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Financial analytics for finance teams

Financial analytics consulting that connects your ERP, accounting and operational data so the CFO and finance team can report, forecast and decide from one trusted view.

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Gross margin fell 2 points because of freight, not priceMargin bridge: Q2 gross margin 38.0%, price +0.3 points, volume and mix -0.2, freight -2.1, Q3 gross margin 36.0% (demo data).Gross margin fell 2 points because of freight, not priceDemo dataGross margin bridge, Q2 to Q3, percent of revenue34%35%36%37%38%39%38.0%Q2 margin+0.3Price-0.2Volumeand mix-2.1Freight36.0%Q3 marginAxis starts at 34%. Price added margin; freight explains the whole fall.Gross margin fell 2 points because of freight, not priceMargin bridge: Q2 gross margin 38.0%, price +0.3 points, volume and mix -0.2, freight -2.1, Q3 gross margin 36.0% (demo data).Gross margin fell 2 pointsbecause of freight, not priceDemo dataGross margin bridge, Q2 to Q3, % of revenue34%36%38%Q2 margin38.0%Price+0.3Volume and mix-0.2Freight-2.1Q3 margin36.0%Axis starts at 34%. Price added margin; freightexplains the whole fall.

Financial analytics turns transactions into answers: where margin is made, how cash will move and which costs are drifting. Our financial analytics consulting gives finance teams the data foundations, dashboards and models to answer those questions without rebuilding spreadsheets every month.

We work across NetSuite, QuickBooks, Odoo, Zoho and Power BI, and we show our approach on finance dashboards built with demo data, so you can see the outputs before sharing your own figures.

CFO view of performance, cash and margin, on demo data

Demo data
Revenue (YTD)$11.7M▲ +4.0% vs budget
Gross margin41.2%▼ -1.3 pts YoY
Opex (YTD)$3.1M▼ -3.1% vs budget
EBITDA margin14.8%▲ +0.3 pts vs budget

Revenue beat budget four months in a row

Monthly revenue vs budget, $M, Jan-Jun

$1.6M$1.8M$2M$2.2MJanFebMarAprMayJunActualBudget$2.1M in Jun: Largest gap above budget$1.6M$1.8M$2M$2.2MJanMarMay$2.1M in Jun: Largest gap above budget
ActualBudget

So what: Volume is ahead, but margin is slipping; check pricing and mix next.

LineActual YTDBudget YTDVariance
Revenue$11.7M$11.2M+4.0%
Cost of sales$6.9M$6.4M+7.2%
Operating expenses$3.1M$3.2M-3.1%
EBITDA$1.7M$1.6M+5.5%
What it is

Financial analytics explains the drivers behind the numbers, not just the totals

Financial analytics is the practice of collecting, modeling and analyzing financial data to explain performance and guide decisions. It goes beyond standard financial reporting, which states what happened, by connecting the numbers to their drivers: volumes, prices, customers, projects and costs.

Three layers of a working financial analytics capability

Select a stage, use Next or the arrow keys

Data layer. The general ledger, subledgers, CRM and project tools are integrated into one consistent financial model.

Step 1 of 3

A working financial analytics capability has three layers. The data layer integrates the general ledger, subledgers and operational systems such as CRM and project tools into one consistent model. The reporting layer delivers dashboards and management accounts that update automatically. The analysis layer supports FP&A work such as variance analysis, forecasting and scenario planning.

Financial data analytics depends on data quality as much as on tools. Clear account mappings, consistent dimensions and reconciled sources are what make the outputs credible to a board, a lender or an investor. That is why financial analytics work should start with the data model rather than the dashboard.

ERP platforms
Benefits

Financial data analytics helps the CFO decide faster and defend the decision

For a CFO, financial data analytics is less about producing more reports and more about faster, better-supported decisions. The finance function is expected to explain performance, not only record it. That requires data that is integrated, reconciled and available on demand.

Each outcome below links a finance decision to the data that supports it. Together they move finance from preparing numbers to advising the business.

