Chapter 8:
CAATTs for Data
Extraction and Analysis
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2e, Hall & Singleton
Singleton
DATA STRUCTURES
 Organization
Access method
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Access:
Non-Index
Methods
Hashing
Pointers
INDEX
File
DATA File
Access:
Index Methods
Data
Organization
SEQUENTIAL
ISAM
RANDOM
SEQUENTIAL
RANDOM
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FILE PROCESSING
OPERATIONS
1.
2.
3.
4.
5.
6.
7.
Retrieve a record by key
Insert a record
Update a record
Read a file
Find next record
Scan a file
Delete a record
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Individual
Records
Table 8-1
DATA STRUCTURES
 Flat file structures
 Sequential structure [Figure 8-1]
All records in contiguous storage spaces in
specified sequence (key field)
Sequential files are simple & easy to process
Application reads from beginning in sequence
If only small portion of file being processed,
inefficient method
Does not permit accessing a record directly
Efficient: 4, 5 – sometimes 3
Inefficient: 1, 2, 6, 7 – usually 3
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DATA STRUCTURES
 Flat file structures
 Indexed structure
In addition to data file, separate index
file
Contains physical address in data file
of each indexed record
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DATA STRUCTURES
 Flat file structures
 Indexed random file [Figure 8-2]
Records are created without regard to
physical proximity to other related records
Physical organization of index file itself may
be sequential or random
Random indexes are easier to maintain,
sequential more difficult
Advantage over sequential: rapid searches
Other advantages: processing individual
records, efficient usage of disk storage
Efficient: 1, 2, 3, 7
Inefficient: 4
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DATA STRUCTURES
 Flat file structures
 Indexed Sequential Access Method (ISAM) [Figure 8-3]
 Large files, routine batch processing
 Moderate degree of individual record processing
 Used for files across cylinders
 Uses number of indexes, with summarized content
 Access time for single record is slower than Indexed
Sequential or Indexed Random
 Disadvantage: does not perform record insertions efficiently
– requires physical relocation of all records beyond that
point – SOS
 Has 3 physical components: indexes, prime data storage
area, overflow area [Figure 8-4]
 Might have to search index, prime data area, and overflow
area – slowing down access time
 Integrating overflow records into prime data area, then
reconstructing indexes reorganizes ISAM files
 Very Efficient: 4, 5, 6
 Moderately Efficient: 1, 3
 Inefficient: 2, 7
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DBMS etc.
Legacy systems
Legacy systems
1960
1970
1980
1990
EVOLUTION OF ORG./ACCESS METHODS
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Efficient
Inefficient
Access single records
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Access entire files
HASHING STRUCTURE
 Employs algorithm to convert
primary key into physical record
storage address [Figure 8-5]
 No separate index necessary
 Advantage: access speed
 Disadvantage
 Inefficient use of storage
 Different keys may create same
address
 Efficient: 1, 2, 3, 6
 Inefficient: 4, 5, 7
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POINTER STRUCTURE

Stores the address (pointer) of related record in a
field with each data record [Figure 8-6]




Records stored randomly
Pointers provide connections b/w records
Pointers may also provide links of records b/w files
[Figure 8-7]
Types of pointers [Figure 8-8]:
 Physical address – actual disk storage location
•
Advantage: Access speed
•
Disadvantage: if related record moves, pointer must be changed
& w/o logical reference, a pointer could be lost causing
referenced record to be lost
 Relative address – relative position in the file (135th)
•
Must be manipulated to convert to physical address
 Logical address – primary key of related record
•
Key value is converted by hashing to physical address


