How Big Data Audit Can Help Your Company

Big data is one of the major buzzwords that revolve around the IT sector. However, today, it’s commonly used in a multitude of other sectors and industries. Utilizing big data can be immensely helpful in auditing as well. If you run or work in an auditing company–you should be vitally interested in this technology. If you’ve never heard about big data audit – don’t worry, you’re about to find out what this solution entails and how it can enhance your work.

In fact, every type of audit that’s based on big data can be significantly more accurate and holistic than the traditional one. That’s thanks to the nature of big data–it allows you to process, analyze, and draw insights from the vast amount of information your company possesses.

Big data is typically a mix of structured, semi-structured, and unstructured data. For instance, it can comprise:

• Internet clickstream data
• Marketing campaigns efficiency
• Web server logs
• Social media content and activity
• Text from customer emails and survey responses
• Mobile phone records
• Data captured by sensors connected to the Internet of Things (IoT)
• Market and social trends
• Competitors’ activity

And many more. It all depends on what questions you ask and what goals you set for yourself. The fact is, big data can change the way auditors think about auditing. Let’s find out how.

You may also find it interesting – big data trends 2020.

Big data audit – the future of auditing

If we had to describe big data auditing in just one sentence, we’d have to write that it’s all about integrating data from various sources. You see, that’s because traditional accounting data is mostly quantitative and structured. Big data doesn’t have such limitations, as it also includes unstructured and semi-structured data that, in many instances, offer much more detailed information. Auditors need to take that into account if they want to conduct more comprehensive audits. Let’s use a straightforward example–an e-commerce company.

Every online store deals with a vast amount of data–about customers, orders, inventory, returns and complaints, marketing, social media, shipping, and so on. Some of these pieces of data are structured (orders, inventory), and some unstructured (social media posts, emails from customers). Integrating these two types of data into one audit makes it much more accurate and comprehensive. Why?

Let’s take the shipment information. Traditional shipping documents are an example of structured data. We have proof of occurrence and Excel files. But if we add unstructured pieces of data (such as GPS data) to our mix, we have a more solid and holistic view of the company’s shipping processes. Similar rules apply to other aspects of doing business. The problem is that today, traditional auditing companies reluctantly take unstructured data into account.

To solve this issue, future auditors should be acquainted with big data. To make that possible, universities should design accounting courses with a focus on data skills. Audit firms should implement big data training into their skills development strategies. Slowly that happens even today. For example, the A7 standard of the Association to Advance Collegiate Schools of Business (AACSB) suggests that “accounting degree programs include learning experiences that develop skills and knowledge related to the integration of information technology in accounting and business”[1].

Big data enhances analytical procedures

First of all, big data opens the door to population-based audits. Analysis that’s conducted on the population level leaves very little room for risks and mistakes. In our e-commerce company example, each sales transaction can be compared to prior transactions, both from the same client and from other entities in the same period. This allows you to spot and identify anomalies in revenue data.

Big data also make fraud detection more effective. How is that possible? Thanks to generating connections between financial and non-financial data. For example, emails, phone calls, and other messages can be collected and analyzed to identify potential patterns or links with financial data.

Last but not least, big data can improve the auditing of external business relationships (EBRs). Big data allows auditing companies to gather data about the company’s EBRs, especially in risky areas, not necessarily acknowledged and captured by traditional accounting data. Why is that relevant? EBRs can be a potential source of the crisis. For instance, a company risks its reputation damage if their supplier would close their business and leave clients without necessary supplies.

What are the benefits of big data audit?

Thanks to utilizing big data, companies can make better, more accurate business decisions, significantly reduce their operational costs, gain much more insight both from the company itself and from the market, and keep up with evolving customer trends and expectations. Many of these elements play a crucial role in auditing.

