As opposed to using traditional and primitive audit techniques, a move towards big data and data analytical tools offers numerous advantages, namely - improved audit quality, goodwill with client and increased effectiveness.
Due to copious amounts of data and limitation of time, traditional audits took roots in sampling of data. A shift towards data analytics allows auditors to analyse entire populations of data and not stick to samples. The shortcoming of sampling is that the auditor can never really be sure of the audit opinion they are giving the client and therefore they often look for ways to limit their liability. By handing over their responsibilities to softwares, the audit team can start its work much sooner and get the mandatory items off their lists. The softwares will do the analyzing bit and draw substantive evidence and identify relationships. This will enable the team to identify the critical high risk areas early on. This means the audit team can construct an audit strategy which is client specific and spend more time on the high risk areas. These factors contribute to an audit of an even greater quality.
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With a greater influx of data, auditors have knowledge of areas that were beyond their scope. Due to limitations of time. These can now cover financial and non-financial areas such as the procurement cycle. This enables auditors to have an improved idea of the functioning of business and identify critical issues. They can then include these recommendations in the management letter and establish lasting long term relationship with the client. This improved knowledge and recommendations bring in clients to the advisory division of the firm.
Auditors will be able to analyse copious amount of data and that too at a faster rate. This means they can identify the risky areas and make an audit plan in accordance with their findings. Furthermore, they will be able to wind up audits faster which will lead to more timely reporting. Since audited reports will be readily available, this will facilitate management and board of directors in the decision making process.
A great deal of small and medium sized practitioners do not have the monetary resources to invest in big data softwares. The same is true for small and medium sized clients that do not have the appropriate softwares for integration. One possible solution is, the audit firm can outsource its analytical services to third parties, However, this will compromise on the quality of work and will also put the client’s confidential data at risk.
The challenges to application of big data and data analytics are mostly related to implementation and integration. The primary problem is with accessibility of data and the integration of systems. Not only are softwares are expensive to build, they are also not very competent with other softwares. In most cases, within a company different softwares exist. For example, a number of companies use Maximo for procurement purpose and Oracle for financial reporting. This means the audit firm’s software should be accommodating of numerous types of softwares.
Another issue is that of cybersecurity. Companies invest heavily to protect their data and build systems that will compromise on security of it. They will be hesitant to integrate their systems with the audit firm’s network. On top of that, the audit firm’s network is pretty extensive and have incorporated local firms to expand their practices across the globe. This further increases the risk of cyber theft.
The final issue is from an implementation perspective. There will be an increased expectation to develop new skills. These skills will be in addition to the basic accounting skills and training they are already going through, The entire education chain will have to undergo reforms. There will be increased pressure on audit firms to conduct extensive trainings. In additions these trainings need to be consistent across their global network to ensure the same quality of work.