SlideShare a Scribd company logo
1 of 24
CaseWare IDEA 10.3
Release Webinar
Presenters
Rohit Kundu, CIDA
Enterprise Account Executive
Jeffery Sorensen, CIA, CIDA, CISA
Industry Strategist
• Introduction
• About CaseWare
• Working with IDEA
• Introducing IDEA 10.3
Agenda
About CaseWare
27Years Experience
400,000+
Users
130
Countries
16
Languages
What’s New in IDEA 10.3
• Character Field Stats
• Duplicate Key Detection in
Visualization
• Stratified Random Sampling
• Python Integration
• IDEAScript Commands and
@Functions
• Other Enhancements
What’s New in IDEA 10.3
• IDEA 10.3 Benefits:
• One-Click Insights
• Faster Results
• Deeper Analysis
• Built for practitioners
• Raises the bar for audit analytics
IDEA 10 Release Evolution
IDEA 10
New Look and Feel /
Discover and Visualize
● Pie, Bar, Tree map charts
● Dashboards
Other
● Fuzzy duplicate detection
● Passport
IDEA 10.1
Discover and Visualize
● Scatter, Line charts
● Drill-down
● Create IDEAScript
from dashboards
Other
● Windows 10
compatibility
IDEA 10.2
Discover and Visualize
● Multi-series charts
● Dashboard reuse
SmartAnalyzer
● Merge & share apps
● Pivot tables
● Batch run of audit tests
● Dialogue to prepare VAT
data
IDEA 10.3
Discover and Visualize
● Duplicate key detection
● Chart zooming
● SmartAnalyzer tests can now
include Visualizations
Other
● Character field stats
● Python integration
● Performance improvements
● Windows Server 2016
compatibility
● SmartAnalyzer enhancements
Field stats is used to get statistics
on Numeric, Time and Date fields in
database
For example, find...
1. Earliest and latest date in a Date
field
2. Average of a Numeric field
3. Transactions on days when
business is closed
Field Stats (Prior to IDEA 10.3)
IDEA 10.3 added the ability to
generate field stats on Character
fields:
• Number of Blanks (NumBlanks)
• Number of Categories
(NumCategories)
Character Field Stats allows users to
find potentially:
• Incomplete records
• Anomalies
• Deficiencies (e.g., transactions where
there is no authorizer)
Character Field Stats
Character Field Stats
• Click on the COUNT, and drill-down into the data
• Results can be saved as their own databases and used as audit findings
Discover tool in Visualization
shows the first field it detects
with duplicate items as part of
the dashboard it builds for you
Duplicate Key Detection in Visualization
From the Field Statistics
panel, drill-down to view the
specific records
Duplicate Key Detection on Dashboards
More Duplicate Key Detection
Further customize the dashboard
to show information about other
fields with duplicate keys or
other relevant field statistic
IDEA Tech Tip
Not all duplicates are audit issues
To find the potential issues, IDEA’s Discover uses its audit intelligence
to evaluate your data and look for duplicates that are exceptions
rather than the rule.
• Stratified Random Sampling is unique to IDEA
• Users can take a more informed audit sample
• Saves time during the sampling process
• Break transactions down into strata before selecting the sample
• Ensuring the sample is truly representative of the entire population
Stratified Random Sampling
Stratified Random Sampling by %
Auditors can specify the
percentage of records within each
stratum, making the process more
effective when populations and
file sizes vary greatly
The actual percentage in the
strata may change as IDEA snaps
to the closest whole number of
records for the sample size
• IDEA 10.3 (Desktop only) includes a
Python Interpreter and key packages
to let you leverage the power of this
tool
• Extend the functionality of IDEA by
building your own advanced analytics
or use existing Python scripts
Python Integration
Python Integration
Augment your analysis by calling Python
using @Python*
*A working Python script with this name must exist in the Custom Functions
Library group
IDEAScript Commands and @Functions
RunPython
Belonging to the Client object, this command lets IDEAScript call the Python script
of your choice, after which it resumes processing in IDEAScript.
RunPythonEx
Similar to RunPython, this command lets IDEAScript call the Python script of your
choice with multiple parameters. It then resumes processing in IDEAScript.
StratifyOnBandWithPercent
Similar to StratifyOnBand, this command lets IDEAScript specify the percentage
of records to extract for each stratum.
NumCategories
This is a new way for the FieldStats object to report the number of unique
categories for a given Character field.
NumCategoriesOutputDB
This command acts as a new way for the Database object to create an instant
summarization database from the Character Field Stats of a database with field
statistics.
NumBlanks
This new command lets the FieldStats object report the number of cells with
blanks for a given Character field.
@Python
A new @Function, @Python lets auditors extend IDEA’s existing library of
@Functions with their own creations written in Python. It is ideal for Virtual fields.
@FieldStatistics
New FieldStatistics indices have been created: “110” for # of Blanks, “111” for # of
Categories.
Chart Zooming
Zoom in directly from the axis scroll bar
or by using your pointer to select
an area within the chart
• Windows Server 2016: IDEA and IDEA
Server are compatible
• Microsoft SQL Server 2016: IDEA Server
is compatible
• Citrix XenApp: 7.6 and 7.12 supported for
IDEA and IDEA Server
• UTF-8: IDEA 10.3 automatically detects
files with international characters encoded
in UTF-8 format and imports data without
the need to change the encoding of the
source files
• Fuzzy Duplicate: Supported on IDEA
Server
• Report Reader: Support imports of PDF
formats listed as PDF/A (objects within
PDF). Users now have the option to
preserve as a text file to save time in
subsequent imports
Additional Features
• Improved Performance: IDEA’s processing
engine is upgraded, reducing processing
time, depending on file size, data structure,
the nature of data and hardware used
• CaseWare Working Papers: IDEA can
send Results output to CaseWare Working
Papers 2017
• Send to Excel: Save Results outputs to the
Results Library group. Supports locally
installed spreadsheet software associated
with the .XLS extension, not just Microsoft
Office
• SmartAnalyzer Enhancements: Many
enhancements included in IDEA 10.3. IDEA
10.3 and SmartAnalyzer App SDK 3.2, can
generate apps that are FIPS compliant
Additional Features
• Not a customer? Interested in a Demo?
• salesidea@caseware.com
• 1-800-265-4332 Ext:2800
Coming Soon:
• CaseWare Cloud Analytics
• Data Analytics & Fraud Apps
• Much more - Stay tuned for 2018 release
IDEA 10.3 Additional Information
connect@caseware.com
1-800-265-4332 ext. 2800

