In the era of digitalisation and ever-evolving technology, businesses possess massive data generated by them every day from different sources, which can be leveraged as huge growth opportunities for advancement. Businesses need to evolve in order to keep up in this dynamic world.
In the current age, information is wealth, and “Data Analytics” is the key that transforms data into information.
In simple words, Data Analytics is the collection, transformation, and organization of data in order to draw meaningful conclusions, make predictions, and drive informed decision-making.
The IAASB defines data analytics for audit as “The science and art of discovering and analysing patterns, deviations and inconsistencies, and extracting other useful information in the data underlying or related to the subject matter of an audit through analysis, modelling, and visualisation for the purpose of planning and performing the audit.”
Role of Data Analytics and Big Data:
Deeper Understanding of Business:
Data Analytics aids in-depth understanding of the client’s business through a detailed analysis of the client’s data. It also provides a better understanding of trends and patterns by the use of visual output like dashboard displays, which make it easier to find anomalies and outliers.
Diagnostic Analytics uses past data and events to uncover the reasons behind such events. This helps the business to find the source of problems and help in solving them. A problem well understood is a problem half-solved.
Identification of Risks and Frauds:
This in-depth understanding plays a major role in identifying risks associated with the client, thereby enabling the auditors to devote more focus to these areas.
Descriptive Analytics is a major player in business intelligence and data analytics. It unlocks valuable information to the question of “What happened?” It helps in identifying risks specific to clients and in uncovering fraud. It also helps in identifying the reasons for fraud which will help in preventing further occurrence of such frauds.
Improves Efficiency of Audit:
Data Analytics makes audits more time-efficient by analysing routine data, which would otherwise be time-consuming to auditors and cause a loss of focus on important areas of the audit.
The technology and processes of data analytics increase consistency in group audits when used identically by all the auditors concerned. It also provides the group auditor with specialised tools for component audits and for testing.
It accelerates the speed and efficiency of audits by processing gigantic amounts of data and provides analysis to base the conclusions for audits.
Sampling Risks:
Sampling refers to the method of testing a part of the population to draw conclusions about the whole. Audit Sampling is a popular method in the world of audit due to time and resource constraints.
However, sampling does not always provide sufficient and appropriate evidence about the population and there is the existence of sampling risks. Data analytics provides an effective solution by enabling 100% testing in auditing, which will eliminate such sampling risks.
New and Improved Audit Techniques:
There are different ways to perform audits, and there is no one method that is applicable to all situations. There are also methods that are best suited for a situation but may not be feasible given the various constraints in an audit.
Data Analytics makes data manipulation much easier for the auditor to perform tasks like sensitivity analysis on management assumptions. It also possesses the ability to interrogate all data and test segregation of duties which boosts fraud deduction during audit.
Value Additions to Audit and Client:
Data Analytics provides various benefits to the auditor and the client by providing value addition to audit and client management through the valuable information generated.
Predictive analytics uses machine learning, advanced statistical algorithms and other techniques in order to forecast events and outcomes of the future. This kind of analysis is key to chartering the turbulent environment of the business world. It makes one proactive rather than just reacting to the changing circumstances.
Big data analytics will provide valuable insights into the shifting trends, which will help in identifying future risks and opportunities.
Leveraging Big Data
Big data refers to a collection of unstructured, semi-structured, and structured data gathered by companies from different sources, which can be mined for information and used for machine learning projects, predictive modelling, and other advanced data analytics applications.
Big data analytics refers to the use of advanced techniques of analysis on such large and diverse data sets to generate useful information that otherwise would not make much sense.
Both internal and external auditors can leverage big data to unravel a host of detailed insights, which will grant a better understanding of the business environment, identification of risks and frauds, improve the quality and quantity of audits, and, on the other hand, also provide value addition to clients.
In the current era and the ones to come, the information a business needs keeps on evolving to higher and newer levels. The required information and insights have expanded beyond the traditional transactional data available in accounting books to unstructured, voluminous data from emails, social media, and more, which are generally referred to as big data. The inferences gained from such data are important for risk assessment, identifying opportunities, and much more.
Conclusion
Data Analytics is an ever-growing technology of the future. It possesses the ability to convert any kind of raw data into structured information which is understandable to all. Auditors can also leverage this to create audit programmes that are best suited for the client. With data analytics, one can make faster and quality decisions which is a deciding factor of a successful business. Businesses can benefit a lot by leveraging big data to create forecasting models and enhance business intelligence.
Analytics helps internal auditors act as a strategic advisor to the client while controlling costs or even reducing them. Analysing data to produce actionable information is a key challenge and opportunity for clients. Any business will witness a manifold increase in quality, quantity, and efficiency by adopting data analytics. Properly leveraging big data and analytics is key to generating valuable information and proper utilization of such information will be the definer of front-running companies.
FAQ regarding Data Analytics in Auditing:
Why is data analytics important in auditing?
Data analytics is crucial in auditing as it allows auditors to analyse large volumes of data quickly and efficiently, identify potential risks, detect fraud, and provide a more comprehensive and accurate assessment of financial information. It enhances the audit process by providing a more thorough and data-driven approach.
What types of data are used in data analytics for auditing purposes?
Auditors use various types of data, including financial transactions, customer data, inventory records, and other relevant information. The data can be structured (like databases and spreadsheets) or unstructured (like emails and social media) and may come from internal or external sources.
What are some common data analytics techniques used in auditing?
Common data analytics techniques in auditing include trend analysis, regression analysis, predictive modelling, anomaly detection, clustering, and data visualization. These techniques help auditors discover insights, patterns, and outliers in the data.
How does data analytics help in risk assessment during an audit?
Data analytics helps auditors assess risk by analysing historical data and identifying patterns or anomalies that may indicate potential risks. This allows auditors to focus on high-risk areas, prioritize their efforts, and tailor audit procedures to address specific concerns.
How can auditors ensure the integrity and quality of the data used in analytics?
Auditors need to establish data governance processes to ensure the integrity and quality of the data. This involves validating data accuracy, completeness, and consistency. Data cleansing and validation procedures should be implemented to address any issues with the data.
Author
Athaliah Nikitha Babu
