We are running a series of blogs on what the most common issues are around data analytics for auditors, so from a lack of knowledge/direction and strategy through to false positives and inconsistent output along with how to handle missing data sets and fields, so, without further ado, here’s blog number 2 on Data Quality and Integrity
Tip 1: The analysis is only as good as the data
The first question you need to ask yourself is how reliable is the data that you are going to be working with? Is this coming from multiple data sources and/or does it need to be cleansed or re-formatted?
Tip 2: Treat every project like a blank canvas
If it is an analysis of a new set of data, treat it as such. Alternatively, if it is simply a continuation of a previous analysis, check the controls that are in place are working as they should and that nothing is slipping through.
Tip 3: Filter the data
Remove the tables and fields that you don’t need or which you think are going to either confuse or dilute the results. Think carefully and logically.
Tip 4: Data access
Get to know who is responsible for handling and storing the data for which you are going to analyse and understand what work may have been conducted previously (if any) with the same. If any areas were brought to light then have they been resolved prior to you conducting your analysis?
Tip 5: Check, check and double check!
If there are key tables/fields that are missing from the data sets, this could well skew the results of your analysis, so be sure to be well prepared and have all that you need well in advance.
Author: David Ryan
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