More Related Content

What's hot

Audit: Breaking Down Barriers to Increase the Use of Data Analytics
Audit: Breaking Down Barriers to Increase the Use of Data AnalyticsAudit: Breaking Down Barriers to Increase the Use of Data Analytics
Audit: Breaking Down Barriers to Increase the Use of Data AnalyticsCaseWare IDEA
 
Presentation in ACL Connections in Atlanta - April 2013
Presentation in ACL Connections in Atlanta - April 2013Presentation in ACL Connections in Atlanta - April 2013
Presentation in ACL Connections in Atlanta - April 2013mcoello
 
Toma de decisiones impulsada por datos en radiología: Rochester Regional Heal...
Toma de decisiones impulsada por datos en radiología: Rochester Regional Heal...Toma de decisiones impulsada por datos en radiología: Rochester Regional Heal...
Toma de decisiones impulsada por datos en radiología: Rochester Regional Heal...Data IQ Argentina
 
Data Quality Dashboards
Data Quality DashboardsData Quality Dashboards
Data Quality DashboardsWilliam Sharp
 
Intro of Key Features of Soft CAAT Ent Software
Intro of Key Features of Soft CAAT Ent SoftwareIntro of Key Features of Soft CAAT Ent Software
Intro of Key Features of Soft CAAT Ent Softwarerafeq
 
SpeedTrack Tech Overview 2015
SpeedTrack Tech Overview 2015SpeedTrack Tech Overview 2015
SpeedTrack Tech Overview 2015Michael Zoltowski
 
Data Quality: Issues and Fixes
Data Quality: Issues and FixesData Quality: Issues and Fixes
Data Quality: Issues and FixesCRRC-Armenia
 
Big Data Testing Strategies
Big Data Testing StrategiesBig Data Testing Strategies
Big Data Testing StrategiesKnoldus Inc.
 
