Private Equity and Venture Capital
Create trusted data foundations for portfolio growth and value creation.
After an acquisition, merger or major investment, leadership teams need reliable information to understand performance, prioritise improvements and execute the value-creation plan. Erisna helps investors and portfolio companies connect fragmented systems, improve data quality, establish accountability and create more dependable management reporting.
PE and VC use cases
Post-investment data foundations
Create an initial view of the portfolio company’s critical systems, datasets, reports, owners and quality risks.
This can support early value-creation planning by helping management understand:
- Which systems hold critical information
- Which reports depend on manual processes
- Where data is duplicated or inconsistent
- Which KPIs lack agreed definitions
- Who owns important datasets
- Which issues may prevent reliable reporting
Business outcome: A prioritised and evidence-led roadmap for improving the company’s data environment.
Portfolio company reporting improvement
Strengthen the information feeding management, investor and board reporting.
Erisna can help catalogue reporting datasets, document definitions, apply validation rules and identify recurring quality problems. Relevant reporting areas may include:
- Revenue and commercial performance
- Customer and sales metrics
- Cost and margin
- Inventory and operations
- Cash and finance
- Workforce information
- Product and service performance
Business outcome: More consistent reporting and greater confidence in the information used to track performance.
Post-merger data integration
When organisations combine, similar information is often recorded differently across systems and entities.
Customers, products, suppliers, locations, business units and financial categories may have inconsistent definitions or structures. Erisna supports:
- Source-system discovery
- Dataset and field mapping
- Business-term standardisation
- Source-to-target documentation
- Data-quality validation
- Ownership assignment
- Lineage and reconciliation evidence
Business outcome: Faster integration with clearer visibility of what has been combined, transformed or left unresolved.
Data platform and system modernisation
Support portfolio companies replacing legacy systems, implementing new ERP or CRM platforms, consolidating databases or moving reporting to modern cloud environments.
Erisna can provide a structured layer for:
- Profiling current data
- Mapping old and new structures
- Defining migration rules
- Validating migrated records
- Recording lineage
- Reconciling exceptions
- Monitoring quality after go-live
Business outcome: Reduced data-related implementation risk and stronger evidence that the new environment is operating as intended.
AI readiness across portfolio companies
Many portfolio companies want to introduce AI, automation or advanced analytics, but their underlying data may not yet be sufficiently understood or trusted.
Erisna helps establish:
- What relevant data exists
- Where it is held
- Who owns it
- How complete it is
- Which quality issues exist
- Whether sensitive information is appropriately classified
- What should be improved before deployment
Business outcome: AI initiatives are prioritised using a clearer assessment of data readiness rather than assumptions.
From post-investment fragmentation to operational clarity
The portfolio challenge
Systems, datasets and reporting processes differ across companies, functions and acquired entities.
The Erisna data layer
Catalogue critical information, connect relevant sources, standardise definitions, validate quality and clarify ownership.
The business outcome
Create a more reliable foundation for portfolio reporting, operational improvement, integration, transformation and future AI adoption.
Erisna can be deployed for one priority portfolio company or used as a repeatable approach across several investments.
How Erisna supports the portfolio value-creation lifecycle
1. Assess
Identify critical systems, datasets, reports, definitions, owners and quality gaps.
Create a practical view of the current data environment rather than relying solely on interviews and documentation.
2. Align
Agree common definitions, target reporting requirements, ownership responsibilities and priority data domains.
Where businesses or entities are being combined, document source-to-target relationships and standardisation requirements.
3. Improve
Connect relevant data, define validation rules, address high-priority issues and build more reliable reporting or analytics layers.
Combine live data platforms with specialist delivery across strategy, governance, engineering, analytics and AI.
4. Monitor
Track data health, failed rules, remediation activity and ownership over time.
Continue monitoring critical datasets as the company grows, implements new systems or completes further acquisitions.
A repeatable model across the portfolio
Portfolio-company diagnostic
Run a focused data and reporting assessment for one company, producing an evidence-led improvement roadmap.
Post-acquisition accelerator
Use a repeatable discovery, mapping, quality and governance process during the early stages of integration.
Management-reporting improvement
Improve the quality and consistency of data used by executive, board and investor reporting.
Transformation assurance
Add data-quality and governance controls to ERP, CRM, cloud and analytics programmes.
Ongoing data monitoring
Maintain scheduled checks and periodic quality reviews after the initial implementation.
Portfolio-wide framework
Create reusable definitions, rules, reporting principles and governance patterns that can be adapted across several companies.
What Erisna helps PE and VC firms achieve
Value across different stakeholders: investment, operations, leadership, finance, technology, data, risk and governance teams
Give portfolio companies cleaner data from day one
Improve integration, reporting and operational visibility while establishing a trusted foundation for transformation, growth and AI.
