Data analytics can improve the quality of reporting on financial and operational systems and controls, and help an audit committee better understand risks and opportunities.
Improved reporting quality results from the ability to monitor entire data sets of transactions and accounts. Capture and analysis of all data on, say, a population (rather than sample) of receipts, payments, deliveries or warranty claims can identify revenue and cash leaks (such as unauthorized discounts taken by customers on invoices or bogus warranty claims), as well as anomalies and outliers that point to process weaknesses or control breakdowns.
Better understanding of risks and opportunities results from insights that management and internal audit can provide when disparate data sets are aggregated and analyzed. Factors driving inventory shrinkage; employee turnover; customer loyalty; and credit, fraud and other risks can be isolated, analyzed and better understood and managed.
The following factors can help an audit committee make the case for data analytics in accounting/finance, internal audit and other key areas:
- Both the cost and the complexity of related tools have decreased in recent years, making them far more accessible and practical.
- Monitoring or analysis of populations of transactions or accounts can enable management to leverage existing data to provide enhanced reporting and analysis.
- Monitoring or analysis of populations of transactions or accounts can also enable internal audit to scope and execute audits more efficiently, and provide higher levels of assurance to the audit committee and the board.
- Internal audit can, with audit committee support, foster adoption of data analytics in the organization by demonstrating benefits in pilot programs that identify process inefficiencies, cash leaks and missing revenue.
Also, while cleaning, extracting and aggregating data present challenges, the audit committee can provide leadership for the organization to address those challenges — and point out that waiting for perfect data is not a practical approach.
Regarding the latter point, costs, time constraints and lack of skills often deter organizations from adopting data analytics. However, these factors should be weighed against the benefits. The costs and time involved in adopting new processes and tools are often offset by efficiencies achieved. Lack of skills can be addressed through hiring, co-sourcing and outsourcing, or by developing guest auditor, rotational or training programs.
Grant Thornton sees private companies’ data analytics capabilities as being about where cyberrisk management stood five or six years ago. As was the case with cyberrisk management, companies that move to adopt data analytics before their peers can gain experience, benefits and marketplace advantages before less forward-thinking competitors.