IIA Los Angeles Chapter Data Analytics Basics · Alteryx and Qlikview. IIA and data analytics The...

IIA Los Angeles ChapterData Analytics Basics

January 14, 2015

2© 2015 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved NDPPS 334170

Today’s objectives

■ Gain an understanding of the data analytics tools popular with internal and external auditors

■ Learn the functions of data analytic tools that can be used to evaluate data for potential fraud or other irregularities

■ Observe demonstrations of how one data analytics package implements these functions and captures the work performed for workpaper documentation

■ Learn about data visualization.


Agenda

Background

IIA and data analytics The new focus on analytics and related

skills Some definitions

Data analytics tools in use today – their benefits and drawbacks

The Microsoft tools

ACL and IDEA

others

Data acquisition Formats

Getting the data

Verifying completeness and accuracy

A closer look

Introduction to IDEA Menu structure and Functions

Importing

Field Statistics and History

Indexing and Sorting

Summarization and Stratification

Extractions

Duplicate and Gap Detection

Smart Analyzer Pre-written routines for key audit tests

Visualization Alteryx and Qlikview

IIA and data analytics

The new focus


The new focus on data analytics and related skills

December 2014 Issue of “Internal Auditor” – The Year Ahead

1. “Perhaps the biggest operational change internal auditors will likely face in the next four quarters is major movement forward in the way risks are identified, as many departments are in various states of implementing data analytics and predictive modeling to mine for past clues to the future.” (See Notes)

2. “Appropriately, the emphasis has continued to be on internal audit departments’ ability to expand their data analytics capabilities. And internal auditors aren’t the only ones developing that skill set in most organizations. Analytics is being used in the first and second lines of defense as well, and I see a growing role for internal auditors in auditing the use of these tools by other functions in the organization.”

3. “We need to know how process owners are using data – assurance that the data they are using to manage the business is helpful, useful reliable and is not giving them false conclusions, we have to be thinking from a data-driven approach We have to be using analytics because the business is expecting leaders are expecting it. The old method of providing assurance based upon sampling just isn’t relevant in the current business model.”


Data analytics in internal audit (PWC 2013)


Benefits of data analytics in internal audit

Enhanced risk management process and audit quality

Focus on more data. Reduce manual review to key areas of interest/concern

Limit sampling; cover 100% of population

Increase in audit quality

Enable analysis of data from different sources/combined data

May provide easy-to-review audit trail


Traditional approach

Tests of application controls

Analytical procedures

Tests of details of transactions and balances

Extracting data for substantive audit procedures

Re-performing calculations performed by the entity's accounting department


The new world of data analytics

Finding patterns and associations among individual or groups of data elements

Validating data entry dates/times/users to identify postings or data entry times that are inappropriate or suspicious

Joining different data sources to identify new or inappropriate relationships and patterns between systems


Some definitions about databases

Database – A collection of information. A database is comprised of one or more tables of data organized by fields, records, and cells.

Tables – The view that displays the data base as a combinations of rows (records) and columns (fields). The first row of a table is reserved for the field names.

Field names – The different categories in a database. The top row is reserved for field names. Examples of field names are first name, last name, address, city, state, zip, phone number.

Field – Categories of information in a database. Fields are displayed in columns. For example, in a database, the zip field contains the zip codes for each of the records.

Records – A collection of fields containing related information about an entity. For example, a first name, last name, address, city, state, zip and phone number name and address for one person or family make up one record in the database. A second set of name, address and phone number constitute a different record.

Key – A field that uniquely identifies a record. For example, assuming no repeats in our name database, first name + last name is the key. Keys can used to get information from multiple tables that are related by a common key.


Data types often seen in CAATs packages

Character: numbers, letters and special characters (punctuation and formatting)

Treated as characters for sorting, i.e., ASCII value

Formatting characters are generally not visible

■ Page break, form feed, space

Numeric: numbers only

Treated as numbers for sorting, i.e., numeric value

Watch out for leading zeros; chose character if uncertain


Data types often seen in CAATs packages (continued)

Date: specially formatted character fields

IDEA stores date fields as YYYYMMDD format

Can display in other formats, using a “mask”

In date format, sorting is date order; otherwise character order

Time: specially formatted numeric field

IDEA displays as HH:MM:SS using a mask

Times are stored as a number of seconds


Odd sorting sequence – Trans_ID is stored as a character field

Data types – Character versus numeric

Data analytics tools in use today

Their benefits and drawbacks


The Big Data World

■ Hadoop – Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs.