FP&A time shifts from preparing numbers to analysisTwo 100% bars: before, 70% of FP&A hours prepare data and 30% go to analysis; after, 25% prepare data and 75% go to analysis (demo data).FP&A time shifts from preparing numbers to analysisDemo dataShare of FP&A team hours in a month, before and after automated data preparationBefore70% preparing data30% analysis and adviceAfter25% preparing data75% analysis and adviceAnalysis time is 2.5 times largerPreparing data: extracting, reconciling and formatting. Analysis: variance, forecast and advice to the business.FP&A time shifts from preparing numbers to analysisTwo 100% bars: before, 70% of FP&A hours prepare data and 30% go to analysis; after, 25% prepare data and 75% go to analysis (demo data).FP&A time shifts frompreparing numbers to analysisDemo dataShare of FP&A team hours in a monthBefore70%30%Preparing dataAnalysis and adviceAfter25%75%Preparing dataAnalysis and adviceAnalysis time is 2.5 times larger

The CFO is the main audience, but controllers, FP&A analysts and the CEO all use the same outputs. That shared use is what makes financial analytics worth the investment: one model, many decisions. Each card shows one outcome.

  • Close to board pack in less time

    Automated flows from ledger to report remove manual work after month-end.

  • No more debates over whose number is right

    Finance, sales and operations share one set of definitions.

  • Know where margin is made and lost

    Profitability by customer, product, project or location shows where to act.

  • Plan cash with confidence

    Forward-looking cash views support financing, investment and working capital decisions.

  • FP&A time spent on analysis

    Automated data preparation frees the team to advise the business.

  • Cleaner audit and lender reporting

    Reconciled, documented data helps finance answer audit queries sooner and makes covenant and lender reporting a routine monthly output.

Types

Four types of financial analytics, each built on the one before

Financial analytics is commonly grouped into four types. Each answers a different question, and each depends on the one before it being reliable. A forecast built on unreconciled actuals is a guess, and a scenario built on a weak forecast is worse.

Know what happened

Descriptive: a monthly P&L and balance sheet dashboard by entity, department and cost center, refreshed automatically after the close.

  • By entity, department and cost center
  • Finance question: what were the results?

The recommended sequence is to strengthen descriptive and diagnostic analytics first, then add forward-looking models. The examples below show what each type looks like in a finance team, from the monthly P&L to a board-level scenario. The point is sequence: reliable actuals first, explanation second, forward-looking views third.

For each type, ask one question: which finance decision would this improve next month? Start with the type that has the clearest answer.

cash flow forecasting
Services

Our financial analytics services cover the path from ledger to board decision

Our financial analytics services cover the full path from source data to board-ready insight. Each one starts with advice on the decision it must support and, once your team is ready, can be delivered as a defined project or as part of an ongoing engagement, and all of them share one governed financial data model. We work with your existing financial analytics software and ERP rather than selling a new tool. Pick the service closest to your most urgent decision.

Cash flow forecastingFinancial reporting automationProfitability analysisFinancial dashboardsNetSuiteQuickBooksPower BI
Solution finder

Which financial analytics service fits your most urgent decision?

2 questions. We suggest one solution. Nothing is sent anywhere.

Illustrative
1. Which question is hardest to answer today?
2. Where does finance lose the most time?
0 of 2 answered
Common challenges

Financial analytics stalls on data, not tools, and the data can be fixed

The same obstacles commonly block the move beyond spreadsheet reporting. The issues are rarely about the BI tool. They are about data structure, definitions and capacity, and each one has a practical fix. Here is how we address them.

  1. 1

    One model instead of scattered data

    We integrate ERP, CRM and spreadsheets with mapped accounts and shared dimensions.

  2. 2

    Month-end without manual rework

    We automate preparation and refresh, with reconciliation checks on every run.

  3. 3

    KPIs that mean the same everywhere

    We document each metric with finance and apply it in every report.

  4. 4

    Forecasts the team can explain

    We build transparent driver models that finance can test and adjust.

  5. 5

    Capacity without new headcount

    Analytics as a service keeps reports and models maintained.

  6. 6

    Board questions answered on the spot

    Drill-through from summary to transaction lets finance answer board questions in the meeting rather than afterwards.

A financial analytics review shows where to start

Share your current reporting pack and systems. We will map the gaps, show where automation would save the most time and outline the first financial analytics improvements worth making, in a short written summary you can share with your leadership team. If implementation would be premature, the summary says so.

Explore financial analytics resources
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