Gold Standard Data Assessment

Complete technical data evaluation with premium engagement

Data is a critical asset in modern businesses, yet it's often overlooked in M&A due diligence. Our data technical due diligence experts assess data infrastructure, warehouse architecture, quality frameworks, and governance maturity—identifying risks that impact valuations and post-acquisition integration.

Available as Gold Standard with extended engagement and managed services
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Data Due Diligence Assessment Areas

Our comprehensive data assessment covers the complete data infrastructure, from sources through analytics and business intelligence platforms.

Data Architecture & Infrastructure

We evaluate the overall design and architecture of your data infrastructure, including:

  • Data warehouse vs. data lake vs. lakehouse architecture
  • Cloud vs. on-premises deployment models
  • Scalability and performance characteristics
  • Multi-region and disaster recovery capabilities
  • Legacy system dependencies and technical debt

Database Platforms & Performance

Assessment of database technologies, performance, and operational excellence:

  • Relational databases (SQL Server, PostgreSQL, Oracle, MySQL)
  • NoSQL and non-relational databases
  • Query performance and optimization opportunities
  • Backup, recovery, and business continuity
  • Licensing costs and optimization

Data Integration & ETL

Evaluation of data movement, transformation, and integration processes:

  • ETL/ELT pipeline architecture and tools
  • Data ingestion from multiple sources
  • Data quality checks and validation rules
  • Pipeline performance and scalability
  • Modern vs. legacy integration approaches

Data Quality & Governance

Assessment of data quality frameworks and governance maturity:

  • Data quality metrics and monitoring
  • Master data management practices
  • Data catalog and metadata management
  • Data lineage and documentation
  • Data stewardship programs

Business Intelligence & Analytics

Evaluation of BI platforms, reporting, and analytics capabilities:

  • BI platform selection and deployment (Tableau, Power BI, Looker, etc.)
  • Dashboard and reporting infrastructure
  • Self-service analytics capabilities
  • Data visualization practices
  • Advanced analytics and statistical modeling

Data Security & Access Control

Assessment of data security and governance frameworks:

  • Data encryption (at rest and in transit)
  • Role-based access control (RBAC)
  • Row-level and column-level security
  • Audit logging and compliance tracking
  • Sensitive data identification and masking

Data Monetization & Strategy

Evaluation of data as a strategic asset and revenue driver:

  • Data product identification and valuation
  • Monetization models and revenue opportunities
  • Data marketplace readiness assessment
  • Third-party data partnerships
  • Data-driven business model opportunities

Real-Time & Streaming Data

Assessment of real-time data processing capabilities:

  • Event streaming architecture (Kafka, Kinesis)
  • Real-time analytics and dashboards
  • Stream processing frameworks
  • Latency and throughput requirements
  • Event-driven architecture maturity

Swipe or use arrows to explore all assessment areas

Data Platforms & Technologies We Evaluate

We have deep expertise across the full spectrum of data platforms and technologies used in modern organizations.

Cloud Data Warehouses

  • ✓ Snowflake
  • ✓ Amazon Redshift
  • ✓ Google BigQuery
  • ✓ Azure Synapse
  • ✓ Databricks

Data Lake Platforms

  • ✓ Apache Hadoop
  • ✓ AWS Lake Formation
  • ✓ Azure Data Lake
  • ✓ Delta Lake
  • ✓ Apache Iceberg

Relational Databases

  • ✓ Oracle Database
  • ✓ SQL Server
  • ✓ PostgreSQL
  • ✓ MySQL
  • ✓ MariaDB

ETL/Integration Tools

  • ✓ Apache Airflow
  • ✓ Talend
  • ✓ Informatica
  • ✓ AWS Glue
  • ✓ Azure Data Factory

BI & Analytics Platforms

  • ✓ Tableau
  • ✓ Power BI
  • ✓ Looker
  • ✓ QlikView
  • ✓ Microstrategy

NoSQL Databases

  • ✓ MongoDB
  • ✓ Cassandra
  • ✓ DynamoDB
  • ✓ Elasticsearch
  • ✓ Redis

Streaming & Real-Time

  • ✓ Apache Kafka
  • ✓ Amazon Kinesis
  • ✓ Apache Flink
  • ✓ Apache Spark Streaming
  • ✓ Confluent Platform

Data Lineage & Catalog

  • ✓ Collibra Data Catalog
  • ✓ Alation Data Catalog
  • ✓ Atlan
  • ✓ Apache Atlas
  • ✓ dbt (data build tool)

Why Data Due Diligence Matters in M&A

Data is increasingly central to business value, competitive advantage, and operational efficiency. Yet data risks are often overlooked in M&A.

