes both nearly impossible.
Financial services organizations face increasing regulatory scrutiny and rising expectations for real‑time insight. Fragmented data makes both nearly impossible.
What We Found
Through stakeholder interviews, data lineage reviews, and analysis of existing reporting workflows, several systemic issues emerged:
- No enterprise KPI catalog or standardized business rules
- Multiple reporting teams producing conflicting versions of financial metrics
- Data quality issues caused by unclear ownership and inconsistent definitions
- Legacy data models that could not support automation or AI initiatives
These gaps prevented the institution from scaling analytics, improving reporting speed, or meeting executive expectations for accuracy and consistency.
Our Approach
We delivered a comprehensive enterprise data strategy designed specifically for the regulatory, operational, and analytical demands of financial services organizations.
KPI Framework
We unified financial and operational KPIs across the institution, ensuring every team measured performance using the same definitions, rules, and calculations.
This included:
- KPI rationalization workshops with finance, risk, and operations
- Standardized metric definitions aligned to regulatory expectations
- Documentation of business rules, ownership, and calculation logic
This alignment eliminated metric disputes and created a shared language for performance.
Why It Matters:
In financial services, inconsistent KPIs lead to regulatory risk, reporting delays, and misaligned executive decisions. Standardization restores trust.
In financial services, inconsistent KPIs lead to regulatory risk, reporting delays, and misaligned executive decisions. Standardization restores trust.
Governance Framework
We established a governance operating model that ensured data quality, ownership, and accountability across all financial domains.
Key components included:
- Data ownership and stewardship roles across finance, risk, and operations
- Data quality standards and escalation paths
- Governance workflows embedded into reporting and analytics processes
This framework created a sustainable structure for maintaining consistency and trust.
Unified Data Models
We designed enterprise‑wide data models that reconciled financial, operational, and customer metrics into a single, governed foundation.
This included:
- Standardized dimensions and facts across financial domains
- Reconciled business rules for consistent reporting
- A scalable architecture ready for automation and AI initiatives
These models accelerated reporting and improved accuracy across the institution.
The Outcomes
With a unified data strategy in place, the institution transformed how it measured performance, managed risk, and supported executive decision‑making.
- Reporting cycles accelerated across finance and operations
- Standardized KPIs adopted enterprise‑wide
- Governance framework implemented and maintained by data stewards
- Executives gained a single source of truth for strategic planning
The organization moved from reactive reporting to proactive, data‑driven leadership.
Ready to Build a Unified Data Strategy?
If your financial institution is struggling with conflicting metrics, slow reporting, or unclear data ownership, we can help you build a governed, scalable, AI‑ready data foundation.