The purpose of Data Analytics is to go into the company and assess the data of said company.
Data analytics is the science of analysing raw data in order to make conclusions about that information and identify key patterns.
Data analytic techniques enables one to take raw data and uncover patterns to extract valuable insights from it. It can be used in audit planning as well as in the performing of actual procedures. It helps identify and assess areas of risk which might only be identified through the analysis of the background tables and data. This is done through analysing the data and identifying patterns, correlations, and fluctuations in the populations.
As the world evolves, develops and changes, many companies are merging together forming even greater companies, which results in many clients having a large amount of data. Oftentimes, this data requires special software in order to withstand the magnitude of the company’s data. Furthermore, transactions are becoming more and more digital, leaving behind a digital footprint. Data Analytics is used to help merge this data and obtain a big picture understanding of the transactions in a company, as well as analyse the digital footprint which may pinpoint key areas for auditors to focus on.
Due to the magnitude of transactions that Data Analytics can work through, data from prior years are able to be retrieved and included in the analysis. This has proven to greatly assist audits in terms of determining the accurate ageing of debtors, creditors, and inventory. With this, it can be used to identify potential or obsolete inventory, slow-moving inventory, long outstanding debtors and creditors, as well as the recoverable debtors.
Data analytics assists audits in many ways such as providing reconciliations of debtors, creditors, inventory, revenue, purchases ledgers and table to the general ledger, rebuilding trial balances from the raw data, performing sampling, valuations, testing on various data and simplifying engagements by involving automated tests into the audit workflow.
The main benefit of using Data Analytics is to enhance the quality of the audit. The use of Data Analytics in audit provides better coverage and assurance, enabling risk focused audits and better understanding of the clients.
How to get started with Audit Data Analytics
- Determine what information you want to collect, define your questions.
- Set a timeframe for Data Collection.
- Determine your data collection method, set the priorities.
- Communicate with the client, learn about their data standardisation.
- Collect and analyse the data and implement your findings.
- Review the various data analytics procedures available to the audit teams and establish what would be beneficial to the audit.
- Obtain a detailed understanding of the client systems so that areas where transactions are automated can be identified and data analytics can be performed thereon.
Data analytics is changing auditing by enabling auditors to work with 100% of the transactions within a population of data. Auditors can quickly see the patterns and connections in vast amounts of data, present the findings graphically, and pinpoint high-risk areas for further audit testing with the assistance of data analytics.
Data Analysts and Auditors need to have a clear communication line in order to establish the most efficient and beneficial ways in which data analytics can be used as it is the future. This will greatly improve the quality of audits and result in more specific and focused audits whereby high risk areas can be better addressed. This may ultimately assist in providing even more detailed and beneficial feedback to the clients.
Audit Supervisor, RSM South Africa