Implementing Data Analytics: Critical Success Factors
The biennial UN/INTOSAI Symposia provide opportunities for capacity building for Supreme Audit Institutions (SAIs) through exchange of subject-specific experiences and information in all relevant fields of public sector auditing. The 24th Symposium, held in Vienna, Austria, between May 31 and June 2, 2017, focused on digitalisation, open data, data mining, and their relevance and implications for SAIs’ audit work and for enhancing their contributions to the follow-up and review of the Sustainable Development Goals (SDGs). As I analysed the conclusions and recommendations reached by the participants, recommendation 3 of the Symposium caught my attention. Participants recommended that it was necessary for SAIs to develop internal strategies in the fields of digitisation, open data and data mining, including capacity building of staff, providing infrastructural resources and developing new audit methodologies, tools and techniques. This is a welcome recommendation since the public sector is also at the forefront in responding to technological advances through automating its processes and delivering services to citizens and businesses online.
As governments digitise, there are large volumes of data that are being produced. This presents opportunities and challenges for public sector auditors regarding accessing and using the data to obtain audit evidence. Data analytics therefore will be an essential tool, technique and method to the public sector auditor to deploy during audits. Data analytics can enable auditors to identify financial reporting, operational business and compliance risks, and better tailor their audit approach to deliver more targeted risk-based audits. However, as SAIs embrace data analytics during their audits, I believe that the following three factors, if not well addressed, can impact negatively on the ability of SAIs to leverage the audit opportunities and efficiencies presented by data analytics.
First, creating a buy-in among the auditors and managers is essential. As observed by the INTOSAI CBC in their October 2018 publication, changing approaches to audit will be embraced by some, rejected by others and will have an enthusiastic acceptance by others. Auditors need to understand the what, why and how of analytics, and have a chance to express their concerns. For line managers, they should be trained how to review the results of data analytics and be given an opportunity to understand the implication of analytics for the audit approach.
Secondly, data analytics should be incorporated in the audit methodology. The purpose of data analytics is to supplement and improve auditing and therefore should not be implemented as a stand-alone tool, method or technique. Also, the auditor’s experience, professional scepticism and judgement will still be required to conclude the audit.
Thirdly, a SAI should set up a specialist unit or a pool of data analytics champions. Whereas analytics should be every auditor’s tool, champions can undertake preliminary data mining, processing and act as data analytics advisors for the audit staff.
In Kenya, we are already walking the talk, ingraining data analytics in audit planning stages and as tools for creating audit evidence during the audit. To aid this, we have conducted bottom-up training of 400 auditors in the basics of data analytics.
As we await the guidelines from the INTOSAI Working Group on Big Data, I re-emphasise the Symposium’s recommendation that member-SAIs continue sharing knowledge and best practices in the field of digitalisation, open data and data mining. This will enable us to make meaningful follow-up on implementation of audit recommendations and SDGs.