Evolution of Data Analysis
By Monica Holtforster
History of Audit Analytics
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Cobol / Easytrieve
DOS
Windows
Server Technology
In the Beginning
DOS Based Data Analytics
Today’s Audit Software
Impact of Technology on Software
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Speed of processing / file size accommodated
Ease of use
Import options available
Graphical repesentation
Technical level of user
Data created and captured worldwide
Exabytes
ATMs
ERPs
Transactional data
CRM , Accounting
databases, new compliance
requirements, new medias etc…
Tenfold growth observed in five years
Source: “Digital Universe” study 2008
IDC Study highlights
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In 2011, digital data was 10 times size of 2006
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44-fold in the next ten years
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Data growth can not be ignored
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Tools are in place and proven
Evolution of Data Analysis Techniques
Source: ISACA– July2011
Where is your company’s use of
data analysis on this chart?
Definition of Ad Hoc
• Typically used for initial investigation
• Typically run to support specific projects
• Rarely performed directly on production
systems
• May be difficult to repeat if steps not well
documented
• Often relies on skill of selected skilled
individuals
Evolution of Data Analysis techniques
PC based audit
software
Source: ISACA– July2011
Definition of Repeatable
• Predefined and scripted to perform the same
tests on similar data
• Data access tools may be used to import data
directly from production systems
• Reliance on skilled individuals significantly
reduced
• The quality of analysis is improved and
remains consistent as the data acquisition
process is partially or fully automated
Centralized Analytics
• Centralized approach for the development,
storage and operation of repeatable DA
• Standards for development of DA are
documented
• Applications are set up and scheduled to run
against the centralized data on a regular basis
• Data can either be pushed or pulled from
different sources
Evolution of Data Analysis techniques
Server based software
Source: ISACA– July2011
Continuous Monitoring
• Analytics are fully automated and running at
regularly scheduled intervals
• may be embedded directly into a production
system
• often developed and owned by operations
management
Evolution of Data Analysis techniques
CM software running on
a server
Source: ISACA– July2011
Recommendations
• Do simple process development first, using
existing software
• Automate data extraction and validation
• Reduce false positives
• Prioritize by likelihood of recovery
• Refine and document the testing process
over several cycles.
Future Expectations
Predictive pre-canned analysis
Increased intelligence in the
software
Integration of different tools
Use of external sources for
comparison
Conclusion
As the definition
of data changes,
who knows what
additional
changes we will
see incorporated
into data
analytics?