Internal Audit Data Analytics for Beginners

Data mining. Data transformation. These are all synonyms for data analytics. 但是数据分析究竟意味着什么，内部审计人员如何在他们的工作中利用数据分析?

Data analytics is defined as, “对原始数据进行检验，并从中得出结论的科学……”¹ .内部审计师必须依靠大量的信息来完成他们的工作, it can be overwhelming to determine the best available data (if they are available at all); how to filter data to the best usable format; and how to use data to identify trends, inconsistencies, potential fraud or process improvements to add value to the organization. Essentially, it must be asked, “内部审计如何最好地利用和操纵数据来帮助组织做出决策??”

With an increase on the reliance of data analytics in engagement fieldwork, scoping, and planning, 确定从哪里开始以及如何确定从哪些项目开始是具有挑战性的. 审计人员可以采取两个阶段和相关步骤来开始利用数据分析的旅程.

Phase 1: The Setup

To begin incorporating data analytics into audit work, 审核员应该把实施分成3个步骤，而不是一次解决所有问题. Phasing the setup into 3 parts makes implementation more manageable. 审计人员可能会意识到，他们在数据分析过程中比预期走得更远.

审计人员可能会意识到，他们在数据分析过程中比预期走得更远.

Step 1: Assessment
首先，审核员应该完成审核和数据分析评估的审核分数. 有一些工具可用于评估审计部门当前的数据和分析性能. 一些工具提供了在哪里集中工作的洞察力，并帮助确定必要的资源. Additionally, some tools assess responses provided against benchmarks. As an output, 提供一份报告，帮助审核员了解他们的团队当前的执行情况, short- and long-term goals, and resources needed to meet strategy and business needs.

Step 2: Embedding
Based on the audit team’s existing knowledge of data analytics, trainings should be identified and conducted to increase knowledge. From there, the use of analytics can be embedded. 审核员可以从建立一个问题库或问询库开始，将其纳入他们的审计程序. Without understanding the desired outcome from gathering data, it is challenging to gain much from data analytics.

可以开发概念验证，以确定组织中数据分析可以提供最大回报的地方, whether it is accounts payable, journal entry evaluation, access management, vendor management or help desk ticket analysis.

In addition, audit teams should adapt and reinforce. 可以调整当前的审计流程，以便将数据分析纳入审计的任何阶段. Those efforts should be reinforced for each audit.

Step 3: Utilize
Fortunately, most audit teams do not have to start from the beginning. 数据分析示例库等资源可用于收集信息和灵感. 这些库是旨在帮助用户理解如何在审计中使用数据分析的实践集合, consulting and continuous monitoring.

此外，审计团队应该确定他们希望使用哪些工具来收集数据分析. 看起来像审计命令语言(ACL)这样复杂的软件程序² or Tableau³ are required to perform data analytics, however, more commonly available applications such as Microsoft Excel and PowerBI⁴ can also help teams achieve their data analytics goals.

Step 4: Data Extraction
Before data analytics can be performed, 为确保数据可靠并实现目标，应采取7个步骤:

1. Identify the source of the data. 数据可能来自各种来源，例如数据仓库、应用程序或电子表格. 由谁来提取数据以及从哪里提取数据取决于谁来管理数据——是it部门吗, the business owner or a vendor?
2. Identify the desired outcome of the data. For example, if using analytics for accounts payable analysis, 人们希望这些数据告诉他们是否有重复的供应商编号, names or addresses.
3. Determine what the results should look like. 结果可以显示在图表、电子表格或段落格式的Word文档中. 结果的外观应该根据所报告的内容和使用这些信息的人来定制.
4. Are the data available and accessible? One may know what type of information they want to complete their analysis, 但是必须确定这些信息是现成的，还是需要其他团队的额外资源和时间.
5. Know which tools to use at each phase of work. 根据需要的信息，准备数据获取和分析工具. There are various tools available, 但重要的是要了解哪种工具可以及时提供结果, easily absorbable manner.
6. Ensure that tools and data are repeatedly tested. This provides assurance that the outputs meet the needs.
7. Validate the output with organizational operations. If it looks good, then it is time to begin.

Phase 2: The Analytics

一旦阶段1完成，审计团队就可以更深入地研究数据分析的“方法”.

Step 1: Analysis
Auditors can combine their training, the embedding of data analytics, their proof-of-concept models and adaptation methods into a single audit. 他们应该从管理层和业务领域获得反馈，了解哪些工作有效，哪些工作不有效，以确定分析是否满足预期.

Once all parties involved agree that the data provided valuable insight, audit teams should record the steps taken and make the process repeatable.

Step 2: Reporting
Once the data have been gathered, 应该创建一个仪表板，与业务所有者和管理层共享分析结果. 仪表板和报告可以帮助讲述数据分析的故事，并教育团队，使数据分析在组织的所有领域都根深蒂固. 一个这样的例子是日记账测试，以确定日记账是否在正常工作时间之外进行, 如果手工更改或输入，或者在非经常性分录上使用了相同的备忘. 这种类型的分析可以帮助识别和防止潜在的欺诈. Additionally, 业务领域可以接受分析方面的培训，并使用它来通知持续的监视过程.

Conclusion

虽然收集数据分析可能看起来很神秘或困难，但事实并非如此. By utilizing the available tools, starting small, and setting goals for gathering data analytics, 分析可以合并到每次审计中，结果可以与管理层共享. 审计团队可以成为数据分析的教育者和啦啦队长，每次只进行一次审计. 数据分析的结果有可能减少人工操作, shorten the time needed to complete a task, help identify trends to monitor for potential fraud, 在持续监控中增加澳门赌场官方下载所有权，突出内部审计的价值.

Endnotes

¹ Stedman, C.; “Data Analytics (DA),” TechTarget, May 2023
² Diligent, “Automate Processes and Deliver the Insights That Drive Strategic Change”
³ Tableau, “Welcome to Tableau”
⁴ Microsoft, “Turn Your Data Into Immediate Impact”

Deneen Richard, CISA, CRISC, CRMA

Is the assistant vice president and chief internal auditor at LWCC. She has more than 20 years of audit experience including operational, financial, and IT audit assurance and advisory services; enterprise risk management; and enterprise policy. Additionally, her experience spans multiple disciplines such as project management, Service Organization Control (SOC) 1 and SOC 2, Model Audit Rule (MAR), data analytics, and management.