How-to: How should auditors begin learning Data Analytics?
In an era of massive and increasingly complex financial transactions, traditional random audits may no longer be sufficient to provide complete assurance. Data Analytics is, therefore, not just an “optional” but a “necessary skill” for modern auditors, enabling them to conduct in-depth audits, detect anomalies, and most importantly create greater strategic value for organizations and clients.
This article is a complete roadmap to help all auditors begin their journey in Data Analytics on a solid and correct direction.
Step 1: Understand Data Fundamentals
Why is this important? Financial data isn’t just numbers in ledgers, it comes in a wide variety of formats from Excel files and SQL databases to log files from ERP systems. If auditors don’t understand the structure and origin of the data, they can’t fully utilize it for analysis.
How to start:
– Understand data types: Know the difference between structured data (data with a clear structure, such as accounting tables in Excel and unstructured data (data without a fixed structure, such as contract content, PDF reports, or emails).
– Study the business’s data flow: Understand the path of data in your client’s business from invoicing and recording transactions to receiving payments, so you know where to extract the data for analysis.
Step 2: Master Your Spreadsheet Tools
You don’t need to start with expensive tools or complex programming. Familiar programs like Microsoft Excel and Google Sheets are the most powerful starting point.
Examples of use:
– PivotTable: Use to summarize monthly sales by employee, group expenses by department, or count transaction frequency to identify anomalies.
– Conditional Formatting: Automatically highlight suspicious or high-risk items, such as adding a red bar to expenses exceeding Baht 1,000,000.
– Power Query: A great tool for merging data files from multiple sources (e.g., a 12-month sales report) and data cleaning in a single click.
Step 3: Learn Specialized Audit Tools
As data becomes larger and more complex, using tools designed specifically for auditing can significantly save time and increase accuracy.
Why is this important? Auditing requires tools that can handle a large number of transactions and have functions ready for auditing-specific tasks, such as finding duplicate payments, checking document sequences, or performing complex reconciliations.
Examples of tools:
– IDEA / ACL (Galvanize): Standard software in the auditing industry. It can directly retrieve data from client accounting systems and has ready-made functions for automated anomaly detection.
– Power BI / Tableau: While not direct audit tools, they are extremely powerful for creating dashboards to provide a real-time overview of risks. This allows auditors and management to quickly see abnormal trends in specific areas or risks concentrated in certain regions.
Step 4: Level Up with Basic Code
When data volumes exceed the capabilities of Excel or ready-made tools, knowing basic coding allows auditors to “control the data” more independently and flexibly.
Why is this important? Coding allows you to tailor analysis to the complex challenges of each case and work with large datasets efficiently. You don’t need to be a programmer, just knowing the basics can significantly speed up your work.
Languages to start with:
– SQL: This language is for directly querying databases, such as retrieving data on “customers who purchased more than 5 times in a single month” or “checking all accounting entries that occurred after business hours.”
– Python (with Pandas and Matplotlib libraries): A popular language for data cleaning, statistical analysis, and automated visualization. Ideal for managing chaotic and large-scale datasets.
Step 5: Practice with Real-World Cases
Theory is meaningless without practice. Applying knowledge to real-world data and situations is crucial.
Why is this important? Working with real data allows you to see problems on the ground and truly understand the value of Data Analytics.
Practice approaches:
– Accounts Payable Testing: Use Data Analytics to find duplicate payments, investigate suppliers with unusually high balances, or identify items paid without invoices.
– Test of Controls: Use data to verify that all purchase orders have been fully approved or to check if invoice numbering is sequential (missing numbers may indicate a risk).
– Substantive Testing: Analyze trends in various expense categories compared to the previous year. If any expense area has increased significantly, it’s a point that warrants further in-depth investigation.
Conclusion
The journey to becoming a data-driven auditor may seem challenging, but the key is to start with small, steady steps. This includes understanding the fundamentals of data, mastering familiar tools, and working on real cases.
These skills will not only increase efficiency and accuracy but will transform your role from “auditor” to an indispensable “strategic consultant” for organizations in the digital age.
Start today to become the auditor of the future!
Author: Pichchapa Avikunprasert
Dharmniti Auditing Company Limited
