Data Analytics for Finance Managers Budgeting and Performance Training Course

Course overview

Finance managers are expected to explain not only what happened against budget, but why it happened, what is likely to happen next, and which actions will protect margin, cash and operating capacity. Yet many finance teams still consolidate spreadsheets manually, analyse variances at an aggregate level, and circulate reports that cannot be traced to source assumptions. This course equips finance managers to turn budget, actual, forecast and operational data into controlled analysis that supports accountable performance conversations and faster decisions.

Participants build a practical finance analytics workflow covering data preparation, data modelling, variance decomposition, driver-based budgeting, rolling forecasts, scenario modelling and executive dashboard design. They use Microsoft Excel, Power Query, Power BI and SQL Server to combine general ledger, payroll, sales, procurement and operational datasets. The course develops the ability to define finance KPIs, test data quality, separate price, volume and mix effects, build forecast assumptions, identify exceptions and communicate findings in board-ready narratives.

Teaching is instructor-led and based on a connected finance case: a multi-division organisation facing revenue pressure, cost escalation and working-capital constraints. Participants work through data extracts, build models and review decisions in management-performance meetings. By the end of the programme, each participant produces a finance performance analytics pack containing a data model, budget-versus-actual analysis, rolling forecast, scenario model, Power BI dashboard and a 90-day implementation plan for their own reporting environment.

The programme is designed for finance managers and senior finance professionals who already work with budgets, management accounts or forecasts and need stronger analytical control over performance reporting. It is equally valuable to leaders approving the training because its outputs can be adapted directly into monthly reporting, planning and business-review processes.

Course objectives

By the end of this course, participants will be able to:

• Design a finance data model linking actuals, budget, forecast and operational-driver data
• Clean and reconcile finance datasets using Excel Power Query transformation steps
• Write SQL queries to extract and validate general ledger and operational data
• Perform price, volume, mix and rate variance analysis for management reporting
• Build a driver-based budget model with auditable assumptions and version controls
• Create rolling forecasts and scenario models for revenue, costs, cash and capacity
• Develop a Power BI dashboard with finance KPIs, drill-through views and exception alerts
• Produce a board-ready performance pack with recommendations, risks and action owners

Benefits of attending

Target competencies

Who should attend

• Finance Managers — who own budgets, forecasts and monthly performance explanations
• FP&A Managers — who need repeatable models and reliable forward-looking analysis
• Commercial Finance Managers — who must connect sales, margin and operational drivers
• Financial Controllers — who need stronger data controls across management reporting
• Business Unit Finance Leads — who challenge local assumptions and performance plans
• Senior Management Accountants — who are moving from report production to decision support

Requirements and prerequisites

Participants should be comfortable reading management accounts, interpreting profit and loss, balance sheet and cash-flow measures, and working with annual budgets or periodic forecasts. Confident use of Microsoft Excel is assumed, including formulas, pivot tables, charts and basic lookup functions; familiarity with Power Query or Power BI is helpful but not essential. Participants should understand common finance concepts such as actuals, budget, forecast, variance, accruals and cost centres. No prior SQL, Python, advanced statistics or data-science experience is required. A laptop capable of running Excel and Power BI Desktop is needed for practical sessions.

Training methodology

Each day combines focused instructor demonstrations with guided work in finance datasets and models. Participants use a continuing case involving divisional budgets, general ledger actuals, sales volumes, headcount and cash data, progressing from data validation to executive reporting. Exercises include SQL extraction, Power Query transformation, Excel model construction, Power BI dashboard development and structured management-review discussions. Small groups challenge forecast assumptions and recommend corrective actions. On the final two days, participants assemble their own performance analytics pack and convert it into a 90-day workplace application plan.

Course outline

Day 1: Finance analytics foundations and performance questions

• Finance analytics operating model and decision-use cases
• Links between management accounts, operational data and performance drivers
• KPI trees for revenue, margin, cost, cash and capacity
• Actual, budget, forecast and prior-period comparison logic
• Data lineage, control points and finance data ownership
• Materiality thresholds and exception-based management reporting
• Analytical question framing for business performance reviews

Workshop: Participants map a divisional performance question into a KPI tree, data requirements list and management-review agenda.

Day 2: Preparing and controlling finance data

• Finance data structures for chart of accounts, cost centres and legal entities
• Data-quality profiling for completeness, validity, uniqueness and timeliness
• Power Query imports from workbooks, CSV files and folders
• Data cleansing rules for dates, account codes, vendors and departments
• Appending actuals and forecast files across reporting periods
• Merging finance and operational tables using controlled keys
• Reconciliation checks between source totals and reporting outputs

Workshop: Participants transform and reconcile raw general ledger, sales and headcount files into a controlled reporting dataset.

