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diff --git a/content/blog/2019-09-09-audit-analytics.md b/content/blog/2019-09-09-audit-analytics.md deleted file mode 100644 index a4a1dd3..0000000 --- a/content/blog/2019-09-09-audit-analytics.md +++ /dev/null @@ -1,201 +0,0 @@ -+++ -date = 2019-09-09 -title = "Audit Analytics" -description = "" -draft = false -+++ - -# What Are Data Analytics? - -A quick aside before I dive into this post: `data analytics` is a vague term -that has become popular in recent years. Think of a `data analytic` as the -output of any data analysis you perform. For example, a pivot table or a pie -chart could be a data analytic. - -[Data analysis](https://en.wikipedia.org/wiki/Data_analysis) is a process that -utilizes statistics and other mathematical methods to discover useful -information within datasets. This involves examining, cleaning, transforming, -and modeling data so that you can use the data to support an opinion, create -more useful viewpoints, and gain knowledge to implement into audit planning or -risk assessments. - -One of the common mistakes that managers (and anyone new to the process) make is -assuming that everything involved with this process is "data analytics". In -fact, data analytics are only a small part of the process. - -See **Figure 1** for a more accurate representation of where data analysis sits -within the full process. This means that data analysis does not include querying -or extracting data, selecting samples, or performing audit tests. These steps -can be necessary for an audit (and may even be performed by the same -associates), but they are not data analytics. - -# Current Use of Analytics in Auditing - -While data analysis has been an integral part of most businesses and departments -for the better part of the last century, only recently have internal audit -functions been adopting this practice. The internal audit function works -exclusively to provide assurance and consulting services to the business areas -within the firm (except for internal auditing firms who are hired by different -companies to perform their roles). - -> Internal Auditing helps an organization accomplish its objectives by bringing -> a systematic, disciplined approach to evaluate and improve the effectiveness -> of risk management, control and governance processes. -> -> - The IIA's Definition of Internal Audit - -Part of the blame for the slow adoption of data analysis can be attributed to -the fact that internal auditing is strongly based on tradition and following the -precedents set by previous auditors. However, there can be no progress without -auditors who are willing to break the mold and test new audit techniques. In -fact, as of 2018, [only 63% of internal audit departments currently utilize data -analytics](https://www.cpapracticeadvisor.com/accounting-audit/news/12404086/internal-audit-groups-are-lagging-in-data-analytics) -in North America. This number should be as close as possible to 100%. I have -never been part of an audit that would not have benefited from data analytics. - -So, how do internal audit functions remedy this situation? It's definitely not -as easy as walking into work on Monday and telling your Chief Audit Executive -that you're going to start implementing analytics in the next audit. You need a -plan and a system to make the analysis process as effective as possible. - -# The DELTA Model - -One of the easiest ways to experiment with data analytics and gain an -understanding of the processes is to implement them within your own department. -But how do we do this if we've never worked with analysis before? One of the -most common places to start is to research some data analysis models currently -available. For this post, we'll take a look at the DELTA model. You can take a -look at **Figure 2** for a quick overview of the model. - -The DELTA model sets a few guidelines for areas wanting to implement data -analytics so that the results can be as comprehensive as possible: - -- **Data**: Must be clean, accessible, and (usually) unique. -- **Enterprise-Wide Focus**: Key data systems and analytical resources must be - available for use (by the Internal Audit Function). -- **Leaders**: Must promote a data analytics approach and show the value of - analytical results. -- **Targets**: Must be set for key areas and risks that the analytics can be - compared against (KPIs). -- **Analysts**: There must be auditors willing and able to perform data - analytics or else the system cannot be sustained. - -# Finding the Proper KPIs - -Once the Internal Audit Function has decided that they want to start using data -analytics internally and have ensured they're properly set up to do so, they -need to figure out what they will be testing against. Key Performance Indicators -(KPIs) are qualitative or quantitative factors that can be evaluated and -assessed to determine if the department is performing well, usually compared to -historical or industry benchmarks. Once KPIs have been agreed upon and set, -auditors can use data analytics to assess and report on these KPIs. This allows -the person performing the analytics the freedom to express opinions on the -results, whereas the results are ambiguous if no KPIs exist. - -It should be noted that tracking KPIs in the department can help ensure you have -a rigorous Quality Assurance and Improvement Program (QAIP) in accordance with -some applicable standards, such as IPPF Standard 1300. - -> The chief audit executive must develop and maintain a quality