Data Quality Issues Cost Money

Poor data quality directly impacts post-acquisition value realization. Common issues include:

  • Inconsistent or incomplete master data
  • Duplicate records across systems
  • Outdated or incorrect customer/product data
  • Poor data documentation and lineage
  • Ad-hoc reporting and analytics

Impact: Inaccurate reporting, poor decision-making, failed analytics initiatives, and IT resource drain.

Technical Debt Slows Integration

Legacy data infrastructure creates post-acquisition challenges:

  • Difficult or expensive system migrations
  • Complex data consolidation requirements
  • Outdated platforms nearing end-of-life
  • Expensive licensing and support costs
  • Architectural mismatches with acquirer

Impact: Delayed integration, cost overruns, and ongoing IT complexity.

Key Data Due Diligence Questions

  • ❓ What is the quality of master data (customers, products, financial)?
  • ❓ How scalable is the current data infrastructure?
  • ❓ What technical debt exists in legacy systems?
  • ❓ How robust are data governance and quality frameworks?
  • ❓ What is the cost of current data infrastructure?
  • ❓ How secure is sensitive customer and business data?
  • ❓ Can data easily integrate with acquirer systems?
  • ❓ What analytics and insights capabilities exist?

Our Data Assessment Process

A comprehensive evaluation methodology designed to identify data risks and opportunities.

1

Architecture Discovery

Document current data architecture, platforms, tools, and systems. Identify all data sources, transformation processes, and analytics platforms.

2

Data Inventory & Quality Assessment

Sample key data domains (customers, products, financials). Assess data quality, completeness, consistency, and identify quality issues.

3

Technical Evaluation

Assess platform performance, scalability, security, backup/recovery, disaster recovery capabilities, and licensing implications.

4

Integration Analysis

Evaluate compatibility with acquirer's data infrastructure. Identify integration complexity, data mapping requirements, and consolidation strategies.

5

Governance & Compliance

Assess data governance maturity, stewardship programs, security controls, regulatory compliance, and privacy readiness.

6

Reporting & Roadmap

Deliver detailed assessment report with risk quantification, integration roadmap, and post-acquisition optimization recommendations.

Swipe or use arrows to explore all process steps

Common Data Due Diligence Findings

Based on our experience across 100+ M&A transactions, here are the most common data-related findings we identify:

01

Data Quality Issues

Duplicate customer records, incomplete master data, inconsistent naming conventions, outdated information causing inaccurate reporting and failed analytics initiatives.

Impact: 15-25% of IT resources needed for remediation

02

Architectural Challenges

Legacy systems not designed for scale, multiple disconnected data silos, outdated database platforms, poor separation of concerns.

Impact: 3-6 month delay in integration initiatives

03

Cost Inefficiencies

Over-provisioned infrastructure, expensive legacy licenses, inefficient data warehouse configurations, unsupported platforms.

Impact: 20-40% cost reduction opportunity post-acquisition

04

Governance Gaps

No formal data governance, undefined data ownership, poor metadata management, inconsistent data security practices.

Impact: Compliance risk, poor data decision-making

05

Limited Analytics Capability

Ad-hoc reporting, limited self-service analytics, poor visualization practices, minimal predictive analytics.

Impact: Missed value creation and competitive advantage

06

Documentation Deficits

Missing data lineage, poorly documented processes, no current architecture diagrams, tribal knowledge concentrated in few people.

Impact: Knowledge loss, slow onboarding, integration delays

07

Integration Complexity

Incompatible data formats, non-standard APIs, tightly coupled systems, missing integration layers, and complex ETL dependencies.

Impact: 2-4x longer integration timelines than expected

08

Talent & Skills Gaps

Key person dependencies, outdated skill sets, limited data engineering expertise, no dedicated data team, high turnover risk.

Impact: $500K-$2M in hiring and training costs post-acquisition

Need a Data Technical Due Diligence Assessment?

Our data experts will provide a comprehensive assessment of your target's data infrastructure, quality, architecture, and integration readiness. Let's identify the data risks and opportunities that matter to your M&A deal.

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