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-date = 2019-09-09
-title = "Data Analysis in Auditing"
-description = "Learn how to use data analysis in the world of 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.
-
-[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.
-
-![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).
-
-> 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.
-
-![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.
-
-> 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. For example, **Figure 4** shows a part of the
-explanation the author gives to illustrate his point.
-
-![Entry-Level
-Visualization](https://img.cleberg.net/blog/20190909-data-analysis-in-auditing/vis_example-min.png)
-
-![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.