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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.cleberg.net/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.cleberg.net/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.cleberg.net/blog/20190909-data-analysis-in-auditing/vis_example-min.png]]
-
-#+CAPTION: Visualization Explanation
-[[https://img.cleberg.net/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.