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-date = 2019-09-09
-title = "Audit Analytics"
-description = ""
-draft = false
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-
-# 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.