Abstract data connectivity pattern
ComplianceTháng Mười Một 12, 2021

Triển khai hiệu quả chiến lược phân tích dữ liệu kiểm toán nội bộ hiệu quả

Trong vài năm trở lại đây, việc sử dụng hoạt động phân tích dữ liệu trong Kiểm toán nội bộ đang ngày càng được nhân rộng. Chúng ta cũng thấy được những bước tiến trong việc thu thập và lưu trữ dữ liệu, cùng với sự phát triển tinh xảo của các kỹ thuật phân tích dữ liệu, nghĩa là ngày càng nhiều chức năng kiểm toán nội bộ được xây dựng khả năng này. Tuy nhiên, triển khai khả năng phân tích dữ liệu hiệu quả không chỉ liên quan đến công nghệ và dữ liệu bạn thu thập. Việc triển khai phân tích dữ liệu thành công đòi hỏi một sự thay đổi toàn diện trong chức năng kiểm toán nội bộ bao gồm việc phát triển các kỹ năng của kiểm toán viên nội bộ, điều chỉnh mô hình hoạt động kiểm toán nội bộ và thay đổi tổng thể về phong cách và cách tiếp cận trong cách chúng ta kiểm toán.

Bài viết này giới thiệu 10 Mẹo hữu hiệu hàng đầu, đem lại kinh nghiệm thực tế về cách phát triển và triển khai chiến lược phân tích dữ liệu kiểm toán nội bộ một cách hiệu quả

Nhận một bản sao của toàn bộ bài báo

Tip #6 – Ensure accountability through effective performance management 

It is important that you ensure people are held accountable for pushing your use of data analytics forward.  If you are to achieve wide-ranging use of analytics it is important to integrate reward and recognition of this into your performance management system.  One organization I worked with ensured everyone, right through the internal audit function, had a clear objective around the use of data analytics in audit work and the function as a whole had targets for the percentage of audit work that deployed simple analytics and more complex audit work that was reviewed and signed off by the QA function.  Whilst this is relatively crude it emphasized the point that this mattered and everyone should be looking for ways in which they could make the use of data analytics endemic in the function’s activities.  It had considerable success and now data analytics is integrated into all that the function does and is delivering considerable results in terms of audit findings, continuous business monitoring, and the deployment of audit developed analytic capabilities into the first and second line for them to use as part of their day-to-day control management activity.

Tip #7 - Widely communicate and celebrate your successes

There are many ways in which you can celebrate your achievements and every organization will have its own culture and approach to sharing success.  At one organization I worked for we prepared a six-monthly set of case studies of audit successes.  We presented at our six-monthly audit leadership event a handful of these, that had been voted as our proudest moments by the whole team, to the chief executive, chairman, and members of the audit committee as 3–5-minute vignettes of success.  A great evening for the team, but also a chance for the audit committee and executives to see a collective view of the value we were adding and the energy with which we brought to our work.  These case studies were not all data analytics-driven, but what we did see over time was an increasing number of them becoming analytics-led as momentum and support for the data analytics initiative took hold.

Tip #8 - Embed consideration of data analytics into every part of your audit methodology

It is important that you truly make using data analytics an ‘opt out’ not ‘opt in’ part of your audit methodology.  Auditors should, at key stages of the audit process (tollgates), have to show why data analytics cannot be used rather than how they can. This opt-out approach will focus the mind of the internal audit team on looking for data analytics opportunities at all stages and increase the proportion of audits using analytics.

Tip #9 - Build your capability in a sustainable manner 

It is very easy, audit by audit, to throw together a spreadsheet to analyse some data.  However, this won’t be reusable in the future.  Encourage your auditors when developing analytics, whether that be through Excel, in TeamMate, or using other software such as PowerBi and Python, to build with reuse in mind.  This will take a bit longer, as will require setting up in a way that someone can pick it up in the future, but the benefits will pay off for the function longer term.  Consider giving time at the end of each audit for the team to clean up the analytics and store it in a well-organized data analytics library.  Central data analytics teams can really help here if you have the scale to do this.  They can take the lead in ensuring this systematization is carried out when analytics are used and also develop pre-configured testing scripts and ensure the libraries set up are well maintained and useful.

Tip #10 - A multidisciplinary (sometimes called Hybrid) approach is most successful. 

This is all about how you set up your operating model to deliver your data analytics.  Some larger functions have dedicated well-resourced teams to deliver analytics, but increasingly functions are taking a hybrid approach where a small group of specialists work alongside front-line auditors who have been trained and encouraged to use analytics.  This blend allows capability built to be widespread and penetration of analytics work to be deeper into more audit activity than if the work is done in a separate central unit. 


There is no silver bullet for the successful development and implementation of a data analytics strategy, but hopefully, the series of tips outlined will be a useful catalyst to your work as you consider the conditions that need to be right for you to achieve momentum around your data analytics work.  Good luck!

Jonathan Chapman
Chuyên gia tư vấn về rủi ro và chuyển đổi kiểm toán nội bộ
Jonathan Chapman là chuyên gia về chiến lược chức năng kiểm toán nội bộ và quản lý sự thay đổi.
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