It is estimated that by the year 2025, 463 exabytes of data will be created each day globally. To put that into perspective, in three years from now, the equivalent of 4,63 billion gigabytes will be generated every day!
The present-day business already generates and collects data at an unbelievable pace, which needs to be analysed, audited and reported on within prescribed deadlines. As a result, the demand for data analytics has increased to such an extent that the South African Institute of Chartered Accountants (SAICA) has even revised their competency framework (CA2025) to include exposure to data analysis. However, despite the demand and benefits of data analytics, its implementation is still relatively low. Making such data easily accessible and understandable to end-users remains one of the major barriers in turning data analytics into real audit value. With this in mind, the era of self-service analytics is born.
Self-service is no new concept and has been around for some time. From banks to airports and even restaurants, self-service has facilitated an easier way to serve more customers faster and easier. On a global scale, fast-tracked by the COVID-19 pandemic, self-service has seen significant growth and has become an important part of the consumer experience. Within the business environment, business intelligence had also been introduced to provide users access to data as well as enable them to analyse the required data to make more informed and insightful decisions.
The main objective of an audit is to ensure that the risk of material misstatement is reduced to an acceptable level. To achieve this, data analysis can play a key role in identifying and addressing risks. However, a conventional auditor may not have the necessary skills to query a database and perform an analysis to obtain audit evidence. This is where self-service analytics provides significant value.
A key feature of self-service analytics is to enable non-technical users to perform analyses without being reliant on IT personnel. Predefined and tested analytical procedures, aligned to the audit methodology, are developed by analysts to be used easily and instantly by an auditor. Large volumes of data which would otherwise be unable to work with or access, is made available through this process. The auditor now has the ability to perform these complex procedures against large datasets to obtain the necessary results or insights required.
Implementing self-service analytics equips audit teams with the latest technology and tools available to improve audit quality as well as realise efficiency. In a traditional setting, an end-user might request IT or an analyst to query the data and generate reports or Excel spreadsheets with the data they requested. Using self-service analytics, auditors are no longer reliant on IT professionals or analysts and thus lead times are significantly reduced. This means auditors now have timeous data and insightful results to tailor their audit approach accordingly. The use of self-service analytics also assists in consistency and quality across audits in an organisation. For example, selecting a sample using the same self-service analytic across all audits means that a standard and consistent process is followed for sample selection across all audits.
With the ever-changing needs of consumers, business must adapt to remain profitable. As business evolves, so too must the auditor to remain relevant. Through the idea of encouraging more emphasis on analytics, together with self-service analytics, low-code/no-code analytics has also been introduced. This empowers the so-called non-traditional analyst to perform complex analytics without the necessary programming knowledge.
Analytical commands are dragged and dropped (often referred to as visual programming) to create fully functioning scripts that can be run and automated. Using these innovative features allows the end-user to create procedures that are as simple or complex as required, thereby effectively using data analysis as a tool to audit big data.
The implementation of self-service analytics brings about new challenges. Security, data integrity, regulatory requirements and users’ demands are just a few that will continue to be obstacles in its path. However, introducing the concept of data analytics requires a paradigm shift in the way we think about data. Data is not just a requirement or consequence of a business, but the underlying story of a business, is told through its data.
Ultimately, for data analytics to be successful, it must overcome organisational and cultural barriers to reach its potential of delivering value to both the auditor and their clients.
Salim Mohamed, Data Analyst, RSM South Africa