Zsolt Várnai, Principal Software Engineer at Skyscanner - "The advantages of...
 Zsolt Várnai, Principal Software Engineer at Skyscanner - "The advantages of... Zsolt Várnai, Principal Software Engineer at Skyscanner - "The advantages of...
Zsolt Várnai, Principal Software Engineer at Skyscanner - "The advantages of...Dataconomy Media
 
Big Data Berlin v8.0 Stream Processing with Apache Apex
Big Data Berlin v8.0 Stream Processing with Apache Apex Big Data Berlin v8.0 Stream Processing with Apache Apex
Big Data Berlin v8.0 Stream Processing with Apache Apex Apache Apex
 
ETIS09 - Data Quality: Common Problems & Checks - Presentation
ETIS09 -  Data Quality: Common Problems & Checks - PresentationETIS09 -  Data Quality: Common Problems & Checks - Presentation
ETIS09 - Data Quality: Common Problems & Checks - PresentationDavid Walker
 
Solution Architecture US healthcare
Solution Architecture US healthcare Solution Architecture US healthcare
Solution Architecture US healthcare sumiteshkr
 
Caseware refresher slides
Caseware refresher slidesCaseware refresher slides
Caseware refresher slidesMatthew Green
 
Using MS Excel In Your Next Audit - Top Basic & Intermediate Techniques
Using MS Excel In Your Next Audit - Top Basic & Intermediate Techniques Using MS Excel In Your Next Audit - Top Basic & Intermediate Techniques
Using MS Excel In Your Next Audit - Top Basic & Intermediate Techniques Jim Kaplan CIA CFE
 
Intro of Key Features of S-CAAT
Intro of Key Features of S-CAATIntro of Key Features of S-CAAT
Intro of Key Features of S-CAATrafeq
 
Computer aided audit techniques (CAAT) sourav mathur
Computer aided audit techniques (CAAT)  sourav mathurComputer aided audit techniques (CAAT)  sourav mathur
Computer aided audit techniques (CAAT) sourav mathursourav mathur
 
But how do I GET the data? Transparency Camp 2014
But how do I GET the data? Transparency Camp 2014But how do I GET the data? Transparency Camp 2014
But how do I GET the data? Transparency Camp 2014Jeffrey Quigley
 
Establishing a Strategy for Data Quality
Establishing a Strategy for Data QualityEstablishing a Strategy for Data Quality
Establishing a Strategy for Data QualityDatabase Answers Ltd.
 
Getting Started Using ACL in Your Next Audit
Getting Started Using ACL in Your Next AuditGetting Started Using ACL in Your Next Audit
Getting Started Using ACL in Your Next AuditJim Kaplan CIA CFE
 

What's hot (20)

Audit: Breaking Down Barriers to Increase the Use of Data Analytics
Audit: Breaking Down Barriers to Increase the Use of Data AnalyticsAudit: Breaking Down Barriers to Increase the Use of Data Analytics
Audit: Breaking Down Barriers to Increase the Use of Data Analytics
 
Ikanow oanyc summit
Ikanow oanyc summitIkanow oanyc summit
Ikanow oanyc summit
 
Presentation in ACL Connections in Atlanta - April 2013
Presentation in ACL Connections in Atlanta - April 2013Presentation in ACL Connections in Atlanta - April 2013
Presentation in ACL Connections in Atlanta - April 2013
 
Toma de decisiones impulsada por datos en radiología: Rochester Regional Heal...
Toma de decisiones impulsada por datos en radiología: Rochester Regional Heal...Toma de decisiones impulsada por datos en radiología: Rochester Regional Heal...
Toma de decisiones impulsada por datos en radiología: Rochester Regional Heal...
 
