Don’t Get Caught Off Guard: How to Budget for Data Analytics Part 2
Continued from Part 1 of our ‘How to Budget for Data Analytics’ blog series, our second post will cover the last of three key steps that audit specialist Rigobert Pinga Pinga, CIA, CPE, CEF, CGMA discussed in a recent report for the Institute of Internal Auditors. The third and final step? Considering several critical success factors.
Key Success Factors
Budgeting for data analytics has to be understood as a changing and multifaceted exercise that takes many factors into account to ensure levels of efforts are estimated well. For the level of effort to be continuously improved and adjusted, a few factors are key to success:
Validation of the Analytics Budget
While the analytics team is generally tasked with estimating the level of effort required for data analytics, they also need to collaborate with internal audit. Essential input needs to be taken into account (e.g., minutes from audit brainstorming meetings, feedback from audit clients on the proposed audit plan, trends in the use of analytics across the years, etc.).
Feedback should continuously be sought from internal audit and management. Stakeholders should also be engaged in validation meetings once the level-of-effort matrix (see Part 1 of the blog series for more information) has been adopted. The level of effort estimated for each engagement can then be adjusted following these meetings if required. The calculated and adjusted levels of effort should be recorded and any significant changes noted in order to help fine tune the criteria that will be used to determine the likelihood and intensity of data analytics exercises in the future.
Adoption of a Funding Mechanism
Before a finalized data analytics budget is shared with the organization, the audit department must determine if the engagement’s original budget should increase to include the data analytics budget, or if the analytics budget should be worked into the original budget for the engagement. In the case of the former situation, the budget for analytics is drawn from a central contingency fund and may add extra days into the audit schedule, essentially erasing any efficiencies created by the analytics work.
In the case of the latter situation, the overall budget remains the same—the team is “doing more with less” on a specific audit engagement—and a sense of accountability is fostered across the audit and data analytics teams.
Once an engagement is complete or the year-end has arrived, the initial budget versus the adjusted budget should be compared and the actual days spent noted in order to help judge the accuracy of level-of-effort estimates. If the final budget was much higher than anticipated, it may be due to two factors:
- The experience level of the data analytics team (an inexperienced team requires more time to complete their work, but as they gain experience the time will decrease).
- The maturity of the analytics process (the level of effort required in the early stages of data analytics use can be substantial due to lack of a strong partnerships, processes, infrastructure, etc.).
Professional Judgment is Irreplaceable
Although it can be difficult to estimate the level of data analytics effort required for each audit engagement, the approach described in this blog series yields a variety of benefits, including a more streamlined and efficient audit process. Pinga Pinga notes, however, that professional judgment should always be the foundation of the process. Decision-making should be informed by auditors’ experience and knowledge, with the level-of-effort methodology being used to both inform and support conclusions.
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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