Day 3: SQL for finance data extraction and validation

• Relational database concepts for finance managers
• SQL SELECT, WHERE and ORDER BY statements
• Aggregation with GROUP BY for account and cost-centre analysis
• INNER JOIN and LEFT JOIN for ledger and operational datasets
• CASE expressions for finance classifications and reporting bands
• Common table expressions for readable finance queries
• SQL validation queries for duplicates, missing mappings and control totals

Workshop: Participants write SQL queries that extract monthly actuals by business unit and identify unmapped or duplicate transactions.

Day 4: Variance analysis and performance diagnosis

• Flexible budgeting and activity-adjusted performance comparisons
• Price, volume and mix variance decomposition
• Rate, efficiency and spending variance analysis
• Labour cost analysis using headcount, FTE and utilisation drivers
• Gross-margin bridge construction and waterfall charts
• Materiality ranking and root-cause investigation methods
• Translating variance evidence into accountable management actions

Workshop: Participants build a margin bridge and variance commentary identifying the three largest drivers and proposed corrective actions.

Day 5: Driver-based budgeting and model governance

• Driver-based planning architecture and model design principles
• Revenue models using volume, price, conversion and retention assumptions
• Cost models using activity, headcount, supplier and capacity drivers
• Assumption registers, input controls and approval workflows
• Excel structured tables, named ranges and dynamic formulas
• Model audit checks, error flags and change-control logs
• Budget version comparison and baseline management

Workshop: Participants construct a driver-based operating budget with documented assumptions, control checks and version comparison.

Day 6: Rolling forecasts and scenario modelling

• Rolling forecast cadence and forecast-horizon design
• Forecast methods using run rates, seasonality and operational drivers
• Base, upside and downside scenario construction
• Sensitivity analysis for price, volume, payroll and exchange-rate variables
• Cash-flow forecasting from profit, working capital and capital expenditure
• Forecast accuracy measures including bias and mean absolute percentage error
• Trigger points and contingency actions for forecast changes

Workshop: Participants create a 12-month rolling forecast with three scenarios and quantify the cash and margin implications of each.

Day 7: Power BI modelling for finance reporting

• Star-schema design for finance and operational reporting
• Fact tables, dimensions and finance calendar tables
• Power BI relationships, filter direction and model integrity
• DAX measures for actuals, budget, forecast and variance
• Time-intelligence measures for month-to-date and year-to-date reporting
• Calculation groups and consistent finance measure definitions
• Row-level security concepts for business-unit reporting

Workshop: Participants build a Power BI semantic model and DAX measure set for actual, budget, forecast and year-to-date performance.

Day 8: Executive dashboards and performance narratives

• Dashboard hierarchy for executives, budget holders and analysts
• KPI cards, variance waterfalls, decomposition trees and trend charts
• Drill-through pages for cost-centre and product investigation
• Conditional formatting and exception alert design
• Dashboard usability, visual integrity and accessibility checks
• Narrative structures for financial performance commentary
• Decision logs linking insights to owners, actions and due dates

Workshop: Participants create an executive Power BI dashboard and deliver a five-minute performance briefing based on its findings.

Day 9: Analytical decision support and finance business partnering

• Decision framing for cost actions, investment choices and resource allocation
• Contribution analysis and customer or product profitability
• Working-capital analytics for receivables, inventory and payables
• Capital expenditure business cases and benefit tracking
• Risk-adjusted scenario communication for non-finance stakeholders
• Challenging assumptions in forecast review meetings
• Data ethics, confidentiality and responsible use of financial data

Workshop: Participants run a simulated business review, defend a recommended action and record agreed owners, milestones and risks.

Day 10: Finance analytics capstone and implementation

• End-to-end finance analytics workflow integration
• Performance-pack structure for monthly executive review
• Quality assurance checklist for models, dashboards and narrative
• Peer review using finance control and decision-usefulness criteria
• Stakeholder adoption planning for new reporting routines
• 90-day implementation roadmap and success measures
• Personal capability plan for continued analytics practice

Workshop: Participants present a completed finance performance analytics pack and produce a 90-day plan to implement one reporting improvement at work.

Tools & standards covered

Microsoft Excel, Microsoft Power BI, Microsoft SQL Server, Python

A typical training day

08:30 – 10:30	First session
10:30 – 10:45	Refreshment break
10:45 – 12:30	Second session
12:30 – 13:30	Lunch and networking
13:30 – 15:00	Third session
15:00 – 15:15	Refreshment break
15:15 – 16:30	Workshop and daily review

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions