Big data and data analytics
Recent advances in the ways that data is captured, processed, stored and communicated have fundamentally transformed, not just business processes, but also the ways in which we do business.
Big Data and data analytics are key issues which relate directly to many roles carried out by accountants in business and in practice.
The ACA syllabus has evolved over many years to reflect these changes and continues to do so with the 2017 and 2018 syllabi. Some highlights include:
Audit and Assurance
In the context of auditing, data analytics concerns the process of extracting, transforming, validating and analysing large volumes of data to make judgements and form conclusions. Data analytics therefore attempts to use improvements in data management tools and digital technology to enhance audit quality and audit efficiency.
Examples of how data analytics can be used in audit include:
- planning and risk assessment
- identifying patterns and inconsistencies in data sets
- identifying and measuring exceptions, inconsistencies, anomalies and outliers in data sets
- population testing
- sophisticated and wide-ranging query and filter options
- predictive and modelling tools to help the auditor assess the reasonableness of management representations
- visual representation of data to enhance understanding (eg graphs, dashboards, bar charts, pie charts and cluster diagrams).
The new requirements for data analytics in the 2017 syllabus for A&A include the impact of data analytics on the extent of tests of controls and of substantive procedures. It also includes benefits and limitations of analytical procedures, including data analytics.
In the Corporate Reporting learning materials for 2017, there is increased content on Big Data and data analytics. This includes issues of audit quality as well as the problems of data capture, extraction, validation and transformation. The issue of risk analytics is also addressed.
Business and Management
While advances in IT has been enabling for business and management it also comes with risks and the need for information risk management.
In impacting on business processes, the use of Big Data and Big Data analytics has become a key source of competitive advantage. Businesses have been able to access new sources and types of information about external factors (eg markets, customers, competitors, industry factors) and internal processes (eg employee productivity, manufacturing process efficiency, use of website, capacity planning).
Businesses have also been able to bring together multiple data sources to present a more complete and integrated picture of datasets relating to businesses, processes and individuals. This has improved prediction capability, and enhanced the quality of evidenced-based decision making.
The Business Strategy syllabus and learning materials for 2017 have an increased content relating to Big Data and data analytics. This includes an assessment organisational and operational capabilities, information systems capabilities. This also includes the analysis of large data sets and the ability of an entity to analyse outputs to assess performance, position and processes.
The Strategic Business Management syllabus and learning materials for 2017 includes the analysis of data relating to markets, industry and performance, including the capture and analysis of Big Data. This requires an understanding of the nature and complexities of Big Data and of how that data can add value to decision making to give competitive advantage. At a higher level, it also requires an understanding of the enabling impact of strategic management information on the entity (eg for the borderless business).