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+#+date: 2019-09-09
+#+title: Data Analysis in Auditing
+
+* 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.
+
+[[https://en.wikipedia.org/wiki/Data_analysis][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.
+
+#+CAPTION: The Intelligence Cycle
+[[https://img.0x4b1d.org/blog/20190909-data-analysis-in-auditing/intelligence_cycle-min.png]]
+
+* 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).
+
+#+BEGIN_QUOTE
+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
+#+END_QUOTE
+
+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, [[https://www.cpapracticeadvisor.com/accounting-audit/news/12404086/internal-audit-groups-are-lagging-in-data-analytics][only 63% of internal audit departments currently utilize 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.
+
+#+CAPTION: The Delta Model
+[[https://img.0x4b1d.org/blog/20190909-data-analysis-in-auditing/delta-min.png]]
+
+* 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.
+
+#+BEING_QUOTE
+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
+#+END_QUOTE
+
+Additionally, IPPF Standard 2060 discusses reporting:
+
+#+BEGIN_QUOTE
+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
+#+END_QUOTE
+
+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 [[https://talent.works/2018/03/28/the-science-of-the-job-search-part-iii-61-of-entry-level-jobs-require-3-years-of-experience/][blog post written by Kushal Chakrabarti]] 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. For example, *Figure 4*
+shows a part of the explanation the author gives to illustrate his point.
+
+#+CAPTION: Entry-Level Visualization
+[[https://img.0x4b1d.org/blog/20190909-data-analysis-in-auditing/vis_example-min.png]]
+
+#+CAPTION: Visualization Explanation
+[[https://img.0x4b1d.org/blog/20190909-data-analysis-in-auditing/vis_example_explanation-min.png]]
+
+* 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.