BI & analytics
Connect revenue, EBITDA, working capital, budgets, and operating drivers to a reviewable company performance model. Give operating partners and portfolio CFOs a way to investigate variance and track agreed actions.
The problem
Common signals
Concrete output
Connect actuals, approved budgets, period-end balances, and relevant operating activity using explicit company and period keys. Define revenue, reported EBITDA, approved adjustments, working capital, and the comparison population with the finance owner.
Build portfolio-to-company and, where sources support it, company-to-location or product review. Preserve source coverage and cutoff context. A variance should lead to the relevant evidence and accountable owner rather than another disconnected chart.
Show actual versus budget, a reconciled revenue or EBITDA bridge, and closing working-capital balances. Distinguish genuine operating changes from acquisition perimeter, timing, currency, and reclassification effects before drawing conclusions.
Attach an agreed baseline, owner, action, review date, measure, and status to a performance issue. Keep scenario assumptions separate from observed results, avoid overlapping benefit claims, and record whether a change reflects released cash, reduced spend, or available capacity.
Scope variants
Build a limited set of shared financial and operating measures with company and period filters. Preserve company-specific definitions where a consolidated comparison would be misleading.
Focus on a defined question such as revenue mix, labor utilization, location contribution, or receivables aging. Establish a reliable driver model and review process before extending the analysis.
Connect a selected initiative to its source baseline and recurring reporting. Define how timing, seasonality, scope changes, and other initiatives affect attribution; use a documented comparison rather than assuming the initiative caused every improvement.
Delivery approach
Agree the buyer, review cadence, selected companies, source availability, and action the analysis should inform. Choose measures the operating team can interpret and influence.
Reconcile an approved period, budget version, and set of balances. Preserve reporting grain and mapping versions. Separate source gaps from verified zero activity and establish the comparable population.
Implement calculations, filters, driver detail, and visible exceptions. Validate totals independently and test whether reviewers can locate the source and owner behind a material variance.
Run an agreed reporting cycle with finance and operations. Capture commentary, decisions, open issues, and approved corrections. Hand over measure definitions, refresh monitoring, access rules, and a controlled change process.
Technical context
Engagement fit
Anonymized proof
The synthetic operations demo exposes weighted EBITDA margin, budget variance, period-end working capital, freshness, and reconciliation exceptions across six fictional companies. Existing anonymized reporting experience supports the systems approach; demo values are not client outcomes or portfolio benchmarks.Explore representative work
Questions
Reporting automation makes the collection, calculation, review, and release cycle repeatable. Performance analytics adds the model and investigation path for understanding operating variance and reviewing actions. The projects can share a data foundation while retaining distinct acceptance criteria.
Use a small shared financial contract where definitions are compatible, then keep operating drivers specific to the business. Comparable company totals do not imply that every productivity ratio or operating metric should be ranked across the portfolio.
A combined EBITDA margin divides total eligible EBITDA by total eligible revenue for the selected period and population. Averaging company margin percentages gives a different result. The public demo includes transparent inputs and calculations for this distinction.
It can preserve the baseline, assumptions, costs, observations, and comparison needed for review. It does not automatically establish causation. Separate scenario estimates from realized results and account for timing, other initiatives, and changes in the reporting population.
Yes. The analytics work establishes the measure, source, and review need. When the next step requires workflow software, an API integration, or an AI-assisted task, Permadyn AI provides a related implementation path with its own scope and controls.
Start with the variance or operating decision your current reports cannot explain. Define the baseline, sources, and first reviewable model through the existing diagnostic engagement.
Discuss a portfolio performance questionThe packaged engagements and delivery models make the commercial boundary visible without forcing every project into the same shape.