fix grammar in 2019 posts

1 files changed, 32 insertions, 32 deletions

diff --git a/content/blog/2019-09-09-audit-analytics.org b/content/blog/2019-09-09-audit-analytics.org
index 400e6fd..5984b5d 100644
--- a/content/blog/2019-09-09-audit-analytics.org
+++ b/content/blog/2019-09-09-audit-analytics.org
@@ -23,8 +23,8 @@ 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.
+can be necessary for an audit (and the same associates may perform these steps),
+but they are not data analytics.
 
 * Current Use of Analytics in Auditing
 
@@ -32,8 +32,8 @@ 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).
+within the firm (except for internal auditing firms hired by different companies
+to perform their roles).
 
 #+begin_quote
 Internal Auditing helps an organization accomplish its objectives by bringing a
@@ -66,15 +66,15 @@ 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:
+The DELTA model sets 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
+- *Targets*: Must be defined 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.
@@ -91,9 +91,9 @@ 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.
+Note 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
@@ -143,36 +143,36 @@ colleagues.
 * 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.
+evaluate the tools at their disposal. There are options available, but 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.
+that have had a lot of success in recent years are Python, R, and ACL Analytics
+(ACL). You can use ACL 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.
+to fully utilize the analytics you need. To create the most robust solutions,
+you should write the scripts manually. Note 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.
+report these results so that readers of their results can take action.
+Performing in-depth data analysis is only useful if the audience can understand
+the results. 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,
@@ -180,8 +180,8 @@ 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.
+is 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