Efficient: 1, 2, 3, 6
Inefficient: 4, 5, 7
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DATABASE STRUCTURES
 Hierarchical & network structures
[Figure 8-9]
 Uses explicit linkages b/w records to
establish relationship
 Figure 8-9 is M:N example
 Relational structure
 Uses implicit linkages b/w records to
establish relationship:
foreign keys / primary keys
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Relational Database: “table” – rows and columns
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Relational Records: “Foreign Keys” in one record establishes
relationships to related records in other files.
CUSTOMERS
INVOICES
INVENTORY
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DATABASE STRUCTURES
 Relational structure
 User views
 Data a particular user needs to achieve his/her
assigned tasks
 A single view, or view without user input, leads to
problems in meeting the diverse needs of the
enterprise
 Trend today: capture data in sufficient detail and
diversity to sustain multiple user views
 User views MUST be consolidated into a single
“logical view” or schema
 Data in the logical view MUST be normalized
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DATABASE STRUCTURES
 Relational structure
 Creating views
 Designing output reports, documents, and
input screens needed by users or groups
 Physical documents help designer
understand relationships among the data
• 3 user views: Table 8-2, Figure 8-12, Table
8-3
 Then apply normalization principles to the
conceptual user views to design the database
tables
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DATABASE STRUCTURES
 Relational structure
 Importance of data normalization
 Critical to success of DBMS
 Effective design in grouping data
 Several levels: 1NF, 2NF, 3NF, etc.
 Un-normalized data suffers from:
• Insertion anomalies
• Deletion anomalies
• Update anomalies
 One or more of these anomalies will exist
in tables
< 3NF
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DATABASE STRUCTURES
 Relational structure
 Normalization process
 Un-normalized data [Table 8-4]
 Eliminates the 3 anomalies if:
• All non-key attributes are dependent on the
primary key
• There are no partial dependencies (on part of
the primary key)
• There are no transitive dependencies; non-key
attributes are not dependent on other non-key
attributes
 “Split” tables are linked via embedded
“foreign keys”
 Normalized database tables examples:
Figures 8-13, 8-14
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DATABASE STRUCTURES
 Relational structure

Creating physical tables
 Created on paper so far
 Then create physical files and populate data
 Physical views can be produced from DBMS

Query function
 Allows users to create customized lists from database
 Users stipulate, using English-like commands, which tables,
records, fields, filtering criteria needed to produce the
desired list
 Result is virtual table derived from actual database tables
 SQL
•
•
SELECT, FROM, WHERE [Figure 8-16]
De facto standard query language
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DATABASE STRUCTURES
 Relational structure
 Auditors and data normalization
 Database normalization is a technical matter that
is usually the responsibility of systems
professionals.
 The subject has implications for internal control
that make it the concern of auditors also.
 Most auditors will never be responsible for
normalizing an organization’s databases; they
should have an understanding of the process and
be able to determine whether a table is properly
normalized.
 In order to extract data from tables to perform
audit procedures, the auditor first needs to know
how the data are structured.
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EMBEDDED AUDIT MODULE
 Identify important transactions live
while they are being processed and
extract them [Figure 8-18]
 Examples
 Errors
 Fraud
 Compliance
• SAS 78, SAS 94, SAS 99 / S-OX
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EMBEDDED AUDIT MODULE
 Disadvantages:
 Operational efficiency – can decrease
performance, especially if testing is
extensive
 Verifying EAM integrity - such as
environments with a high level of
program maintenance
 Status: increasing need, demand, and
usage of COA/EAM/CA
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GENERALIZED AUDIT
SOFTWARE
 Brief history
 Most widely used CAATT [Figure 8-19]
 Usages include:
1) Footing and balancing entire files or selected data
items (e.g., extending inventory)
2) Selecting and reporting detail data
3) Selecting stratified statistical samples from data files
4) Formatting results into audit reports (auto work papers!)
5) Printing confirmations
6) Screening / filtering data
7) Comparing multiple files for differences
8) Recalculating values in data
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GENERALIZED AUDIT
SOFTWARE
 Popular because:
1. GAS software is easy to use and requires
little computer background
2. Many products are platform independent,
works on mainframes and PCs
3. Auditors can perform tests independently
of IT staff
4. GAS can be used to audit the data
currently being stored in most file
structures and formats
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GENERALIZED AUDIT
SOFTWARE
 Simple structures [Figure 8-19]
 Complex structures [Figures 8-20, 8-21]
 Auditing issues:
 Auditor must sometime rely on IT personnel to
produce files/data
 Risk that data integrity is compromised by
extraction procedures
 Auditors skilled in programming better prepared
to avoid these pitfalls
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ACL
 ACL is a proprietary version of GAS
 Leader in the industry
 Designed as an auditor-friendly meta-
language (i.e., contains commonly
used auditor tests)
 Access to data generally easy with
ODBC interface
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ACL
 See ACL tutorial #1
 Input file definition
 Customizing a view
[Figure 8-23]
 Filtering data
[Figures 8-24 thru 8-27]
 Stratifying data [Figure 8-28]
 Statistical analysis
IT Auditing & Assurance, 2e, Hall & Singleton
Chapter 8:
CAATTs for Data
Extraction and Analysis
IT Auditing
& Assurance,
2e, Hall
Singleton
IT Auditing
& Assurance,
2e,&
Hall
&
Singleton