Accounting Data Analysis and Reconciliation with Excel and Power BI Course

Introduction:

Accounting data analysis, also known as data analytics for accountants, is the practice of collecting, cleaning, summarising and visualising accounting data, such as ledgers, trial balances and receivables ageing, so that finance decisions rest on evidence rather than manual checks. This 1-week course is for accountants, bookkeepers, finance officers, payables and receivables staff and junior auditors, and its final deliverable is a working monthly finance dashboard built from a sample accounting dataset. It is taught at a beginner to intermediate, practitioner level through hands-on labs in Microsoft Excel, Power Query and Power BI Desktop, with every exercise based on realistic accounting records.

Course Objectives:

• Define the main data concepts accountants meet, including structured tables, data types, account codes and the difference between transactional and master data
• Apply Power Query steps to clean, combine and refresh ledger, payables and receivables extracts instead of repeating manual edits each month
• Distinguish PivotTables from formula-based summaries and select the right one for trial balance, ageing and expense analysis
• Interpret ratio and variance results, separating price, volume and timing effects behind budget-to-actual differences
• Compare exception reports and dashboards, and apply Power BI basics to build visuals that finance managers can read at a glance
• Justify reconciliation and anomaly findings, such as duplicate payments and unmatched items, with evidence drawn from the data

Target Audience:

• Staff who record and close the general ledger each month, deciding which balances and movements need analysis before sign-off
• Professionals who process supplier invoices and payments, judging which duplicates, exceptions and ageing items to investigate first
• Staff who manage customer billing and collections, deciding which overdue accounts and payment patterns to escalate
• Finance officers who prepare management and budget reports, choosing which ratios, variances and visuals best explain results
• Junior auditors and accountants who test balances and reconciliations, judging which data samples and exceptions need follow-up
• Finance team members starting to work with data, deciding which spreadsheet tasks to automate and which to keep manual

Course Outline:

Day 1: Data Concepts for Finance: How Accounting Records Become Analysable Data

• Descriptive vs Diagnostic Analytics: What Accountants Can Answer With Each Type
• Chart of Accounts Structure: Using Account Codes as Keys for Analysis
• Excel Tables and Data Types: Setting Up Ledger Extracts for Reliable Analysis
• ERP and Accounting System Exports: CSV, Text and Report Extract Formats
• Finance Data Readiness Check: Assessing Completeness, Accuracy and Timeliness of Records

Day 2: Preparing and Cleaning Accounting Data With Power Query

• Power Query Editor Basics: Applied Steps, Data Types and Refreshable Queries
• Manual Cleaning vs Power Query Transformation: Repeatability for Month-End Files
• Splitting, Trimming and Fixing Dates: Cleaning Messy Bank and Ledger Exports
• Append vs Merge Queries: Combining Monthly Files and Matching Supplier Master Data
• Unpivoting Budget Spreadsheets: Turning Cross-Tab Layouts Into Analysable Tables

Day 3: Summarising and Analysing Finance Data With PivotTables, Ratios and Variances

• PivotTables vs SUMIFS Formulas: Choosing a Method for Trial Balance Summaries
• Receivables and Payables Ageing Analysis: Building Ageing Buckets With PivotTables
• Descriptive Statistics for Finance: Averages, Medians, Ranges and Transaction Distributions
• Financial Ratio Analysis: Liquidity, Efficiency and Profitability Ratios From Ledger Data
• Budget-to-Actual Variance Analysis: Separating Price, Volume and Timing Differences

Day 4: Reconciliations, Anomaly Spotting and Visual Reporting Pitfalls

• Data-Driven Reconciliations: Matching Bank, Subledger and General Ledger Records in Excel
• Duplicate Payment Checks: Using Conditional Formatting and COUNTIFS to Flag Repeats
• Outliers vs Errors: Spotting Unusual Journal Amounts, Weekend Postings and Round Sums
• Power BI Desktop Basics: Importing Queries, Simple Relationships and Core Visuals
• Misleading Finance Charts: Axis Scaling, Chart Choice and Colour Pitfalls to Avoid

Day 5: Lab: Building a Monthly Finance Dashboard From an Accounting Dataset

• Lab Dataset Preparation: Applying Power Query Steps to a Sample Ledger Extract
• Lab Ratio and Variance Page: Applying Earlier Measures to Power BI Visuals
• Lab Exception Report: Applying Reconciliation and Duplicate Checks to the Dataset
• KPI Cards vs Detailed Tables: Choosing Visuals for Finance Managers and Reviewers
• Monthly Finance Dashboard: Final Build, Peer Review and Refresh Checklist

Skills You Will Gain:

• Power Query Data Cleaning
• PivotTable Summarisation
• Receivables and Payables Ageing Analysis
• Budget Variance Analysis
• Financial Ratio Interpretation
• Data-Driven Reconciliation
• Duplicate Payment Detection
• Power BI Dashboard Design

Why Attend This Course:

• From retyping and reformatting the same ledger exports every month to refreshing one Power Query that cleans them in a single step
• From scrolling through thousands of transactions to summarising balances, ageing and expenses with PivotTables and ratios
• From ticking reconciliations line by line to matching records and flagging duplicates and unusual entries with data checks
• From static spreadsheet reports to a working monthly finance dashboard in Power BI that managers can read and question

Conclusion:

Accounting data analysis turns raw ledger, payables and receivables records into clear summaries, checks and visuals that support month-end and management decisions. The course makes three distinctions clear: manual spreadsheet cleaning versus repeatable Power Query steps, PivotTable summaries versus formula-based reports, and genuine errors versus legitimate outliers in transaction data. It is the right choice for accountants, bookkeepers, finance officers and junior auditors who are new to data analytics and want a beginner to intermediate, practical start, with SQL, coding and advanced modelling left for a later stage of development.

Frequently Asked Questions (FAQ):

What do delegates need to know before a data analytics for accountants course?

Delegates need everyday Excel skills, such as entering formulas and sorting or filtering lists, plus a working knowledge of basic bookkeeping and the general ledger. No experience of Power Query, Power BI, SQL or coding is expected; each tool is introduced from first steps with accounting examples.

How does data analytics for accountants differ from a general data analytics course?

It works only with accounting records, such as trial balances, ageing reports and bank statements, and applies analytics to finance tasks like reconciliations and variance reporting. General courses use business or marketing data and often move into SQL or coding, which belong to a more advanced level.

Is Excel or Power BI better for data analytics for accountants?

Neither replaces the other. Excel with Power Query and PivotTables suits ad hoc analysis, reconciliations and working papers, while Power BI suits shared, refreshable dashboards for managers. Most finance teams prepare data in Power Query, which both tools share, and choose the output tool by audience.

What do delegates take back to work from data analytics for accountants training?

Delegates take back a working monthly finance dashboard built from a sample accounting dataset, the Power Query steps behind it and a refresh checklist. These can be adapted to their own ledger, payables and receivables data to shorten month-end reporting and reconciliation work.