Data is often one of the most valuable—and most misunderstood—assets in technology acquisitions. In the age of AI and machine learning, proprietary data can be a key differentiator and valuation driver. Data assessment evaluates the quality, accessibility, uniqueness, and monetization potential of data assets.
Why Data Matters in M&A
| Data Value Driver | M&A Impact | Assessment Focus |
|---|
| Proprietary Data Sets | Competitive moat, AI training data | Uniqueness, defensibility, scale |
| Customer Data | Marketing, personalization, cross-sell | Consent, quality, completeness |
| Operational Intelligence | Process optimization, automation | Accessibility, timeliness |
| Analytics Capabilities | Decision-making maturity | Self-service, real-time, predictive |
Real Example: An acquirer paid a 40% premium for a logistics company primarily because of its proprietary route optimization data—15 years of delivery patterns that could train ML models no competitor could replicate. The data was worth more than the technology platform itself.
Data Asset Categories
1. Customer Data
| Type | Value | Risk Considerations |
|---|
| Profile/Demographics | Segmentation, personalization | PII, consent requirements |
| Behavioral Data | Product development, recommendations | Privacy regulations, retention |
| Transaction History | LTV analysis, cross-sell, churn prediction | PCI compliance if payment data |
| Consent Records | Marketing permissibility | GDPR/CCPA compliance |
2. Operational Data
- Business Process Data: Workflow states, approvals, SLAs
- Telemetry: System performance, usage patterns, errors
- Audit Logs: Who did what, when, compliance evidence
- External Feeds: Third-party data integrations
3. Product Data
- Catalogs: Product/service information, attributes
- Content: User-generated content, media assets
- Configuration: Customer-specific settings, customizations
- Training Data: Labeled data for ML models
Data Quality Assessment
Quality Dimensions
| Dimension | Definition | How to Assess |
|---|
| Accuracy | Is the data correct? | Spot checks against source of truth |
| Completeness | Are required fields populated? | Null/empty analysis by field |
| Consistency | Is data uniform across systems? | Cross-system comparison |
| Timeliness | Is data current? | Last updated timestamps |
| Validity | Does data follow rules? | Format/constraint validation |
| Uniqueness | Are duplicates minimized? | Duplicate detection analysis |
Quality Benchmarks
| Data Type | Good | Acceptable | Concerning |
|---|
| Customer Records | >95% complete | 85-95% complete | <85% complete |
| Email Validity | >90% valid | 80-90% valid | <80% valid |
| Duplicate Rate | <3% | 3-10% | >10% |
| Data Freshness | <24 hours | 1-7 days | >7 days |
Data Architecture Assessment
Architecture Maturity Levels
| Level | Characteristics | M&A Implication |
|---|
| Level 1: Silos | Data in application databases, spreadsheets | Integration expensive, no single source of truth |
| Level 2: Warehouse | Centralized DW, batch ETL | Analytics possible but may be stale |
| Level 3: Modern | Data lake/lakehouse, ELT, streaming | Flexible, scalable, near-real-time |
| Level 4: Platform | Self-service, data products, mesh | Decentralized ownership, high maturity |
Key Components to Evaluate
- Data Warehouse: Snowflake, BigQuery, Redshift, Synapse
- Data Lake: S3, Azure Data Lake, GCS with catalog
- ETL/ELT: Fivetran, Airbyte, dbt, Matillion
- Orchestration: Airflow, Dagster, Prefect
- Data Quality: Great Expectations, Monte Carlo, dbt tests
Analytics Capability Assessment
Analytics Maturity
| Level | Capabilities | Tools/Examples |
|---|
| Descriptive | What happened? Historical reporting | Basic BI, dashboards |
| Diagnostic | Why did it happen? Drill-down analysis | Ad-hoc querying, OLAP |
| Predictive | What will happen? Forecasting | ML models, statistical analysis |
| Prescriptive | What should we do? Recommendations | Optimization, decision automation |
BI and Reporting
- Tools: Tableau, Looker, Power BI, Metabase, Mode
- Self-Service: Can business users create their own reports?
- Real-Time: Are dashboards near-real-time or batch?
- Adoption: Are dashboards actually used for decisions?
Data Governance Assessment
Governance Components
| Component | Mature State | Red Flag |
|---|
| Data Catalog | Searchable inventory of all data assets | No documentation of what data exists |
| Data Lineage | Clear understanding of data flow | "We're not sure where that number comes from" |
| Data Ownership | Assigned stewards for each domain | Nobody responsible for data quality |
| Access Control | Role-based access, audit trails | Everyone can see everything |
| Data Dictionary | Documented definitions for fields | Tribal knowledge of what fields mean |
Privacy and Compliance
- Data Inventory: Do they know what PII they have and where?
- Consent Management: Are consent records maintained?
- Data Subject Rights: Can they fulfill deletion/access requests?
- Cross-Border Transfer: Are data transfers compliant?
- Retention Policies: Is old data being properly disposed?
AI/ML Data Assessment
If the target uses or claims AI/ML capabilities:
- Training Data: What data was used? Is it proprietary? Sufficient volume?
- Data Labeling: How was data labeled? Quality of labels?
- Model Performance: What are actual accuracy metrics?
- Bias Assessment: Has model bias been evaluated?
- Data Pipeline: Can models be retrained with new data?
- Feature Store: Are features reusable across models?
Data Red Flags and Costs
| Red Flag | Risk | Remediation Cost |
|---|
| No data quality monitoring | Decisions based on bad data | $50K - $150K |
| Undocumented data lineage | Compliance risk, debugging difficulty | $75K - $200K |
| Critical data in spreadsheets | No audit trail, error-prone | $100K - $300K |
| No master data management | Multiple versions of truth | $150K - $500K |
| Privacy compliance gaps | Regulatory fines, breach liability | $100K - $1M+ |
| Data silos with no integration | Limited analytics value | $200K - $1M |
| No data catalog | Data discovery impossible | $50K - $150K |
Key Takeaway: Data value depends on quality, accessibility, and uniqueness—not just volume. A company with 1 million clean, consented customer records is more valuable than one with 10 million stale, incomplete records. Assess whether data is a genuine competitive advantage or just a liability waiting to be discovered.