By analyzing historical data, you can use both predictive and prescriptive analytics to estimate the likelihood of future outcomes. For auditors and auditing companies, it means the improved effectiveness of measuring and analyzing specific company operations. Moreover, auditors can use big data to expand their work scope and draw insights from vast amounts of data. With the help of artificial intelligence, data can be processed in much larger volumes and at a higher velocity to draw conclusions and predict possible outcomes. Big data also helps auditors to streamline the reporting process. As a result, auditing companies can assess business risks in time more accurately and conduct more relevant audits.

Last but not least, big data enables to automate many elements of the auditing process. Human error is a common reason why the auditing process is time-consuming and complicated. Big data makes it accessible to automate many manual and repetitive tasks. For instance, auditors can create various controls and verification procedures in advance in order to monitor how well a company is clinging to established guidelines and principles.

We now know the benefits of utilizing big data, but what is needed to make all possible?

Big data audit requirements and challenges

As you know from our e-commerce example, auditors face serious challenges, mostly due to data incompatibility. Granted, big data is unstructured and lacks a common identifier. This is why, first of all, your company has to have an adequate data management system and aggregation processes in place. That’s the nature of big data analytics–if you want to benefit from this technology, you have to have your data organized and clean. Only then big data analysts can do their job effectively. Partly, that’s the reason why large companies store their big data in data warehouses. These are sophisticated repositories where data is kept in a closely controlled state. To make it useful for business intelligence and big data analytics purposes.

All datasets that are to be used for the purpose of auditing need to be verified for their accuracy, timeliness, and capacity. In this way, auditors can work on reliable and high-quality information. This is particularly important when it comes to auditing because, in many instances, decisions regarding compliance, risk, and investments are typically made after audit reports are done. That’s why they have to be as accurate and thorough as possible.

However, experts also indicate other obstacles. The first one is information overload. Many studies[2] have shown that too much information hinders the ability to process data. And the second problem is cost-related. Undoubtedly, software that can efficiently handle the big data volume and analyze it will be costly.

But the juice is definitely worth the squeeze, as the benefits of big data in auditing are unquestioned. This begs the question:

How can your company implement big data auditing?

Unfortunately, implementing big data auditing is not going to happen overnight. As you already know from the previous part of this article, some adjustments and changes are necessary. Above all, you have to prepare your data in an adequate way. However, it doesn’t end here. Adapting big data auditing requires much more preparation. You should take into account that two-step process:

Step 1: know your end goal

A big data audit is frequently based on specific AI-fueled algorithms and statistical models. In order to design them appropriately, you have to establish your end goal. That’s why your auditing team should be involved in the entire process to make sure that devised algorithms meet your company’s needs.

Step 2: outsource AI workforce and build required algorithms

Typically, auditing companies do not have big data specialists and AI professionals in their internal teams. And that’s perfectly fine because today you simply don’t have to! You can outsource such a team and ask them to build necessary algorithms that will enhance audit. And this is where Addepto comes into play because this is exactly what we offer.

Our specialists work for companies that need to utilize AI-related solutions but don’t have the necessary workforce or experience. If you want to enhance your audits and draw more accurate conclusions–just give us a call, and let’s see what we can do together!

If you run an auditing company or just want to improve your internal audits–be sure that big data analytics and AI-based algorithms can make your work much more effective and accurate. We are keen to show you all of the benefits of big data audits!

References

[1] AACSB International. Eligibility Procedures and Accreditation Standards for Accounting Accreditation. Apr 8, 2013. URL: https://www.aacsb.edu/-/media/aacsb/docs/accreditation/accounting/standards-and-tables/accounting-standards-2013-update.ashx?la=en&hash=88DDA25A00B242165742153AD3AD8ACF6BFEABF5. Accessed Aug 7, 2020.

[2] Jiali (Jenna) Tang. Big Data in Business Analytics: Implications for the Audit Profession. June 2017. URL: https://www.cpajournal.com/2017/06/26/big-data-business-analytics-implications-audit-profession/. Accessed Aug 7, 2020.