Data Quality Dashboards
Data Quality DashboardsData Quality Dashboards
Data Quality Dashboards
 
Intro of Key Features of Soft CAAT Ent Software
Intro of Key Features of Soft CAAT Ent SoftwareIntro of Key Features of Soft CAAT Ent Software
Intro of Key Features of Soft CAAT Ent Software
 
SpeedTrack Tech Overview 2015
SpeedTrack Tech Overview 2015SpeedTrack Tech Overview 2015
SpeedTrack Tech Overview 2015
 
Data Quality: Issues and Fixes
Data Quality: Issues and FixesData Quality: Issues and Fixes
Data Quality: Issues and Fixes
 
Big Data Testing Strategies
Big Data Testing StrategiesBig Data Testing Strategies
Big Data Testing Strategies
 
Zsolt Várnai, Principal Software Engineer at Skyscanner - "The advantages of...
 Zsolt Várnai, Principal Software Engineer at Skyscanner - "The advantages of... Zsolt Várnai, Principal Software Engineer at Skyscanner - "The advantages of...
Zsolt Várnai, Principal Software Engineer at Skyscanner - "The advantages of...
 
Big Data Berlin v8.0 Stream Processing with Apache Apex
Big Data Berlin v8.0 Stream Processing with Apache Apex Big Data Berlin v8.0 Stream Processing with Apache Apex
Big Data Berlin v8.0 Stream Processing with Apache Apex
 
ETIS09 - Data Quality: Common Problems & Checks - Presentation
ETIS09 -  Data Quality: Common Problems & Checks - PresentationETIS09 -  Data Quality: Common Problems & Checks - Presentation
ETIS09 - Data Quality: Common Problems & Checks - Presentation
 
Solution Architecture US healthcare
Solution Architecture US healthcare Solution Architecture US healthcare
Solution Architecture US healthcare
 
Caseware refresher slides
Caseware refresher slidesCaseware refresher slides
Caseware refresher slides
 
Using MS Excel In Your Next Audit - Top Basic & Intermediate Techniques
Using MS Excel In Your Next Audit - Top Basic & Intermediate Techniques Using MS Excel In Your Next Audit - Top Basic & Intermediate Techniques
Using MS Excel In Your Next Audit - Top Basic & Intermediate Techniques
 
Intro of Key Features of S-CAAT
Intro of Key Features of S-CAATIntro of Key Features of S-CAAT
Intro of Key Features of S-CAAT
 
Computer aided audit techniques (CAAT) sourav mathur
Computer aided audit techniques (CAAT)  sourav mathurComputer aided audit techniques (CAAT)  sourav mathur
Computer aided audit techniques (CAAT) sourav mathur
 
But how do I GET the data? Transparency Camp 2014
But how do I GET the data? Transparency Camp 2014But how do I GET the data? Transparency Camp 2014
But how do I GET the data? Transparency Camp 2014
 
Establishing a Strategy for Data Quality
Establishing a Strategy for Data QualityEstablishing a Strategy for Data Quality
Establishing a Strategy for Data Quality
 
Getting Started Using ACL in Your Next Audit
Getting Started Using ACL in Your Next AuditGetting Started Using ACL in Your Next Audit
Getting Started Using ACL in Your Next Audit
 

Similar to IDEA 10.3 Launch Webinar

QuerySurge Slide Deck for Big Data Testing Webinar
QuerySurge Slide Deck for Big Data Testing WebinarQuerySurge Slide Deck for Big Data Testing Webinar
QuerySurge Slide Deck for Big Data Testing WebinarRTTS
 
