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+#+date: <2019-09-09>
+#+title: Audit Analytics
+#+description: 
+
+
+* 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.
+
+* 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.
+
+* 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.
+
+#+begin_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.
+
+* 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.