■ SAS – Statistical Analysis System is a software suite developed by SAS Institute for advanced analytics, business intelligence, data management, and predictive analytics.

■ R – An open source project, R is capable of addressing almost every statistical inference problem.

Software offerings in the data analytics space


Software offerings in the CAATs space

■ ACL

■ IDEA

■ Excel

■ Access

■ SQL

■ TopCAATs – automates IDEA/ACL – like functions in Excel – underlies TeamMate Analytics

■ Audit Analytics (Arbutus Software) – founded by one of the ex-ACL partners


Current comparison of CAATs tools

Comparison of Three ToolsMS Excel MS Excel – v2010 MS Access IDEA v9

Data

Data Size 1048576 rows by 16384 columns 2 GB (database size)1.6 exabytes (1.15 X10^18)(approx. 2.2 billion rows)

Data Type Fixed Width – Delimited Fixed width – delimited Fixed width, delimited, structured reports

Data Source Manual, external spreadsheets, text files and other data formats

Manual, external spreadsheets, text files and other data formats

Text files, databases, formatted reports

Data Manipulation

Sort Yes Yes Yes

Filter Yes Yes Yes

Query Somewhat Yes Yes

Report / Chart Charts and graphs Predefined and customizable reports Customizable reports and graphs

Links to other data Static link to other spreadsheets; customizable link to other data

Real-time link to internal and external data; no customization needed.

Static links to databases via ODBC

Other

Automatic Audit Log No No Yes

Customizable Via Macro and Visual Basic for Applications (VBA) scripts

Via Macro and Visual Basic for Applications (VBA) scripts

Forms and Reports

Via Macro and Visual Basic for Applications (VBA) scripts

Data Export Yes Yes Yes

Best Used For Small numeric data setsCharting and presenting results

Medium size text and numeric data set, cross reference to other dataNeed a form-driven front end.

Medium and large data sets, cross reference to other data

Data acquisition

Formats

Getting the data

Verifying completeness and accuracy


Before you ask IT – Ask yourself – What are you trying to test

Reports

■ Off-the-shelf

■ Custom/ad-hoc

Transaction Details

■ Reports

■ Data

Metadata

■ Reports

■ Data

Controls

■ Combinations of all of the above


What you are trying to test defines the layers of IT you need to consider

Reports

■ Applications: reporting functionality

■ Custom/ad-hoc: report writers

Transaction Details

■ Reports: Applications: reporting functionality

■ Data: database

Metadata

■ Reports: report writers

■ Data: database

Controls

■ Combinations of all of the above and the O/S


Some common applications and their databases

MS Dynamics – Great Plains

FAS (Fixed Asset Software)

Equity Edge

ADP – PC Payroll for Windows

SAP

JD Edwards

MAS 90/200

MS SQL

MS SQL

ORACLE

ORACLE/BaseSQL

ORACLE/DB2/MS SQL

DB2

SQL/ProvideX (from the app)


Some common applications and their report writers

MS Dynamics – Great Plains

FAS (Fixed Asset Software)

Equity Edge

ADP – PC Payroll for Windows

SAP

JD Edwards

Oracle Financials

Crystal Reports

FAS Report Writer

Crystal Reports

Report Smith

Business Objects

JDE Report Writer

Hyperion


Formats for data analytics tools

The following are common to CAATs tools

Excel – row limitation; data easily changeable

Access – data also easily changed; might also hit a size limitation (1GB for pre 2003)

Flat File – ASCII vs. EBCDIC

Delimited text file

PDF (Usable and not usable) and text reports

Open Database Connectivity (ODBC)

XML


Importing text and PDF with in IDEA and ACL

Used for non-columnar data that repeats in structure or contains identifiable patterns in the data you want included

