Private EquityExafort Intelligence

A Common Analytics Language for a Private Equity Portfolio

A technical account of centralized data pipelines, governed financial definitions, portfolio dashboards, and automated investor reporting.

TableauPower BIOracle NetSuite

The Operating Review Stalled on Definitions

The operating review reached an impasse. Participants could not establish whether matching KPI labels represented matching calculations.

Portfolio companies submitted financials by email in their own formats. The deal team then re-keyed the material into a master Excel model.

The issue started before reporting. Each company used its own chart of accounts and revenue recognition timing. EBITDA and operational KPI definitions also differed.

Different Systems, Different Financial Languages

The portfolio spanned manufacturing, SaaS, healthcare services, and logistics. Its source systems included NetSuite, SAP, QuickBooks, Sage, and custom applications.

The firm needed a common reporting language without replacing those systems. It also needed to preserve each company's operating context.

Source formats were incompatible with direct portfolio comparison.
Financial definitions varied by company.
Investor reporting depended on manual spreadsheet consolidation.

A Common Portfolio Data Model

Exafort built automated pipelines from the portfolio ERPs into Snowflake Data Cloud. Fivetran handled source extraction where appropriate.

dbt transformation models mapped charts of accounts and operating metrics to a governed taxonomy. The model harmonized EBITDA, revenue recognition, working capital, and operational KPIs.

The mapping layer sat between the source systems and the reporting tools. No source ERP replacement was required.

NetSuite API and SAP RFC supported source connectivity.
Data validation was applied before information reached reporting views.
Anomaly detection flagged financial outliers and operational deviations.

Audience-Specific Decision Views

Tableau dashboards were designed around the decisions made by each audience. Operating partners received a portfolio health heatmap, company comparisons, exception alerts, and trend analysis.

Portfolio company executives received peer benchmarking and operational KPI tracking. These views preserved company-level detail while using the common taxonomy.

Cross-portfolio procurement analytics supported group purchasing analysis. The same model also supported examination of shared-services opportunities.

Automated Reporting and Validation

Investor reporting used automated quarterly packages with commentary templates. Validation checks were part of the reporting flow.

Power BI Embedded was included in the technology scope alongside Tableau. The reporting layer consumed governed warehouse data rather than emailed spreadsheets.

The delivery also included benchmarking models and stakeholder training. Those capabilities were connected to the shared data foundation.

Source Systems Remained in Place

The architecture did not force portfolio companies onto a common ERP. NetSuite, SAP, QuickBooks, Sage, and custom systems remained source applications.

Standardization happened in the extraction, transformation, and semantic layers. This separated portfolio governance from local transaction processing.

The result was a centralized analytics design built around common definitions. It preserved the systems named in the source material and changed how their data was prepared for review.

Client names and identifying details are withheld under confidentiality obligations.