Azure Data Studio Extension Development
Azure Data Studio Extension DevelopmentAzure Data Studio Extension Development
Azure Data Studio Extension DevelopmentDrew Skwiers-Koballa
 
Webinar - DataStax Enterprise 5.1: 3X the operational analytics speed, help f...
Webinar - DataStax Enterprise 5.1: 3X the operational analytics speed, help f...Webinar - DataStax Enterprise 5.1: 3X the operational analytics speed, help f...
Webinar - DataStax Enterprise 5.1: 3X the operational analytics speed, help f...DataStax
 
Building A Product Assortment Recommendation Engine
Building A Product Assortment Recommendation EngineBuilding A Product Assortment Recommendation Engine
Building A Product Assortment Recommendation EngineDatabricks
 
PatSeer Projects Overview
PatSeer Projects OverviewPatSeer Projects Overview
PatSeer Projects OverviewGridlogics
 
II-SDV 2017: Gridlogics Technologies
II-SDV 2017: Gridlogics TechnologiesII-SDV 2017: Gridlogics Technologies
II-SDV 2017: Gridlogics TechnologiesDr. Haxel Consult
 
Building your first Analysis Services Tabular BI Semantic model with SQL Serv...
Building your first Analysis Services Tabular BI Semantic model with SQL Serv...Building your first Analysis Services Tabular BI Semantic model with SQL Serv...
Building your first Analysis Services Tabular BI Semantic model with SQL Serv...Microsoft TechNet - Belgium and Luxembourg
 
FEDSPUG April 2014: Visual Studio 2013 for Application Lifecycle Management &...
FEDSPUG April 2014: Visual Studio 2013 for Application Lifecycle Management &...FEDSPUG April 2014: Visual Studio 2013 for Application Lifecycle Management &...
FEDSPUG April 2014: Visual Studio 2013 for Application Lifecycle Management &...WSPDC & FEDSPUG
 
Architecting an Open Source AI Platform 2018 edition
Architecting an Open Source AI Platform   2018 editionArchitecting an Open Source AI Platform   2018 edition
Architecting an Open Source AI Platform 2018 editionDavid Talby
 
OSMC 2023 | Current State of Icinga by Bernd Erk
OSMC 2023 | Current State of Icinga by Bernd ErkOSMC 2023 | Current State of Icinga by Bernd Erk
OSMC 2023 | Current State of Icinga by Bernd ErkNETWAYS
 
7 steps to simplifying your AI workflows
7 steps to simplifying your AI workflows7 steps to simplifying your AI workflows
7 steps to simplifying your AI workflowsWisecube AI
 
ppt for idea (1).pptx
ppt for idea  (1).pptxppt for idea  (1).pptx
ppt for idea (1).pptxsatgup78
 
Azure Cosmos DB: Features, Practical Use and Optimization "
Azure Cosmos DB: Features, Practical Use and Optimization "Azure Cosmos DB: Features, Practical Use and Optimization "
Azure Cosmos DB: Features, Practical Use and Optimization "GlobalLogic Ukraine
 
Android developer fundamentals training overview Part II
Android developer fundamentals training overview Part IIAndroid developer fundamentals training overview Part II
Android developer fundamentals training overview Part IIYoza Aprilio
 
Microsoft Azure Big Data Analytics
Microsoft Azure Big Data AnalyticsMicrosoft Azure Big Data Analytics
Microsoft Azure Big Data AnalyticsMark Kromer
 

Similar to IDEA 10.3 Launch Webinar (20)

Mongo db 3.4 Overview
Mongo db 3.4 OverviewMongo db 3.4 Overview
Mongo db 3.4 Overview
 
Taming the shrew Power BI
Taming the shrew Power BITaming the shrew Power BI
Taming the shrew Power BI
 
QuerySurge Slide Deck for Big Data Testing Webinar
QuerySurge Slide Deck for Big Data Testing WebinarQuerySurge Slide Deck for Big Data Testing Webinar
QuerySurge Slide Deck for Big Data Testing Webinar
 
Azure Data Studio Extension Development
Azure Data Studio Extension DevelopmentAzure Data Studio Extension Development
Azure Data Studio Extension Development
 
Webinar - DataStax Enterprise 5.1: 3X the operational analytics speed, help f...
Webinar - DataStax Enterprise 5.1: 3X the operational analytics speed, help f...Webinar - DataStax Enterprise 5.1: 3X the operational analytics speed, help f...
Webinar - DataStax Enterprise 5.1: 3X the operational analytics speed, help f...
 