Also used to pull detail columnar data without the header and footer information

Works from the most detailed level of information to the highest

Remember – PDF that results from scanned in files may be problematic


Text/PDF – Report Reader – A formatted text report


Completeness and accuracy of the file import

This step, at the beginning of your work, can make or break all of your analysis and conclusions

There are several features which will give you insight into completeness and accuracy

■ Control Totals

■ Field Statistics

■ Record Counts

Depending on the kind of data you acquire for your analysis, you may need to get creative to demonstrate these two attributes of your import


Completeness and accuracy – Control totals

Control Total shows the net total of one key numeric field. Compare this to the total imported from your source document.


Record counts and amounts – Summarization and comparison to source

Fraud or Error on source code JE?


When numeric data isn’t….

Completeness and Accuracy

How can we validate completeness without control totals?

■ Line editor that shows line counts and cursor position (ex. Vedit)

■ Programming tool – not SQL – that allows querying against non-columnar data (e.g., PERL)


Completeness and accuracy – Non-numeric data

You still need to demonstrate completeness and accuracy

Accuracy can generally be shown on a sample basis, pulling certain records and comparing against the source

Completeness is another issue…


ADP Operator security profile – A non-numeric file


Non-numeric data and completeness – One approach

Working with PDF reports – using scripts to count lines may be a problem; how should we define a specific line format for the software to count

Metrics

■ 1,624 pages in PDF version of the Operator Profile Report

■ 44,696 imported records for access-related pages

Reasonableness is acceptable

Predicting the number of pages and records

■ Use IDEA to count page types

■ How many records per page


Non-numeric data – Completeness – Fields for IDEA to count

Operator and Title occuron each page


Non-numeric data – Completeness – Summarize operator and title from each page

Operator Authorized Tasks 1152

Operator Definition 59

Operator Department Access 235

Operator Signon Times 59

Process Scheduler Profile 59

Total 1624

Page count = last page number

Our import has covered the correct number of pages from the source document


Non-numeric data – Completeness – Lines per page – Lines in total

Assuming full pages for each user

Assuming full pages for each user, except each user’s last page is only half full

IDEA Menu structure and functions

Menu

Importing

Field statistics and history


What’s on the menu?


Common tasks in data analytics – Not in any particular order

■ Importing/verifying data

■ Profiling data/gaining a high-level perspective

■ Indexing and Sorting

■ Summarization and Stratification

■ Extractions

■ Sampling/Benford’s Law (not covered in this session)

■ Duplicate and Gap Detection

■ Joins and Appends

■ Visualization

■ Reporting (not covered in this session)


When should data not be imported as its original type?

Fields with numerics that are not used in calculations – risk of dropping one or more leading zeroes

Fields with dates where there may be a blank or malformed data

■ Avoid errors on import

■ Can convert after import


When to use ODBC

Yes

Don’t need to test data relationshipsindependently

You only want certain fields

Clear, simple table layouts

Clear-text data storage

No

× Client does not allow connection into their system

× Tested report available

× Complex tables / relationships

× Database does not support ODBC

× Embedded objects in the data

× Very large datasets


File types by ease of import

dBase III/IV

Print report files

MS Excel

Text (Delimited / Fixed length)

ODBC

EBCDIC

* Record definitions needed

*

*

Increaseddifficulty

MS Access


Importing text and PDF with report reader

Used for non-columnar data that repeats in structure or contains identifiable patterns in the data you want included

Also used to pull detail columnar data without the header and footer information

Works from the most detailed level of information to the highest

Demonstration

Importing

Excel and PDF/Text report


Field statistics and profiling data

Field Statistics with its multiple views into the data is often provides the key on how to proceed

Combining field statistics with stratification, summarization and extraction (coming up) allows you to profile the data

You need the 10,000 foot view and the detail view, often interchangeably

CAATs tools allow this profiling without having to worry too much about the mechanics


Field statistics – What else they can tell you

Field Statistics are another method to check totals and ranges on numeric, date, and character fields

You can print worksheets with field statistics information and include in your workpapers.

