Creating a Single Source of Truth Across Six Systems with Continuous Data Movement

Industry Context: Growth Exposes Data Fractures‍

Large professional services firms—particularly in tax, audit and advisory—operate across sprawling technology ecosystems. Client and entity data is created and modified in dozens of systems: risk management platforms, finance and billing tools, tax engines, document management systems, CRMs, and increasingly, internally built analytics and AI platforms.

As firms grow through acquisition, global expansion or new service lines, these systems evolve independently. Each introduces its own identifiers, constraints and data models. Over time, what began as manageable complexity turns into structural friction.

When “Client” Means Something Different Everywhere

A global professional services firm is grappling with a problem that threatens its ability to scale: no single source of truth for client and entity identifiers across its technology ecosystem. As the firm expands through acquisitions and builds out its tax technology platform, identity fragmentation becomes embedded into day‑to‑day operations.

The firm's technology stack spans risk management systems, finance software, CRM platforms, tax technology solutions and document management systems. Each system generates its own unique identifiers with different formatting rules—one uses 12-character alphanumeric codes, another requires 21 characters, billing codes max out at 10 characters. None of the systems can talk to each other programmatically.

The strain becomes unavoidable once the data starts moving:

• New entities are added in tax systems during the busy season but never created elsewhere
• Sales teams create duplicate records in CRM to move deals forward, triggering downstream revenue recognition issues
• Finance and risk systems contain only partial representations of the client universe
• Data teams spend significant time writing and maintaining reconciliation queries just to assemble a usable view

As complexity increases, the firm is forced into a reactive posture, manually reconciling identity issues after they surface instead of preventing them upstream. What should be foundational infrastructure becomes a constant source of drag on growth, reporting accuracy and modernization efforts.

Automating Reconciliation with a DataDevOps Platform

EASL addresses a problem traditional ETL tools couldn't solve for this professional services firm: continuous reconciliation of client and entity identity across multiple systems, with the flexibility to adapt as tax technology environments evolve. The EASL platform is built for the realities of seasonal spikes, the revenue recognition risks caused by ID sprawl and the audit and compliance demands of tax-focused organizations.

Keeping identifiers in sync over time‍

EASL’s on-cluster storage maintains a persistent mapping of client identifiers across systems. As data flows through a pipeline, it’s written to disk and becomes available to subsequent workflows, which reconcile new records against existing ones. This creates a shared reference point that conventional ETL pipelines can’t provide.

Client data is extracted from risk management, finance, CRM, ERP, tax technology and document management systems. Each extract is stored, then used to build and maintain a unified client and entity ID. The finance system ID can be established as the primary identifier firmwide without rewriting hundreds of custom queries.

Catching issues before they cascade‍

Rules-based validation flags missing entities and mismatched identifiers as data moves. When a new entity appears in a tax system but doesn’t exist in finance or risk platforms, EASL surfaces the discrepancy immediately, before it creates downstream reporting or revenue recognition issues. Every stage of every workflow is logged, providing the audit trail required for tax compliance and regulatory review.

How EASL Delivers Enterprise-Wide Data Integrity

With EASL, the firm is moving from a chaotic landscape of manual queries and fragmented data to an automated reconciliation system that runs on a predictable schedule.

The deployment enables:

• Data engineering teams to stop spending busy seasons writing dozens of queries to stitch together client views
• Proactive detection of revenue recognition issues caused by mismatched IDs
• Predictable budgeting through fixed-cost pricing even as data volumes grow
• Global scaling that incorporates new member firms without rebuilding reconciliation infrastructure
• Technical teams that can focus on building the future-state tax technology platform instead of maintaining unstable reconciliation scripts

The platform gives the firm precisely what it lacked: a way to access, clean, orchestrate and monitor client data across disparate systems.

Why EASL?

• Built by a team with 35+ years of experience moving data at massive scale for Banking and FinServ.
• A full DevOps PaaS delivery—or utilize compartmentalized modules custom to your environment.
• Enable your strategic resources to focus on core value-add development, or put it in their hands.