Limit Management Visibility: From Hidden Data to a Unified View

The Data Was There. Nobody Could See It.

The dashboards didn’t start as dashboards. They started with confusion.

Scattered across the bank were limits — risk limits, credit limits, liquidity ceilings, Volcker exposure constraints — buried across disparate systems. Some lived in GRC system Archer. Others sat in SharePoint folders with outdated naming conventions. A few were still managed manually in Excel by individuals who had since rotated out of their roles.

But they all had one thing in common: if you wanted to know which limit was being breached — or even what the limit was — you needed to ask around. Responses could take days.

The data warehouse was already in place: Snowflake, Databricks — solid infrastructure. It wasn’t a technology gap. It was a visibility gap.

Then someone in management asked a pivotal question:

“What if we could bring all the limits — across all risk areas — into one consistent, auditable view?”

Chapter One: Fragmentation and Effort Everywhere

Each team had its own version of reality:

• Risk teams maintained multiple trackers with inconsistent definitions.
• Compliance updated decks manually for attestations.
• Liquidity and operational reporting depended on SharePoint updates and legacy views.

There was no central lens. Analysts worked late reconciling numbers. Business managers relied on emailed reports. Audit requests turned into multi-week internal chases.

The infrastructure existed — but it hadn’t been activated at the reporting layer.

That’s when the collaboration started.

Chapter Two: Cross-Functional Execution — The Real Enabler

What made this project move wasn’t just technology — it was early alignment between three key functions:

Infrastructure Lead (IT):

Owned systems, access provisioning, and backend architecture. Ensured secure and consistent access to data from Archer, SharePoint, Snowflake, and legacy sources.

Business Risk & Compliance Manager:

A stakeholder deeply familiar with day-to-day reporting pressures and the voice of end users. Defined the key questions: Which limits matter most? What do executives care about? Where do audits get stuck?

Reporting & Data Modeling Lead (Arrayo):

Bridged technical and business domains to model, design, and build a reporting suite that turned underlying data into actionable insight — balancing auditability with ease of use.

Through workshops and validation sessions, terminology was aligned, data structures cleaned up, and prototypes built. The first phase wasn’t everything, but it showed the way. It created a shared language and a working foundation — one now growing into subsequent iterations.

Some departments had already begun isolated reporting efforts, but without a common structure, little could scale. This project created the connective tissue — pulling pockets of reporting into a single, governed narrative. It wasn’t built in a day. But it’s becoming something every bank would want to have.

Chapter Three: From Blind Spots to Boardrooms

Phase I delivered a multi-tiered reporting suite now gaining adoption across departments:

• Risk management teams can monitor usage against limits more consistently.
• Compliance is beginning to align Volcker reporting with greater clarity.
• Audit has a clearer starting point with lineage and timestamps.
• Executives are gradually accessing up-to-date limit visualizations.

The dashboards are structured into layers:

• Global overview of limit usage, breaches, and trends
• Drill-downs by limit type (Credit, Liquidity, Volcker, Operational)
• Definitions mapped directly to data points for transparency and audit review

The core infrastructure — Snowflake, Databricks — remains intact, and this project continues to build on that investment, bringing it closer to day-to-day decision-making.

What Made It Work: Beyond Technology

This initiative’s early success rested on a few key principles:

• Business-led requirements: Reports were shaped by real use cases, not abstract templates.
• Clear ownership & collaboration: IT, business, and analytics worked in coordination.
• Progressive model design: Modular logic, scalable dataflows, and flexible RLS supported reuse and adaptation.
• Embedded audit trails: Data definitions, update timestamps, and limit owners are visible to the end user.

It’s not finished — but it’s fully functional. And, most importantly, it has traction.

The Technology Stack That Powered It

• Data Warehouse: Snowflake
• ETL & Modeling: Databricks, Python, Power Query
• Dashboards: Power BI (RLS, DAX, bookmarks, drill-throughs)
• Integrated Sources: Archer, SharePoint, internal APIs

Dashboards refresh daily. User access is governed. The model is modular and evolving.

Final Reflection: From Static Infrastructure to Strategic Visibility

This wasn’t just about a dashboard. It was about turning dormant data into usable intelligence — one phase at a time.

The first phase laid the groundwork. It created momentum and delivered value early. Now, the team is moving into deeper iterations — expanding coverage, improving definitions, and strengthening adoption.

What was once fragmented is becoming centralized. What was once opaque is becoming visible. What was once delayed is now available on demand.

It’s not the end of the journey, but it’s the moment when the investment begins to pay off.

Built in partnership. Delivered with clarity.