Demonstration

Field statistics


History – What it is

History is an audit trail or log of all tasks carried out on a database, including its import and each audit test.

Produces a linear log so that the process of how the database was created can be traced.

History is contained within the database, the IDEA Merged Document (filename.imd) and can be saved, exported, printed, and filtered.

History carries forward to child databases


History – What it tells you

1. File name

2. Number of records

3. Action Taken

4. Control Totals (if applicable)

5. IDEA script code

Basically – everything done to the file since it was imported.


What the history file looks like


Indexing and sorting

Sorting and Indexing allow you to change the order in which the records are displayed. These make specific changes to the data:

Sorting changes the physical order of the records in the database.

■ A sort is generally performed in order to speed up the processing of certain functions that change the order of the data

■ Sorting generally creates a new database. Creating multiple views through sorting will take up disk space.

■ It may be advantageous to use sorting with very large datasets for functions that rely on one or more key fields to operate: duplicate key detection, gap detection, database join and others.

Indexing does not change the physical order of the records.

■ It modifies the logical order in which records are displayed

■ Indexing does not create a new file, no matter how many times you index

■ Speeds up character searches


Why do an index?

Perform a separate analysis on a dataset without disturbing the order of the original dataset

Scan the highest/lowest values of a dataset

Identify items that are outside of pre-determined parameters

Evaluate record order in relation to process order

Demonstration

Indexing

IDEA Menu structure and functions

Indexing and sorting

Summarization and stratification

Extractions

Duplicate and gap detection

Appends and joins


Summarization

Summarization involves accumulating the value of the entity’s data for a unique key, for example, loans by customer type, losses by geographic location, sales by product type, etc.

A key can be more than one field or just part of a field

■ you can create a key field by combining values from two or more fields or pulling out part of a field

There are often instances when totals do not factor in

■ information is provided by record counts – grouping data on a particular field such as customer number, transaction type, vendor name, etc.

In IDEA, summarization menu includes statistics for numeric fields other than SUM, including MAX, MIN, Average, and Standard Deviation

Often useful as a diagnostic for determining next step in analysis


Summarization example

■ Queried cash transactions from expense reports between $20 – $25

■ Ran SUM, MAX, MIN, and AVERAGE under Analysis tab

■ Identified behavior of excessive claims for expenses under $25 documentation threshold

Demonstration

Summarization


Stratification

Stratification is the process of dividing a population into subgroups for further analysis

Creates bands based on ranges of values (normally from the minimum to the maximum values of one or more fields) and accumulating the records from the database into the appropriate bands

Stratification bands are normally based on numeric fields but in IDEA, one can also stratify on character and date fields

Conclusions or analysis may be easier to apply to population subgroups

Useful for determining comparison ranges in:

■ Vendor invoices

■ Isolate split purchase orders

Look for outliers – especially items outside the stratification bands


Stratification example

■ Used the entire disbursement file and identified each approval range under company policy

■ Ran STRATIFY under Analysis tab

■ Grouped disbursements by approval level for further testing

Demonstration

Stratification


Extraction

The Extraction task lets you to create a subset of the database which contains records that meet specific criteria for further testing and analysis apart from the rest of the database.

In IDEA, you can use Direct Extraction to perform a single extraction on a database, or up to 50 separate extractions with a single pass through the database.

Extractions in IDEA can be performed in a number of ways:

■ Saving the result of filtering the whole database based on some criteria (e.g., a query or selecting)

■ Clicking through hyperlinks representing related records from summarization, stratification, and aging

■ Using one of the extraction types provided in the ribbon

– Direct, Top Records, Key Value

Demonstration

Extraction

Direct, top records, key value


Duplicate detection

A built-in test to help you detect duplicate payments, invoices, etc., based on the comparison of one or more key fields.

IDEA allows up to eight key fields – and you can create key fields

To avoid numerous false positives, know your entity and its data

■ What is a duplicate?

■ What field or fields will act as differentiators?

■ Does this information change based on the good or service?


Duplicate detection (continued)

Can be used to find items where there should be duplicate, such as testing for matching debits and credits

The Duplicate Key Detection test runs faster on a sorted (rather than indexed) database.


