Acca aaa s21 Notes

2. Big data and data analytics
A current issue for the profession is the development of emerging technologies including big data 
and the use of data analytics. How may they affect the conduct of an audit and audit quality? 
2.1. Big data
The characteristics which make data ‘big’ are:
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Volume – will be vast
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Variety – non-uniform/unstructured
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Velocity – fast and continuous
See Chapter 23 of our Strategic Business Leader (SBL) notes
for further details if you are not familiar 
with big data.
The fourth ‘V’ – veracity – is particularly relevant to auditing. Big data is likely to include bias, 
abnormalities, inconsistencies and/or duplication. So how true or accurate is the data? How 
trustworthy the sources?
2.2. Data analytics
Data analytics is the science of examining 
raw
data to draw conclusions. So an obvious audit 
application is in 
analytical procedures
– the main differences between ‘traditional’ analytical 
procedures and analytical procedures using data analytics are:
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As the data is raw, the auditor must assess and verify the level of veracity
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Data analytics must be computer-assisted.
The IAASB set up a Data Analytics Working Group (DAWG) to explore the increasing use of technology 
in audit with a focus on data analytics. It suggests that using data analytics can:
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Enhance audit quality through a more robust understanding of the entity
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Gather audit evidence from larger population
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Improve risk-based sample selection for further testing.
However, it is important to recognise the limitations of data analytics:
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Need for veracity
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Even with 100% testing, assurance can only be ‘reasonable’
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Not a substitute for professional judgment and professional scepticism
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The risk of over confidence in using technology.