ACI Infotech
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ACI Infotech

Enterprise data and AI, engineered and run in production.

ACI Infotech is an enterprise data and AI engineering firm headquartered in Somerset, New Jersey, with delivery hubs worldwide. We build the data foundation, put AI on top of it, and run both in production for enterprises in financial services, healthcare, retail, manufacturing, and energy.

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ACI Infotech
  • Founded 2006
  • 1,200+ engineers
  • 500+ enterprise projects
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All playbooks

/ Data Engineering playbook

40 Systems → One

Post-Acquisition System Consolidation

Complete playbook for consolidating 30-50 disparate systems post-merger with zero disruption to financial reporting.

  • 23x deployed
  • Data Engineering
  • 12-18 months typical
  • 15-25 consultants

/ Typical outcomes

$9.2M

Average year-one savings

Zero

Financial reporting disruptions

78%

Manual effort reduced

5 days

Monthly close cycle

/ Overview

What this playbook is for

When companies merge or acquire, the immediate challenge isn't strategy but rather the chaos of 30-50 disparate systems that don't talk to each other. Finance teams spend 40% of their time manually reconciling data across systems, executives can't get a unified view of the combined entity, and regulatory compliance becomes a nightmare with no unified audit trail. This playbook documents our battle-tested approach refined across 23 enterprise deployments in financial services, private equity portfolio companies, healthcare, and manufacturing.

/ Challenge pattern

When this playbook applies

This playbook fits organizations facing these common challenges:

  • 0130-50 disparate systems from merged entities requiring consolidation with no clear integration path
  • 02Multiple data formats with inconsistent standards, definitions, and business rules across legacy systems
  • 03Finance teams spending 40%+ of time manually reconciling data instead of strategic analysis
  • 04Regulatory compliance (SOX, GDPR, HIPAA) requiring unified audit trails that don't exist
  • 05Executive mandate for aggressive 12-18 month timeline without any business disruption
  • 06Political complexity with different teams owning different systems and resistant to change

/ Solution approach

How the pattern runs

  • Phase 1 - Discovery & Assessment: Comprehensive inventory of all systems, data flows, dependencies, and ownership. Identify quick wins vs. complex integrations. Map regulatory requirements.
  • Phase 2 - Architecture Design: Design target state architecture with unified data model. Define integration patterns (real-time vs. batch). Establish data governance framework.
  • Phase 3 - Phased Migration: Execute parallel runs with automated validation before each cutover. Start with non-critical systems, build confidence, then migrate core systems.
  • Phase 4 - Data Quality Gates: Implement automated checks catching 95%+ of issues before they impact downstream systems. Continuous monitoring and alerting.
  • Phase 5 - Compliance & Audit: SOX compliance by design with audit logging, segregation of duties, and control frameworks built in from day one.
  • Phase 6 - Change Management: Stakeholder communication plan, user training program, and organizational change management to ensure adoption.

/ Key learnings

Hard-won lessons from 23 deployments

01

Phased migration with parallel runs eliminates cutover risk. Never do "big bang" migrations, they fail 70% of the time.

02

Automated data quality gates catch 95% of issues before they become business problems. Invest in quality early.

03

SOX compliance must be designed in from day one. Retrofitting compliance after the fact is 3x more expensive.

04

Executive sponsorship is critical. Weekly steering committee meetings with C-level attendance ensure blockers are removed fast.

05

Plan for 30% more complexity than initial assessment suggests. Hidden integrations and undocumented processes always surface.

06

Political challenges are harder than technical ones. Invest in change management and stakeholder alignment early.

/ Stack

  • SAP S/4HANA
  • Python ETL
  • Azure/AWS Data Lakes
  • PowerBI
  • Auto Reconciliation
  • Audit Logging

/ Industries served

  • Financial Services
  • Private Equity
  • Healthcare
  • Manufacturing

/ Results

What the pattern delivers

$9.2M

Average year-one savings

Achieved through process automation, system consolidation, and reduced manual reconciliation effort

Zero

Financial reporting disruptions

All quarterly closes completed on time during migration. No restatements required.

78%

Manual effort reduced

Finance team time freed from reconciliation to focus on strategic analysis and business partnering

5 days

Monthly close cycle

Reduced from 15 days to 5 days through automated reconciliation and unified reporting

/ More patterns

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Let's Walk Through This Playbook