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UseCase

Data Lakehouse Modernization

PiTech has delivered complex data platform migrations for financial institutions compressing an 18-month migration to under 11 months, auto-resolving 68% of data conflicts, and reducing compliance overhead by 43%. We apply that same methodology to every banking data modernization engagement.

11 months

18-month migration delivered

68%

Data conflicts auto-resolved

43%

Compliance overhead reduction

73%

At-risk data assets protected

Client Snapshot

Industry

Banking & Financial Services

Solution

Data Solutions | Cloud Solutions

Complexity

High

Delivery

Architecture + Implementation

The Problem

Legacy data architectures at most regional banks evolved incrementally over decades  one data mart for risk, one warehouse for finance, one reporting database for compliance  producing a fragmented landscape that no single team fully understands. When regulators request data cuts for DFAST, CCAR, BSA/AML examination, or fair lending analysis, operations teams spend weeks manually assembling data from systems that were never designed to communicate. The cost is not just operational  it is a risk management liability.

Without a clean, unified data platform, AI investments produce unreliable outputs regardless of model quality. Every AI initiative at a bank traces back to data infrastructure quality. Institutions that attempt AI adoption without addressing fragmented data foundations consistently discover this through expensive, delayed projects that fail to deliver expected outcomes.

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43%

compliance overhead reduction achieved for a financial institution after PiTech migrated fragmented legacy data infrastructure to a unified platform through automated conflict resolution, single source of truth reporting, and elimination of manual data assembly for regulatory submissions.

How PiTech Delivers

01

Current-State Architecture Inventory

All data platforms, warehouses, marts, and pipelines inventoried. Data lineage mapped from source systems to regulatory reports and AI consumers. Technical debt quantified in operational risk and business impact terms.

02

Target State Architecture Design

Lakehouse architecture designed for the institution’s regulatory reporting, AI readiness, and cloud strategy IBM InfoSphere, Databricks, Snowflake, or cloud-native platforms selected based on existing investments and data residency requirements.

03

Risk-Tiered Migration with Automated Conflict Resolution

Migration sequenced by regulatory and business criticality. Automated conflict resolution engine handles the majority of data quality issues without manual intervention reserving human review for complex edge cases that require business steward judgment.

04

Parallel-Run Validation and Cutover

Legacy environment remains live throughout migration. Reconciliation dashboards confirm data parity across all critical regulatory and business reporting domains before any cutover. Cutover is against validation evidence  not a calendar deadline.

Proven Outcomes

11 months

18-month banking data migration delivered in reference engagement

68%

Data conflicts auto-resolved without manual intervention

43%

Compliance overhead reduction through unified data infrastructure

Proven Outcomes

18+

Years in Regulated Industries

What You Gain

11 months

18-month migration timeline compressed in reference banking engagement

68%

Data conflicts resolved automatically not through manual intervention

43%

Reduction in compliance reporting overhead through unified data access

Single

Source of truth replacing fragmented warehouse and data mart environments

What's Included

Data architecture inventory

Data architecture inventory

Current-state platform catalog with lineage mapping from source systems to regulatory consumers

Target state architecture design

Target state architecture design

IBM InfoSphere, Databricks, Snowflake, AWS Glue, or Azure Synapse driven by existing investments

Automated conflict resolution engine

Automated conflict resolution engine

Data quality issue classification with deterministic resolution rules and human escalation for edge cases

Risk-tiered migration execution

Risk-tiered migration execution

Regulatory reporting data migrated first; AI and analytics consumers follow in sequenced phases

Parallel-run validation environment

Parallel-run validation environment

Reconciliation dashboards confirming data parity before any production cutover

Data governance layer

Fair lending monitoring module

Data governance layer

Business definitions, lineage documentation, stewardship workflows, and access controls in target platform

Analytics and AI enablement

Analytics and AI enablement

Downstream BI and AI consumer access layer on the new unified platform

Frequently Asked Questions

How does PiTech compress migration timelines so significantly?

Automated conflict resolution is the primary driver. PiTech’s resolution engine classifies data quality issues by severity and type, applies deterministic rules for the majority, and surfaces only complex conflicts to business data stewards with resolution recommendations. Eliminating manual conflict resolution from the critical path consistently compresses timelines 25–40% versus traditional approaches.

PiTech has deep implementation expertise in IBM InfoSphere, Databricks, Snowflake, AWS Glue and Lake Formation, and Azure Synapse. Platform selection is driven by the client’s existing investments, cloud strategy, and regulatory data residency requirements  not vendor preference.

Yes. PiTech’s parallel-run methodology keeps the legacy environment live throughout migration. Production regulatory submissions continue from the legacy system until reconciliation dashboards confirm data parity across all critical reporting domains. Cutover occurs only after validation never against a calendar deadline.
Every AI model is only as reliable as the data it runs on. A unified, governed data platform eliminates the fragmented, inconsistent data that causes AI model failures in production. PiTech sequences the data foundation first  specifically so AI investments that follow deliver on their expected business case and pass SR 11-7 model validation.

The modernization replaces tribal knowledge with documented, version-controlled data pipelines and a regulatory data mart with full source-to-submission lineage. When a key person leaves after modernization, the institution has documented pipelines and reproducible processes  not a compliance crisis.

A modern data platform is prerequisite infrastructure for AI, regulatory readiness, and competitive analytics.

PiTech has executed these migrations for regulated financial institutions under time pressure with documented outcomes. Contact us to discuss your program.
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