Gap detection

The Gap Detection task is commonly used to test for completeness

Use it to identify missing items in a numerical sequence in Numeric or Character fields or a range of dates in Date fields in a database.

■ In character fields, it searches for gaps in numeric sequences, not letter sequences (e.g., A123…B456…etc.)

A gap indicates missing items and could include one or more missing items

Gap detection can be used to find such things as:

■ Missing check numbers or purchase orders

■ Missing invoice numbers in a sales order file

Demonstration

Duplicate detection

Gap detection


Appending and joining databases – The basics

Appends

Same data across time / geographic / organizational boundaries

Essentially puts files together end-to-endFields in the data files to append must have the same names and type (field order can be different)

Often used for combining partial files into one:

Quarterly journal entries annual file

Subsidiaries entity

Etc.

Joins

Data from different files, processes or systems

Adds fields from one file to another to add information

By definition, the key fields used for the join in each file need to be the same.

Often used for adding information from other tables to one file or perform file comparisons:

Vendor information

Upstream process information

metadata


Appending quarters into a year file

q1 sales 147q2 sales 224q3 sales 297q4 sales 232

900


Appending – Watch out for…

Fields from appended databases must have the same name and data type in all members of the append; otherwise new fields will be created in the append.

■ Invoice_nr – (character 18) and Invoice_nr (numeric) are not going to line up.

■ Invoice_num and Invoice_nr – (both character 18) are not going to line up.


Append files – When fields are not named the same

For numbers 1 and 2 at left, the file type and size is the same, but the name is slightly different. As a result, the files do not align.


Including the active database in the append file

Duplicating the original file issue in the append is no longer an issue; you are prevented from appending a file to itself


Joining databases – Why

Combine information from multiple files for better understanding

■ Vendor codes and vendor names

See relationships between datasets

■ Compare account balances across periods

Trace data through different systems

■ Is the data being modified from systems A – C?

Why not Excel’s Vlookup?

■ Factors that cause Vlookup in Excel to not return all records http://officeimg.vo.msecnd.net/en-us/files/397/327/AF102038056.pdf


Joining databases – How it works

1001 Lagos 1002 Cairo1003 New York1004 Paris

1005 Berlin1006 Sydney 1007 Toronto 1008 Durban1009 London

1004 France1004 China

1006 Australia1007 Canada 1008 South Africa1009 U.K.

1010 Brazil1011 Austria1012 Peru

Primary Secondary

All records from Primarynote that ‘1004 China’ will not be included

No matches in SecondaryNote that ‘1005 Berlin’ also will be included and no empty columns from secondary database will be included

Matches Onlynote that ‘1005 Berlin’ and ‘1004 China’ will be excluded

No matches in PrimaryNote that ‘1004 China’ will NOTbe included andempty record from primary will be add to these 2 columnsAll records in both files

All records from secondary is not included -> select secondary file as primary file


Joining databases – Result sets

All records from both files1001 Lagos 0

1002 Cairo 0

1003 New York 0

1004 Paris 1004 France

0 1004 China

1005 Berlin 0

1006 Sydney 1006 Australia

1007 Toronto 1007 Canada

1008 Durban 1008South

Africa

1009 London 1009 U.K.

0 1010 Brazil

0 1011 Austria

0 1012 Peru

All records from Primary

1001 Lagos 0

1002 Cairo 0

1003 New York 0


1005 Berlin 0




Africa

1009 London 1009 U.K.

Matches Only





Africa

1009 London 1009 U.K.

No Secondary

1001 Lagos

1002 Cairo

1003 New York

1005 BerlinNo Primary

0 1010 Brazil

0 1011 Austria

0 1012 Peru


The join history file – Tells a lot about the test

Smart analyzer

Pre-written routines for key audit tests


IDEA Add – On smart analyzer

Pre-programmed tests for key audit areas

Map to client data using simple XML interfaces

Add parameters to fit your specific testing focus or materiality’