assurance and -> improvement program that covers all aspects of the internal audit activity. -> -> - IPPF Standard 1300 - -Additionally, IPPF Standard 2060 discusses reporting: - -> The chief audit executive must report periodically to senior management and -> the board on the internal audit activity's purpose, authority, responsibility, -> and performance relative to its plan and on its conformance with the Code of -> Ethics and the Standards. Reporting must also include significant risk and -> control issues, including fraud risks, governance issues, and other matters -> that require the attention of senior management and/or the board. -> -> - IPPF Standard 2060 - -The hardest part of finding KPIs is to determine which KPIs are appropriate for -your department. Since every department is different and has different goals, -KPIs will vary drastically between companies. To give you an idea of where to -look, here are some ideas I came up with when discussing the topic with a few -colleagues. - -- Efficiency/Budgeting: - - Audit hours to staff utilization ratio (annual hours divided by total - annual work hours). - - Audit hours compared to the number of audits completed. - - Time between audit steps or to complete the whole audit. E.g., time from - fieldwork completion to audit report issuance. -- Reputation: - - The frequency that management has requested the services of the IAF. - - Management, audit committee, or external audit satisfaction survey - results. - - Education, experience, certifications, tenure, and training of the - auditors on staff. -- Quality: - - Number and frequency of audit findings. Assign monetary or numerical - values, if possible. - - Percentage of recommendations issued and implemented. -- Planning: - - Percentage or number of key risks audited per year or per audit. - - Proportion of audit universe audited per year. - -# Data Analysis Tools - -Finally, to be able to analyze and report on the data analysis, auditors need to -evaluate the tools at their disposal. There are many options available, but a -few of the most common ones can easily get the job done. For example, almost -every auditor already has access to Microsoft Excel. Excel is more powerful than -most people give it credit for and can accomplish a lot of basic statistics -without much work. If you don't know a lot about statistics but still want to -see some of the more basic results, Excel is a great option. - -To perform more in-depth statistical analysis or to explore large datasets that -Excel cannot handle, auditors will need to explore other options. The big three -that have had a lot of success in recent years are Python, R, and ACL. ACL can -be used as either a graphical tool (point and click) or as a scripting tool, -where the auditor must write the scripts manually. Python and the R-language are -solely scripting languages. - -The general trend in the data analytics environment is that if the tool allows -you to do everything by clicking buttons or dragging elements, you won't be able -to fully utilize the analytics you need. The most robust solutions are created -by those who understand how to write the scripts manually. It should be noted -that as the utility of a tool increases, it usually means that the learning -curve for that tool will also be higher. It will take auditors longer to learn -how to utilize Python, R, or ACL versus learning how to utilize Excel. - -# Visualization - -Once an auditor has finally found the right data, KPIs, and tools, they must -report these results so that actions can be taken. Performing in-depth data -analysis is only useful if the results are understood by the audiences of the -data. The best way to create this understanding is to visualize the results of -the data. Let's take a look at some of the best options to visualize and report -the results you've found. - -Some of the most popular commercial tools for visualization are Microsoft -PowerBI and Tableau Desktop. However, other tools exist such as JMP, Plotly, -Qlikview, Alteryx, or D3. Some require commercial licenses while others are -simply free to use. For corporate data, you may want to make sure that the tool -does not communicate any of the data outside the company (such as cloud -storage). I won't be going into depth on any of these tools since visualization -is largely a subjective and creative experience, but remember to constantly -explore new options as you repeat the process. - -Lastly, let's take a look at an example of data visualization. This example -comes from a [blog post written by Kushal -Chakrabarti](https://talent.works/2018/03/28/the-science-of-the-job-search-part-iii-61-of-entry-level-jobs-require-3-years-of-experience/) -in 2018 about the percent of entry-level US jobs that require experience. -**Figure 3** shows us an easy-to-digest picture of the data. We can quickly tell -that only about 12.5% of entry-level jobs don't require experience. - -This is the kind of result that easily describes the data for you. However, make -sure to include an explanation of what the results mean. Don't let the reader -assume what the data means, especially if it relates to a complex subject. _Tell -a story_ about the data and why the results matter. - -# Wrap-Up - -While this is not an all-encompassing program that you can just adopt into your -department, it should be enough to get anyone started on the process of -understanding and implementing data analytics. Always remember to continue -learning and exploring new options as your processes grow and evolve. |