Transforming Internal Audit: From Compliance to Value Driver
Internal Audit isn’t just about managing risk, it’s about creating value. Internal audit is helping ...
In the evolving landscape of internal audit, the buzz around AI and advanced analytics is deafening. Yet, for many audit professionals, the promise of smarter, more efficient audits remains just that – a promise, often mired in the complex reality of data. This last month a webinar titled "Mastering Data Quality for Audit Analytics Success" cut through the noise, offering a pragmatic roadmap for internal audit teams. Featuring internal audit afficionados Trent Russell, Founder of Greenskies Analytics, Robert Murray, Internal Audit Senior Manager at Mativ, and Arnaud Merlet, co-CEO of Supervizor, the session unpacked why data quality isn't just a check off item, but the cornerstone of successful audit analytics.
The session began by highlighting an eye-opening statistic shared at IIA’s GAM this spring: 80% of the time spent on analytics is dedicated to structuring and cleaning underlying data. The figure underscores the central challenge that makes data quality a pervasive and critical issue for internal audit teams.
The Peril of Poor Data: Amplifying Inaccuracies, Eroding Trust
As emphasized in the webinar, without reliable, accurate, and consistent data, any analytic efforts are going to result in flawed insights, and they're going to lead to incorrect conclusions and wasted resources and a lack of trust in the audit findings. The core message was clear: no matter how sophisticated the analytical techniques, if the data is poor, the inaccuracies will only be amplified, giving a false sense of understanding and leading to poor decision-making. Building a robust and scalable data foundation is non-negotiable for audit analytics success.
The Top Data Quality Hurdles: A Snapshot from the Audience
An initial polling question of the audience revealed their most pressing challenges. The top concern, voiced by nearly 50% of participants, was the business not providing access to required data sets. This highlighted a critical relational rather than technical issue. Other significant challenges included:
- Inconsistent data formats and definitions across disparate IT systems.
- Incomplete data sets.
- General data quality issues.
- Data privacy concerns.
These results underscored the pervasive nature of data quality challenges.
Cutting Through the Noise: Trent Russell on Data Literacy and Relationships
Trent Russell, known for his pragmatic approach to making analytics initiatives "actually work," immediately zeroed in on the non-technical aspects of data quality. He emphasized that many issues boil down to a data literacy problem. Trent shared an anecdote about a bank audit team struggling to connect two data sets because one had "branch ID; name of branch" and the other just "name of the branch." What seemed insurmountable to them was a simple fix with a couple of clicks in Excel for someone with higher data literacy.
Trent argued that auditors often "throw their hands up" if data isn't in "perfect condition." He highlighted that even audit teams with dedicated data engineers are primarily focused on getting data into "mint condition" before auditors can use it. This points to a fundamental gap in understanding basic data manipulation techniques within many audit teams.
Furthermore, addressing the top polling result, Trent stressed that the lack of access to data is primarily a relationship issue, not a technical one. He emphasized that DBAs and IT professionals inherently know how to provide data. The reluctance often stems from a negative perception of audit ("what are you going to do with this data? Audit us to death?"). Building strong relationships and demonstrating how audit can help the business (a strong business case) makes it "pretty hard for them to say no." Trent even shared his secret technique to build rapport involving pickleball—but you’ll have to watch the webinar to learn about this.
Trent advocated for a multi-level approach to relationships:
- CAE/Senior Management: Should own the top-level relationship with the CIO and be ready to "put their knee in their back" (figuratively, of course) to get data when lower-level attempts fail.
- Junior/Senior Manager Level: Needs relationships with the "hands-on-keyboard" DBAs and IT support. These individuals often understand the business rules behind the data better than the business users themselves, as they are involved in developing the applications. Trent shared his personal anecdote of getting a stalled GL report delivered in three minutes by simply walking over and asking the DBA directly.
Arnaud Merlet: The Persistent Problem and the Power of Source Data
Arnaud Merlet, with his extensive background at SAP and in enterprise software, echoed Trent's sentiments, asserting that the "data problem is never going away." He highlighted its increasing importance in the age of AI. Arnaud emphasized the need to partner with internal organizational functions responsible for data quality, viewing them as crucial allies in the fight for clean data. Their daily struggle to establish proper metadata and reference lists aligns directly with audit's needs.
Addressing the challenge of inconsistent data from major ERPs like SAP and Oracle, Arnaud explained that while SAP data tends to be "relatively well-structured" and "easier to put your arms around" than Oracle, the key is direct connection to the source system. He stressed that data often gets modified during extraction or when placed in data lakes, making it less reusable. Direct access minimizes these modifications, providing the cleanest possible data for analysis. Supervizor, for instance, focuses on normalizing this source data.
Beyond technical connections, Arnaud reinforced the "be friend with DBA" advice and strongly recommended a "lean fashion" approach: spending time with whoever is entering the data. He suggested auditors spend an hour sitting with data entry personnel to "figure out how that data gets entered" and "see the process happening." This direct observation provides a subtle business understanding that no system or AI can replicate, helping auditors truly grasp why data might be shaped in a particular way.
Robert Murray's Journey: Relationships, Prioritization, and Partnership
Robert Murray shared his practical experience from Mativ's internal audit analytics journey, acknowledging his journey was still underway--and being "around the fourth inning." He attributed their data quality success largely to their IT leadership and team, reiterating the critical importance of strong relationships and solid communication. Mativ's preference for a secure file transfer approach, rather than direct ERP connection (though the latter is a future goal), underscores the need for IT support. Robert highlighted their proactive relationship-building, including regular meetings between the CAE and IT leadership, and yes, Robert, too, credits playing pickleball as an important element.
Robert acknowledged that their biggest speed bump has been the limited availability of IT resources and competing priorities. To overcome this, he emphasized the importance of demonstrating a strong value proposition and generating meaningful, tangible ROI. He spoke to the importance of meeting with his financial leadership to review initial analytics results and findings, aiming to secure accounting-side validation and gain further momentum for IT support.
The Human Element: Building the Right Team and Culture
The webinar underscored that data quality and audit analytics success are not just about technology. They are fundamentally about people, relationships, and a shift in mindset.
- Data Literacy: A critical skill gap that needs addressing through training and a willingness to understand basic data manipulation.
- Relationships: Cultivating strong ties with IT, DBAs, and even data entry personnel is paramount for data access and understanding.
- Leadership Buy-in: The CAE's role in championing analytics, advocating for resources, and leading by example is indispensable. Trent pointed out that successful teams often have leaders who foster a culture of sharing "analytics wins," creating positive peer pressure and continuous learning.
The Crucial Role of Purpose-Built Technology
While human relationships and data literacy are foundational, the right technology is equally vital in tackling the task of data quality for audit analytics. Platforms similar to Supervizor's demonstrate how specialized solutions can dramatically reduce the 80% time spent on data preparation. By offering unique capabilities to structure and normalize disparate data sets from various ERPs and systems, these platforms automate much of the laborious data cleansing and transformation process. This not only frees up auditors from manual, repetitive tasks but also significantly enhances the reliability of the data entering the analytical phase. Such tools play a critical role in bridging the gap between raw, messy disparate data and the clean, consistent format required for meaningful audit insights, ensuring that even complex, multi-source data environments can be effectively leveraged.
Want to dive deeper into the strategies and actionable insights shared by these experts? ****We encourage you to watch the webinar on-demand! Discover practical tips, understand how Supervizor normalizes disparate data sets, and learn how your internal audit team can cut through the noise to achieve true audit analytics success. Watch the on-demand webinar here.