Covers the following account types

■ General Ledger

■ Accounts Receivable

■ Accounts Payable

■ Inventory

■ Fixed Assets


Smart analyzer – Representative tests – General ledger

Out of Balance Journal Entries

Journal Entries with Specific Comments

Duplicate Journal Entries

Journal Entries with Rounded Amounts

Summary by Account Combination

Journal Entries Posted

■ On Weekends

■ On specific dates

■ At specific Times

■ By User


Smart analyzer – Representative tests – Accounts payable

■ Aging by invoice date

■ Duplicate Invoices or Payments

■ Creditors with Balances Greater than Approved Limit

■ Invoices without Purchase Order Numbers

■ Duplicate field search – Payables


Smart analyzer – How it works

The routines are pre-programmed

In order to run, the programs the following information from the user

■ What module to test (e.g., A/R, A/P, GL)

■ What tests within this module to run

Based on these answers, the program asks the user for

■ Field names from client the client file that equate to field names in the program

■ Runtime parameters (e.g., amounts to use as filters, specific dates to look at, words to search for)


Smart analyzer – GL tests which can be run


Smart analyzer – GL tests – Tagging fields


Making the actual assignment of the tags


All mandatory tags assigned

Demonstration

Smart analyzer

Visualization

Alteryx and Qlikview


Alteryx


Alteryx interface

Tool Properties

Preview

Output

Tools

Canvas


Top 10 Alteryx tools

First tool on canvas/in stream; use to import data

Split a dataset based on an expression

Group tools; container can be minimized or disabled

Generate descriptive statistics on selected fields

Summarize data by group, sum, count, average, etc.

Combine 2 or more similar data streams. Column contains all results

Last tool in stream; output results to file or database

Create or update fields using expressions

Cleanse and format time/date data

Select or deselect fields, change field type, field name


QlikView

Demonstration

Visualization


Presenter contact information

Micah Manquen

Senior Associate, Advisory ServicesInternal Audit, Risk, and Compliance

KPMG LLP | 550 S. Hope Street | Suite 1500Los Angeles, CA 90071

Mobile +1 (909)-561-4832 | [email protected]

Tim Smith

Associate Director, Global Services Centre, Audit Data and Analytics

KPMG LLP | 4747 Executive Drive | Suite 600 | San Diego, CA 92121

Direct +1 (858) 750-7196 |Mobile +1 (619) 721-9958 | [email protected]

Additional resources

User groups

Hints and tips

Planning considerations for data analytics

Notes for works cited

Reading about data and analytics

CAATs software comparison (Lanza 2004)


CAATs User groups in Southern California

ACL

IDEA Independent User Groups


Hints and tips

Planning the use of CAATs/Data Analytics

■ Audit Objective

■ Data needed (including fields)

■ Timeline for data

Practice run before year end

Testing validity of imported data

Use the routines build into your tools (i.e., macros, templates)

The more you use a tool more you will learn

If stuck, ask for help

When first doing analytics, try baby steps first before trying complex analysis


Planning considerations for data analytics

Evaluation information systems relevant to your audit objectives

■ Origination, authorization, processing, reporting

■ Consider the flow across/between systems

IT knowledge and experience of engagement team

■ important for data acquisition and in some cases, interpretation of results

Audit evidence needed

Evaluation of IT general controls

■ Reliability of the data you will be analyzing


Notes for works cited

December 2014 Issue of “Internal Auditor” The Year Ahead by Russell Jackson. Quote 1 is from Mr. Jackson in the print edition. Quotes 2 and 3 are from Harold Silverman, Vice President, Internal Audit, at The Wendy’s Co. in Dublin, Ohio, The are from embedded interview clips in the online version of ‘Internal Auditor”

CAATs tool comparisons

Richard Lanza, Comparing Best Software for Fraud Examinations: Fraud Data Interrogation Tools, Nov – Dec 2004

Limits in Excel

http://office.microsoft.com/en-us/excel-help/excel-specifications-and-limits-HP010342495.aspx

Limits in Access

http://office.microsoft.com/en-us/access-help/access-2010-specifications-HA010341462.aspx


Reading about data and analytics

Data and Analytics

Code – The Hidden Language of Computer Hardware and Software – Charles Petzold, Microsoft Press, 2000, ISBN-13: 978-0735611313

