Using Big Data In Auditing And Analytics

Written by: Ken Lynch of Reciprocity Labs

Behind any pile of data is a story. Ideally, the data provides a well-outlined plot of the strengths, weaknesses, risks, and opportunities that your business faces. Unless your business can analyze this data, the story it tells remains hidden behind facts and figures.

Lucky for modern-day businesses, the conventional approach for auditing and data analytics has provided a baseline for firms to leverage the power of big data. Using these strategies, organizations can predict market patterns, investment opportunities, and business risks- all which influence the decision-making process.

Sadly, the precision at which these conventional strategies can predict the future isn’t enough. The good thing is that big data looks to fill the gaps that conventional approaches have, and revolutionize the entire auditing and analytics industry. As long as you can leverage big data, auditing for clients will be a walk in the park.

Read on to learn about the opportunities that big data presents your business and common challenges to its adoption:

The Perks Of Big Data

1.   Enhanced Audit Quality

Conventionally, auditors had to sample their client’s data to come up with useful insights. Though sampling has been effective for some time, it doesn’t provide enough precision. You typically have to ignore data anomalies a well as outliers, which can often help identify risks before they occur. Big data analytics systems will help you to analyze a wider scope of data, if not all the necessary data, to come up with more precise conclusions.

Also, it will allow you to analyze your client’s data early in the auditing process, making it easy to streamline the rest of the process. You can pick metrics for analysis early, identify problems, and know the kind of audit evidence to look for.

2.   Improving The Auditing Frequency

Other than being costly, data analysis can be quite time-consuming, especially if you lack the necessary analytics tools. This is why firms choose to analyze their data after every fiscal quarter or year- even though they know that frequent analysis will yield better results. Luckily big data streamlines the data analytics process, reducing auditing lead times.

As a result, businesses can enjoy more audits at a reduced cost. Not only does this continuous testing revolutionize risk identification, but it also paves the way for accurate control assessments as well as timely insights.

3.   Improved Client Service

As outlined above, big data helps shorten the auditing process as well as improve the results. Such factors can be quite helpful in the decision-making process by clients. Even better, this new approach to data analytics ensures that you can communicate time-sensitive threats and opportunities early enough, making the role of auditors in the business growth scene even more appreciated.

How Big Data Is Transforming The Audit

Auditors work in the interest of all stakeholders. They help with the quality assurance of businesses, from a financial to a security standpoint. They deliver insights that improve reporting, identify business risks, and even offer insights on tailored fields.

While conventional technology had played a significant role in supporting the task of the auditor, it limited their power. With big data and developments in the analytics field, everything changes for you as an auditor. You can now focus on an entire population of audit-relevant data instead of trying to fixate your judgment on a mere sample. It even allows you to tailor your auditing journey to deliver the right results.  

Algorithms For Data Analysis Make Big Data Even More Useful

Present-day auditing applications that are based on big data are designed with a series of algorithms. This provides a platform for both running checks for completeness and formatting analysis. At the very least, such algorithms help to streamlines a formerly manual process.

The applications will offer you, as an auditor, a dashboard-based information pool from which you can draw conclusions. It also becomes easy to check for anomalies and outliers, as well as pay attention to any red flags early. By combining them with the traditional approach to analysis and auditing, the extent to which such algorithms can change the business world is huge.

Auditors And Analysts Can Shift Their Focus Towards Risks

Ideally, data collection, processing, and checking are one of the most time-consuming tasks for auditors. These algorithms help reduce the role that you can play in the initial stages of data collection as well as the processing and checking the data. As the application does it all for you, you can shift your focus on the intricate details of auditing.

This allows for better performance benchmarking and the use of resources. The biggest benefit is that auditing and analysis oversight is enhanced. However, it will be essential to train people on the skills needed to use big data and related tools in auditing and analytics.

Threats To The Integration Of Big Data

There is a reason why big data hasn’t yet gained enough traction in the auditing field. The threats that slow down its integration are many, but they aren’t insurmountable. Here are some of them:

1.   Barriers To Capturing Company Data

As long as you can access client data, it can be pretty easy to use big data analytics in the auditing process. You could draw conclusions and even identify threats in a fraction of the time it would have taken you to do so if you were using conventional means. However, the fact that you have to access company data brings in the form of complexity.

Businesses spend years layering security tools to reduce the data security risks their data faces. To gain access to this data, auditors have to rely on a time-consuming approval process, with some businesses being reluctant in providing the data completely. Instead, they claim that they will be putting their data at risk, which is understandable.

2.   Data Extraction Isn’t An Auditing Competency

Businesses typically use a number of accounting systems to achieve their accounting needs. Since data extraction is not a core competency for auditors, and most businesses lack this competency, it adds a layer of complexity.

