Before you start analyzing data, you need to 1) know you have the right data, and 2) understand the data and the process that produced it.
This post assumes, of course, that you already accomplished some of the hardest tasks already: figuring out what data you need, where to get it, and actually getting the data. Good luck with that. :)
This post is part of the Excel: Basic Data Analytic series.
Continue reading →
Like this:
Like Loading...
If you’re an auditor, you need data analytic skills or you will die.
Or put another way, if you don’t acquire them in the next 1-5 years, you will no longer be an auditor.
Pretty bold statement, isn’t it?
Continue reading →
Like this:
Like Loading...
Filed under Audit, Data Analytics, Employment, Free, Technology, Written by Skyyler
Tagged as Academy, acl, concatenate, data, ETL, extract, fuzzy, iia, isaca, join, lookup, MISTI, outlier, pattern, pivot, population, table, training, unstructured, validation
Before you analyze data, you must first validate it.
Otherwise, your analysis may not be accurate, and you may miss some important insights or errors.
This post is part of the Excel: Basic Data Analytic series.
Before analyzing your data, you need to check the following:
- Duplicate transactions do not exist.
- Required fields/key fields do not contain blanks, spaces, zeroes, unprintable characters, or other invalid data.
- Date fields contain real dates, and the range of dates is appropriate.
- Amount fields don’t contain inappropriate zero, positive, or negative amounts, and the range of values is appropriate.
- Each field is stored in the correct format. This prevents data from being converted on the fly into something else unexpectantly (e.g., user ID JUL15 becomes 15-Jul).
Continue reading →
Like this:
Like Loading...
Filed under Audit, Data Analytics, Excel, How to...
Tagged as amount, analyze, data, data analytics, date, dollar, duplicate, error, excel, field, ID, inappropriate, invalid, match, population, validation, verify
Do you perform appropriate population validation of the data you rely on in an audit?
Population validation is simply gaining confidence that the data you are using in your audit contains all the appropriate data for your audit objectives (e.g., your server list includes all the SOX servers).
For the difference between population validation and data validation, see Why You Must Validate Data.
So how do you do population validation? Let’s look at an example…
Continue reading →
Like this:
Like Loading...
Filed under Audit, How to...
Tagged as 5 whys, alter, comfort, confidence, data, duplicates, manipulate, observe, population, question, rely, SME, sox, spider sense, validate, validation
A couple of us were arguing about the differences between random, haphazard, and judgmental sampling. One person said that picking samples here and there manually was random sampling. I argued the method described was actually haphazard sampling. Another said that haphazard sampling was not appropriate and that “audit judgment” was valued, not haphazard sampling.
Continue reading →
Like this:
Like Loading...
Filed under Audit
Tagged as acl, bias, debiasing, haphazard, judgmental, population, pseudo-random, random, reliable, sampling, selection, sox system