Editor's note: The following is an excerpt from the November 2018 report Perspectives on the Finance Function Journey from the International Federation of Accountants' Professional Accountants in Business Committee (PAIB). The committee is chaired by Charles Tilley, OBE, FCMA, CGMA, who is also executive chairman of the CGMA Research Foundation. Tilley is the former CEO of CIMA.
Data analytics is a broad term encompassing many diverse techniques and processes that deliver insights from financial and nonfinancial data to improve decision-making on matters that are critical to an organisation's success. Data analytics has become omnipresent in the way organisations manage their businesses.
Accountants working in either the finance function or internal audit are using data analytics to help organisations uncover valuable insights within their financial reporting processes and to better manage risk, as well as to more broadly improve strategic and operational decisions for revenue growth and cost reduction.
As it becomes increasingly ubiquitous, accountants need to identify how they can develop their roles to contribute to data analytics activities that involve data to enhance business decisions and improve risk management and internal control.
Reflections from the PAIB Committee on their experiences of data analytics in different settings
Data analytics for finance teams in small and medium-sized enterprises (SMEs)
- It is important to apply the professional accounting skillset to ensure data governance and to help build the foundation for data analytics that will drive decision-making. In an SME where there may only be a CFO and a few accounting staff, it is often not possible to employ experts such as data scientists. This makes managing financial and nonfinancial data across the organisation an opportunity to create additional value for the CFO and finance team.
- Need to consider what data are already available about the organisation and its operations and what would be useful and collectable and would provide insights into business problems to help identify opportunities.
- Need to invest in a system to store and generate robust and reliable data. Without a solid foundation, it is difficult to apply business intelligence tools to exploit the data. Underlying data sets need to be reliable and credible, otherwise investment in analytical tools is ineffective.
- A focus on data governance and controls is key, and the finance team can develop the necessary policies, procedures, and data taxonomies to ensure confidence in data.
Data analytics in an internal audit function of a large financial organisation
- The automation of large parts of the audit plan using analytics has made the internal audit function more efficient and effective. Up to 75% of internal audit activity now relies on data analytics, which involves automated day-to-day testing.
- The audit committee and management have experienced greater value from internal audit, as have other operations that benefit from the insights delivered. Use cases include:
- Financial planning and analysis (FP&A) — ensuring a business unit's FP&A is consistent with independent analysis and allowing the identification of unexpected anomalies and variances.
- Software to monitor communications by employees to identify potential prohibited sales.
- Reviewing financial transaction information based on a full population of data combined with data-visualisation tools is a powerful way to show trends and underlying areas of risk.
- Developing a data analytics "IQ" among all internal audit staff has been a priority. This involves ensuring staff are connected to the wider analytics community to acquire new knowledge and experience. Internal audit has become a resource centre in data analytics. The data analytics knowledge from IA is being transferred to the business, and representatives from internal audit serve as data analytics champions.
- Internal audit provides a data analytics programme framework that enhances data governance to drive consistent data analytics methods, processes, documentation standards, quality control, and talent management.
Using data analytics in the finance function
- Together with automation, analytics enables better risk understanding and reporting across thousands of data points that form the basis of P&L reporting.
- Data analytics has improved collaboration with external auditors using their software and tools to detect patterns and trends. The relationship and dialogue between the finance function and external auditors have significantly improved, leading to better outcomes.
- Providing leadership to the organisation in data governance, building on the natural skillset of the finance function. Ensuring appropriate data ownership and policies and providing the necessary structure to ensure data robustness. Data analytics capability is gradually being built in-house within the finance function.
- Supporting the business analytics process beyond the finance function. This is also being done in conjunction with other technology projects such as using blockchain to enhance, for example, commodity trading.
Opportunities and threats arising from data analytics
- Efficient and real-time information allows more time for action and value-added activities.
- Greater job satisfaction and contribution to business strategy, risk management, and value creation for finance staff.
- Better co-operation with external auditors and more efficient audits ultimately lead to enhanced audit quality.
- Enhancing professional scepticism improves the ability to ask the right questions and build on existing analytical ability.
- Better prioritisation, more targeted in approach to support the organisation.
- Greater transparency and accountability.
- Greater insights into the business and the potential to enhance decisions and insights.
- At the transactional level, the accountant can become marginalised and less relevant, or even obsolete.
- Data security and privacy need to be carefully managed within the organisation as well as with the external auditor. IT and cyber risk is an increasingly important topic for discussion between audit firms and their clients, and one that carries significant reputational risk. Audit firms process large volumes of client data and are therefore vulnerable to data breaches or external cyberattacks.
- Resistance to training and upskilling has to be overcome. The finance function needs to achieve real impact from analytics initiatives. To do so requires applying finance and accounting skillsets and knowledge of the business to help the organisations identify and prioritise their problems, based on which will create the highest value when solved.
- Resource constraints to implementing necessary processes, systems, and tools particularly in an SME context.
- Risk of error in underlying data that can lead to misleading results. Poor quality data in other parts of the organisation can also be a challenge to the usability of data.
- There is a need to work effectively in collaboration with other experts such as data engineers, data architects, and data-visualisation experts to ensure the finance function is relevant and not marginalised in data analytics.
The Perspectives on the Finance Function Journey report can be found at ifac.org.