Today, data is indispensable in business as well as technology. This has given rise to data analytics which helps in acquiring insights to strategic decisions and analyses the information. However, despite the potential of analytics, due to the genesis of the technology, some of its applications remain untouched. With this, the use of analytics in internal auditing remains in its early stages. However, with growing demand, data analytics is entrenching its foot in the internal audit sector. As per a recent report, three out of four departments utilise this technique as part of their internal audit process, seeking efficiencies, enhancements, and insights buried within internal workflows. The main reason behind the adoption is, increasing pressure to deliver advisory support and valuable insights across the organisation and analytics is proved to be the ultimate solution for the challenges.
Effective data analytics offers greater value, elevates performance, and increases the credibility of an internal audit with the stakeholders. Analytics breaks down a huge volume of data and rebuilds it to form information clusters which can be analysed at ease. It also helps in transforming internal audits via automating processes and offers a higher level of operational assurance. In addition, this insights-driven approach offers benefits such as:
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Cheaper audits
Connecting internal auditors directly to the process via data with risk analytics & data visualisation enables exploratory analytics to drive a focused audit. Which implies that shifting to automated routines over manual saves money as well as time.
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Faster audits
Enhancing data access and developing significant insights before starting with the actual auditing process lets you focus on the utmost important data and avoids merely confirming the obvious or transaction risks in real time.
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Better audits
Combination of internal and external data adds granularity, richness and better understanding of risks. Comparative analysis and benchmarks deliver impactful results to the stakeholders.
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Develops innovation
Offering rich data combination and utilising advanced generation improves, automates and reports analysis process.
However, while making the most of data analytics, internal auditors face challenges including misuse or misinterpretation of data, inaccurate or misleading results, conflicts in independence; and challenges around data privacy & security. To address these issues, a QA framework or strong test must be incorporated.
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Data quality standards & assessments - For e.g. direct access to validated data sources (data warehouse or ERP applications), Service Level Agreements (SLAs), header row counts and data-quality profiling.
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Output validation - To identify the output answers for business problem, consistency output readability.
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Code verification - Includes logical correctness, accuracy and formats.
In recent times, data analytics framework is transforming internal audit by enabling auditors to prioritise & investigate high-value areas. Established businesses are developing data-quality assessments and standards at macro and micro levels. The purpose is to identify inaccurate data and measure impact on analytics-driven processes.