Internal auditors are rightly recognized for integrity, sound judgement, analytical thinking, attention to detail and influencing skills.
But if you’re an internal auditor, you know that the time-consuming work of collecting data, sampling transactions, sifting through paperwork and reams of communication, is less visible.
How can you, as an internal auditor, meet the cognitive challenge of sustaining forensic, granular attention to detail and zooming out to provide big picture strategic recommendations to your leadership team?
In this article, I’ve outlined some of the latest data analysis approaches that can help you with this challenge and deliver more value to your boardroom.
Be aware that data analytics isn’t an instant fix. It takes clear strategic planning, time, and effort to harness the full potential. But it’s worth it.
The problem with ever increasing volumes of data.
As organizations become digitized, larger volumes of structured and unstructured data must be analyzed. This work becomes time consuming, challenging, and onerous.
The easy response is to find new tools. There’s a fantastic range of low-cost scripting, low code, no code, visualization, and reporting tools that can handle huge datasets quickly and comprehensively.
But there’s a learning curve, especially for teams with varying levels of knowledge, technical skill, and resource. And regardless of how good each tool may be, stitching tools together often leads to disjointed and inefficient workflows.
Using an assortment of tools may still mean workload continues to increase with data volumes, and before too long, you’re running faster just to keep up.
Why Data Analytics?
Rather than responding tactically by doing more of the same with new tools, try thinking strategically about the opportunities presented by increased volumes of data.
A strategically focused data analytics approach delivers three levels of benefits: efficiency gains, deeper insights, and total coverage.
- Simply reducing large amounts of manual effort will allow you to focus on higher level work.
- Moreover, a well thought through data analysis approach is guaranteed to increase accuracy.
- Finally, more internal audit time can be spent on being trusted advisors rather than inspectors and assessors.
There’s more: advanced data analysis techniques can cut through complexity and rapidly uncover deep, valuable insights in all areas of your organization. It will make manual testing feel as outdated as traveling on horseback.
And the most powerful benefit of data analytics is that it means you can analyze entire populations of transactions rather than just samples. This complete coverage means internal audit can quickly detect control gaps that traditional sampling approaches may miss.
To get the best from data analysis, it’s important to understand some of the standard and emerging data analysis techniques. Read on to find out more.
Benchmark Data Analysis Techniques
Thanks to its potential to handle entire datasets, data analytics gives you 20/20 vision into the soul of the organization, rather than the incomplete representations that traditional sampling techniques provide.
Data analytics techniques allow you to:
- Check the quality and completeness of data you are auditing, by pinpointing missing values, invalid categorical values, and outlier numerical values.
- Easily audit systems and processes to check if they work as intended by independently implementing business rules and calculations across whole datasets.
- Trace flows of all audited data, end-to-end across multiple systems to check for anomalies.
- Assess accuracy of audit reports by re-creating reports, using original data sources and business rules.
Advanced Data Analysis Techniques
Techniques such as continuous monitoring, machine learning, and natural language processing are becoming common.
Seeing your organization’s activities continuously with unprecedented clarity and granularity is a bit like switching the lights on for the very first time. You’ll get deeper insights and be able to respond much faster to risks and anomalies.
- Continuous monitoring dashboards can be automated to source more data, in real time or near-real time to visualize key risk indicators and allow faster more informed responses.
- Machine learning algorithms can autonomously find complex data patterns and relationships. You can use supervised machine learning algorithms to detect and predict fraudulent activities and anomalous activities. Unsupervised machine learning can discover patterns, groupings, anomalies, and new risks or trends that help management teams anticipate issues long before they can be perceived by normal methods.
- Natural language processing can extract insights from unstructured text data, such as sentiment, or topics which is invaluable for an organization to respond fast.
Vision for Internal Audit
By switching from manual testing methods to data analytics, you can unlock internal audit’s potential to provide real strategic value.
With continuous, complete visibility of entire datasets and unmatched insights, internal audit can continuously audit risks rather than periodically sampling transactions.
And because you spend less time and effort on inspections and assessments, you can focus on providing trusted, captivating advice that move your stakeholders to meaningful action.
But it won’t happen overnight!
Despite the clear benefits of transforming data analysis capabilities, it needs strategic planning, time, and effort to harness its full potential.
Skills gaps, poor data quality, and cultural resistance can create barriers, but the good news is these are all addressable. As with anything new, it’s best to progress at a measured pace, and ensure each step is completed before moving onto the next step.
By committing, you can evolve from evaluating the past to anticipating the future, and meaningfully contribute at the highest levels of strategic decision making.
C&F is a technology firm with deep experience in internal audit, and data analysis. Despite the abundance of data, companies don’t always leverage data as well as they could. This may be down to siloed teamwork, conflicting reports based on different sources, and ineffective communication between internal audit, IT, business divisions and leadership.
As digital transformation specialists, we support companies in leveraging data to become data driven. We work with IT teams and leadership teams during their digitalization journey. See an overview of our data capabilities and internal audit technology capabilities or talk to us to find out more.