10 Essential keys to get the most out of audit data analytics. Part 1

Most Internal Audit groups are relatively low on the data analytics maturity scale. According to CaseWare Analytics’s Audit Trends, most auditors are using data analysis software for ad hoc use or manual testing in their audits. Less than 30% of respondents indicated they are using the technology for continuous auditing and continuous monitoring for more complete and current coverage of internal controls. In the years ahead, expect to see more and more audit groups putting these more advanced capabilities to use.

In this article, we will cover the first five key principles for departments that are planning their first steps or seeking to better integrate data analytics.

Key Areas of Focus  Leading to Full Adoption

1.Raise Awareness / Shift the Mindset

According to the Protiviti survey, “Fewer than one in five organizations report that their audit committee is highly interested in the internal audit group’s innovation and transformation activities.”

According to the global consulting firm, it is “incumbent on CAEs to convey the internal audit department’s commitment to innovation and transformation to audit committee members through effective and efficient information-sharing practices and persuasive presentations.”

In light of this, CAEs are well-advised to:

  • Win buy-in and support for data analytics’ deployment from key stakeholders.
  • Demonstrate the capabilities of data analytics in ways that can be readily understood by non-financial or audit professionals.
  • Raise awareness not only of the benefits of investment and forward movement but of the budget, resource and other constraints faced by internal audit. 

2. Establish a shared audit data analytics vision and roadmap

The strategy should include:

  • A business case
  • A plan for investments in training, tools and skilled resources
  • Expected returns on investment
  • Identification of first-deployment areas in the organization 
  • Measures of success
  • Staff utilization targets

3. Explore the Landscape

Getting the most out of data analytics depends, in our experience, on active engagement with other internal audit groups and professionals, and not just at the CAE level. In this way, auditors can see what others are doing to get the most out of this technology, learn the pitfalls to avoid, and find out what works best. CAEs should be championing the effort to find opportunities to expand their group’s understanding of some of the more sophisticated capabilities audit data analytics offers, including continuous monitoring.

4. Will it scale? 

We all know this is one of the most common and sometimes misplaced questions in business, but it’s important to ask here. Internal audit departments are well-advised to effect a staged transition and to budget several years for full implementation. This allows for lessons learned at one stage to be incorporated into the approach for the next. 

 Start with a pilot audit that’s low in complexity but high in value. Many groups start with a process in procurement. The data’s all there, generally of good quality, and the use of analytics will generate returns on investment that are tangible and, just as important, visible.  Carefully selected and well-planned pilot programs will go far in demonstrating the capabilities of analytics, and the merits of continued investment.

5. Choose Your Tools Carefully

There are a variety of software options available for data analytics. Criteria should include:

  • Ease of onboarding — Ensure the training support is there to get staff up and running with the tool quickly.
  • Ease of use — No one in Internal Audit should have to become a data scientist to use one of these programs.
  • Talking system-to-system — Ensure a wide range of data formats are accessible for the tool you select, and easily imported for analysis.
  • Ongoing support — Confirm that your vendor has your back if and when audit challenges arise.
  • Library of procedures — Verify that the tool provides access to common business processes.
  • Data capacity — Remember that your tool must be able to grow as you grow and handle ever-larger data sets and complex applications.
  • Reporting — Make certain you can use the tool to effectively communicate back to the business and stakeholders using visualization tools that they can understand.

Data analytics a game-changer for organizations that want to better manage risk.

By embedding data analytics in every stage of the audit process and mining the vast (and growing) repositories of data available (both internal and external), Internal Audit can deliver unprecedented realtime insight, as well as enhanced levels of assurance to management and audit committees.

Read the following keys for successful adoption of data analytics in our next article 27.02.2020