The Information: A History, A Theory, A Flood – James Gleick, Vintage 2012, ISBN-10: 1400096235 ISBN-13: 978-1400096237

Internal Audit: Efficiency Through Automation (IIA (Institute of Internal Auditors) Series) – David Coderre, IIA, 2008, ISBN-13: 978-0470392423 (author also of CAATs and other Beasts)

Statistics

Statistics in a Nutshell – Sarah Boslaugh (O’Reilly Press) ISBN-13: 978-1449316822


CAATs Software comparison (Lanza 2004)

Data Analysis Features Description ACLTM

Active Data ForExcelTM

ExcelTM IDEATM AccessTM

Aging Produces aged summaries of data based on establish cutoff dates. ✔ ✔ ✔

Append/Merge

Combines two files with identical fields into a single file. An example would be to merge two years worth of accounts payable history into one file.

✔ ✔ ✔ ✔ ✔

Calculated Field/Functions

Creates a calculated field (which can use a Field/Function such as ABS for the absolute value of Functions the field) using data within the file. For example, the net payroll pay to an employee could be recalculated using the gross pay field and deducting any withholding/taxes.

✔ ✔ ✔ ✔ ✔

Cross Tabulate

Allows you to analyze character fields by setting them in rows and columns. By cross-tabulating character fields, you can produce various summaries, explore areas of interest, and accumulate numeric fields

✔ ✔ ✔ ✔


CAATs Software comparison (Lanza 2004) (continued)




Aging Produces aged summaries of data based on establish cutoff dates. ✔ ✔ ✔

Append/Merge

Combines two files with identical fields into a single file. An example would be to merge two years worth of accounts payable history into one file.

✔ ✔ ✔ ✔ ✔

Calculated Field/Functions

Creates a calculated field (which can use a Field/Function such as ABS for the absolute value of Functions the field) using data within the file. For example, the net payroll pay to an employee could be recalculated using the gross pay field and deducting any withholding/taxes.

✔ ✔ ✔ ✔ ✔

Cross Tabulate

Allows you to analyze character fields by setting them in rows and columns. By cross-tabulating character fields, you can produce various summaries, explore areas of interest, and accumulate numeric fields

✔ ✔ ✔ ✔

Digital Analysis/Benford’s Law

Audit technology designed to find abnormal duplications of specific digits, digit combinations, specific numbers, and round numbers in corporate data. Since the objective is to find abnormal

✔ ✔ ✔






Duplicates

Identifies duplicate items within a specified field in a file. For example, this report could be used to identify duplicate billings of invoices within the sales file.

✔ ✔ ✔ ✔

Export

Creates a file in another software format (for example, Excel, Word) for testing. An example would be to export customer address information to Word for “Mail Merge”ing to customer confirmation letters.

✔ ✔ ✔ ✔ ✔

Extract/Filter

Extracts specified items from one file and copies them to another file, normally using an “if” or “where” statement. Examples include extracting all balances over a predefined limit.

✔ ✔ ✔ ✔ ✔

GapsIdentifies gaps within a specified field in a file. For example, identify any gaps in check sequence.

✔ ✔ ✔

Index/Sort

Sorts a file in ascending or descending order. An example would be sorting a file by social security number to see if any blank or “999999999” numbers exist.

✔ ✔ ✔ ✔ ✔

Join/Relate

Combines specified fields from two different files into a single file using key fields. This function is used to create relational databases on key fields. It can also be done in an unmatched fashion to identify differences between data files.

✔ ✔ ✔






Sample Creates random or monetary unit samples from a specified population. ✔ ✔

Statistics

Calculates various statistics on a selected numeric field. These may be total positive items, negative items, average balance, etc.

✔ ✔ ✔ ✔

Stratify

Counts the number and dollar value of records of a population falling within specified intervals. Stratifications also provide a useful view into the largest, smallest, and average dollar transactions.

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Summarize

Accumulates numerical values based on a specified key field. An example would be summarizing travel and entertainment expense amounts by employee to identify unusually high payment amounts.

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Date post:	22-Jul-2018
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