I’m sure anyone who has been to a recent conference or training event has heard the terms “data analytics” or “big data” used excessively. 因为组织越来越依赖数据, and advances in technology such as Blockchain continue to make data more readily available there is an increasing interest in figuring out how to access and utilize this data effectively in internal audit. 关于数据分析有很多讨论, there hasn’t been much guidance on how to integrate data analytics into the Internal Audit Department (“IAD”). One of the questions we are frequently asked by our clients is “How do I get started?”

IADs that have successfully integrated data analytics into their processes employ many methods. 传统上, use of data analytics within the IAD focuses on auditing large data sets in an efficient and effective manner. One example of this testing strategy is establishing exception criteria and evaluating a full population of specific transactions using tools such as IDEA or ACL. Other more advanced data analytics users have ventured into leveraging visualization tools, 如表, 以及先进的分析工具, 比如R, to support audit results and integrate data visualization into final audit reports. Visualization enhances Stakeholders ability to grasp the audit results and eases the C-suite’s consumption of audit information. The most sophisticated data analytics users have begun to leverage predictive analytics, 比如IBM Watson, 预测问题发生的地方，并集中/计划审计活动. While it is great to hear how Internal Audit departments are leveraging data analytics, 它仍然把我们带回到“我如何开始”这个问题上?”

一旦预算和资源支持到位, the most important step in setting up a data analytics function within your IAD is ensuring an effective data governance process is in place. Policy and procedures should be in place detailing how data should be vetted prior to use and where data can be sourced. One of the common issues we see at our clients is data users accessing what is believed to be like-data from different data access points, 后来又判定它不能调和. The causes of variance may be due to data manipulations occurring during the data flow process, or the timing on when data is transferred or processed between data access points. The key to ensuring consistency in the use of data is strong data governance to establish data standards. Otherwise the saying, “garbage in, garbage out” will likely come into play.

建立数据标准后, it is time to select tools that will be leveraged and what resources will be responsible for owning the data analytics process. 正如前面所提到的, there are many data analytics tools available and each has its benefits depending on the goals (e.g. audit 执行, visualization, predictive analytics) of the data analytics function. 另外, resources should be assessed to determine if the Internal Audit department has bandwidth to staff the data analytics function in house, 是否需要外部招聘, 或者合作伙伴或外包伙伴是否有意义. 我们的经验是，联合采购模式是最有效的. 合作伙伴可以带来他们的观点, 经验, 和餐桌上的熟悉度, and work with internal resources who understand the business to develop a sustainable data analytics function that can ultimately be maintained internally with outside guidance as needed.

一旦确定了资源和工具, 是时候弄清楚如何在实践中使用数据分析了. A good starting point is to look at audits completed in the prior year and determine if there was an opportunity to leverage data analytics from a planning, 执行, 或报告的角度来看. Another good place to start is to look at what data is available and determine if there are any data sets ripe for data analytics. It is always advantageous to get a few “quick wins” that show the value data analytics brings to the audit process to gain momentum.

Another important step in building out a data analytics function is educating the business on Internal Audit’s data analytics capabilities. It is ideal to have the business thinking about how data analytics could be used to make the audit process more efficient and effective, simultaneously looking for opportunities to leverage analytics in their internal controls. Internal Audit departments who have successfully rolled out data analytics functions often work closely with the business to help develop continuous monitoring controls that the business executes and Internal Audit will ultimately audit.

如果成功实施, a strong data analytics function will lead to enhanced audit planning, 执行, 和报告. It will also strengthen relationships with the business who can turn to Internal Audit to consult them through leveraging data analytics in their day to day functions. 随着技术的不断变化, 大数据使用的增加, 以及通往开源数据的不可避免之路, every Internal Audit department should be considering how they will leverage data analytics going forward. 希望您现在已经掌握了一些入门技巧!