Ideally, you might have to go through a lot of back and forth between you and the organization you are auditing to capture the necessary data. Without enough insights into how data extraction works, this might seem like an uphill task.  

While conventional audits focused on the general ledger, you will need to obtain information from the sub-ledgers to truly enjoy the benefits of big data. Sadly, this also increases the complexity of integrating big data into auditing.

3.   Finding The Balance Between Auditor Judgment And Analytics

It is pretty easy to use descriptive analytics to pinpoint threats and opportunities that lie in the shadows. For instance, if a situation of fraud has been plaguing a business, you can easily point it out to your clients. Sadly, it is a little bit tougher to produce audit evidence trying to respond to the identified risks.

Big data mainly relies on the black box nature of analytics, whereby rules and algorithms are needed to transform the collected data into reports and visualizations. Once the data gets to this stage, auditors need to find a balance between relying on these analytics and using their judgment to make the necessary conclusions.

4.   Auditor Training Is Yet To Change

As outlined above, big data completely revolutionizes the auditing job. It requires you to have both analytics and IT skills as an auditor. This will allow you to know the kind of questions to ask the collected data and know how to use the analytical output to produce quality audit evidence. Simply put, the new skills make deriving business insights and drawing conclusions pretty easy.

However, the modern-day training for auditors hasn’t yet caught up with the demand for big data. The learning and development programs at the college level are mostly based on the conventional approach to auditing. This means that an auditor that comes from these levels will have a hard time adjusting to the new requirements.

Ideally, getting rid of this problem requires a ground-up approach to auditing training. Learning institutions need to incorporate the necessary big data skills into their training to arm auditors with the right skills.

The Changes That Big Data Brings Along

1.   Auditing And Analytics Standards Have To Adapt

Since time immemorial, the role of auditors has been governed by a specific set of standards. These standards have been governing what you can and cannot do as an auditor. They have control over how you communicate with clients and what tools you can use. However, they limit the use of big data tools in auditing and analytics.

The new tools disrupt data management, workflow management, as well as data interrogation. Without changes in these standards, some of these tools might never be used as effectively as they should be used. Ideally, the regulatory bodies that make such standards need to update them to pave the way for big data and related tools.

2.   Skillsets Need To Change

Ignorance can never be an excuse in the face of disruption. You need to be well versed with the latest analytics skills to remain competitive in the world of big data. Ideally, it starts at the college level. Sadly, a single issue has made it tough for the necessary skillsets in a world run by big data to gain traction.

Having not taught students about the recent developments in the different fields, learning institutions choose not to test such areas. On the other and, students fail to study those specific areas since they know they won’t be tested. The good thing is that institutions are slowly updating their courses to incorporate ad hoc changes, and online platforms are offering courses that can help arm you with these skills.

Regardless of whether you are working or a student, you need to access courses that can help you sharpen your skills for a world centered on big data. While training on the job is possible, go beyond this. The only way to be effectively competitive is to immerse yourself in the most recent developments. The good thing is that this will be straightforward as long as you have the conventional auditing practices as your baseline.

3.   Audits And Analysis Need To Dig Deeper

Big data provides more insights than before. It allows auditors to dig deeper into their client’s data environments and identify anomalies and risks that they previously couldn’t. Even better, it makes it easy to turn analytics and audits into a continuous process, offering businesses real-time insights throughout the year.

As an auditor, you need to have the necessary applications and tools to achieve both of these improvements. You should also change the way you describe your offering to clients to ensure that they understand that audit and analytics quality is better than before.

4.   Security Needs To Be Improved

Big data uses both structured and unstructured data to come up with business insights. Some of this data can range from communications with clients to financial data. The bad thing is that there is a looming threat of this data falling in the hands of cybercriminals. If this happens, not only could be the future of businesses in jeopardy, but their relationships with their clients and other stakeholders could also be at risk.

Ideally, businesses need to invest in security tools that fit right into their data environments without making big data analytics tough. On the other hand, you- as an auditor- should assess the tools you use for auditing with a lot of criticism. The last thing any auditor wants is to compromise the security of their client’s data when doing their job. This is why training in the latest developments in a world run by big data is essential.

Big data promises a lot of opportunities in the world of audits and analytics- from increasing analytics efficiency to improving the decision-making process. As long as the challenges behind the adoption of big data in analytics and auditing are eliminated, it will be much easier for businesses to grow and tackle risks. Be sure to up-skill and keep up with trends in the big data world to take advantage of it.

About the Author

Ken Lynch is an enterprise software startup veteran, who has always been fascinated about what drives workers to work and how to make work more engaging. Ken founded Reciprocity to pursue just that. He has propelled Reciprocity’s success with this mission-based goal of engaging employees with the governance, risk, and compliance goals of their company in order to create more socially minded corporate citizens. Ken earned his BS in Computer Science and Electrical Engineering from MIT.  Learn more at ReciprocityLabs.com.