Don’t Get Caught Off Guard: How to Budget for Data Analytics Part 1
If you’re using data analysis software to help conduct your audits, then you already know the significant value it offers. You’re also most likely all too aware that the success of your data analytics program rests on your ability to effectively use those tools, to plan their use, and to accurately estimate how much time and effort must be dedicated to data analytics during any given audit.
Audit specialist Rigobert Pinga Pinga, CIA, CPE, CEF, CGMA recently wrote a report for the Institute of Internal Auditors addressing this exact challenge. He noted that while some organizations can simply put aside a set percentage of each engagement’s budget for data analytics—in cases of repeated audits, for example—the unique nature of many audits requires that a “reasonable and justifiable” budget be established for each engagement.
Pinga Pinga listed three key steps involved in determining an analytics budget. In Part 1 of this two-part blog series, we will cover two of these steps: leadership support and estimating the level of effort.
As with any type of program, securing the support of your leadership team is essential. Having buy-in from the top lets the rest of the organization know that there is a commitment to data analytics. If possible, the CAE should communicate to employees the strategy for data analytics and why it is essential that adequate staff and time are dedicated to its use throughout audit engagements.
Estimate Level of Effort
Identify Potential Engagements
To begin the process of estimating the level of effort required, you can first identify potential audit engagements that may require data analytics and think of ways to best leverage data analytics to support your audit’s results. Based on this list, analytics work can be prioritized for effort estimation. Thought should also be given to any challenges that could potentially arise during each audit.
Next you can assess the probability of data analytics being used for each audit. Pinga Pinga recommends implementing a three-tiered assessment system, with the three tiers being ‘None’ (no data analytics to be involved); ‘Likely’ (some data analytics will be needed); and ‘Certain’ (data analytics will definitely be required). Because the difference between ‘Likely’ and ‘Certain’ may not be entirely clear cut, it’s recommended in these cases to use a hybrid assessment: ‘None/Certain’, ‘None/Likely’, or just ‘Yes/No’.
To help determine the level of effort required to leverage data analytics in an audit engagement, the intensity of the analytics needed should be estimated using, for example, a three-level scale:
- Low – Only a basic analysis will be conducted; estimated resources needed are small.
- Medium – An intermediate level of data analysis is required, including profiling and pattern identification, benchmarking, stratification, gap analysis, efficiency measurement, and calculation of statistical parameters to identify outliers.
- High – It’s expected that the audit will involve a lot of data analytics, with the core of the review being driven by the analytics.
Develop a Matrix
Last but not least, Pinga Pinga suggests creating a matrix, based on the probability and intensity estimates, that demonstrates how the level of data analytics activities were assessed. The goal of the matrix is, ultimately, to help decide the budget estimates for your data analytics budget.
Click here for the next installment in the ‘Budgeting for Data Analytics’ blog series, where we cover key success factors, benefits and the bottom line.
About Anu Sood:
Anu Sood is the Director of Product and Corporate Marketing at CaseWare Analytics and is responsible for the company’s global marketing strategy. Prior to CaseWare Analytics, Anu worked in various roles in the high-tech industry and her accomplishments range from writing software for telephone switches to launching a new global satellite communication service. Anu has extensive experience in strategic marketing, corporate communications, demand generation, content marketing, product management, product marketing and technology development.
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