Building A Product Assortment Recommendation Engine
Building A Product Assortment Recommendation EngineBuilding A Product Assortment Recommendation Engine
Building A Product Assortment Recommendation Engine
 
PatSeer Projects Overview
PatSeer Projects OverviewPatSeer Projects Overview
PatSeer Projects Overview
 
II-PIC 2017 in Bangalore
II-PIC 2017 in BangaloreII-PIC 2017 in Bangalore
II-PIC 2017 in Bangalore
 
II-SDV 2017: Gridlogics Technologies
II-SDV 2017: Gridlogics TechnologiesII-SDV 2017: Gridlogics Technologies
II-SDV 2017: Gridlogics Technologies
 
Building your first Analysis Services Tabular BI Semantic model with SQL Serv...
Building your first Analysis Services Tabular BI Semantic model with SQL Serv...Building your first Analysis Services Tabular BI Semantic model with SQL Serv...
Building your first Analysis Services Tabular BI Semantic model with SQL Serv...
 
FEDSPUG April 2014: Visual Studio 2013 for Application Lifecycle Management &...
FEDSPUG April 2014: Visual Studio 2013 for Application Lifecycle Management &...FEDSPUG April 2014: Visual Studio 2013 for Application Lifecycle Management &...
FEDSPUG April 2014: Visual Studio 2013 for Application Lifecycle Management &...
 
Architecting an Open Source AI Platform 2018 edition
Architecting an Open Source AI Platform   2018 editionArchitecting an Open Source AI Platform   2018 edition
Architecting an Open Source AI Platform 2018 edition
 
OSMC 2023 | Current State of Icinga by Bernd Erk
OSMC 2023 | Current State of Icinga by Bernd ErkOSMC 2023 | Current State of Icinga by Bernd Erk
OSMC 2023 | Current State of Icinga by Bernd Erk
 
7 steps to simplifying your AI workflows
7 steps to simplifying your AI workflows7 steps to simplifying your AI workflows
7 steps to simplifying your AI workflows
 
Ow
OwOw
Ow
 
ppt for idea (1).pptx
ppt for idea  (1).pptxppt for idea  (1).pptx
ppt for idea (1).pptx
 
Azure Cosmos DB: Features, Practical Use and Optimization "
Azure Cosmos DB: Features, Practical Use and Optimization "Azure Cosmos DB: Features, Practical Use and Optimization "
Azure Cosmos DB: Features, Practical Use and Optimization "
 
Android developer fundamentals training overview Part II
Android developer fundamentals training overview Part IIAndroid developer fundamentals training overview Part II
Android developer fundamentals training overview Part II
 
Microsoft Azure Big Data Analytics
Microsoft Azure Big Data AnalyticsMicrosoft Azure Big Data Analytics
Microsoft Azure Big Data Analytics
 
Tableau
TableauTableau
Tableau
 

More from CaseWare IDEA

Auditor Sous Les Projecteurs: Marcelo Barreto Rodrigues
Auditor Sous Les Projecteurs: Marcelo Barreto Rodrigues Auditor Sous Les Projecteurs: Marcelo Barreto Rodrigues
Auditor Sous Les Projecteurs: Marcelo Barreto Rodrigues CaseWare IDEA
 
Auditor Destacado: Marcelo Barreto Rodrigues
Auditor Destacado: Marcelo Barreto Rodrigues Auditor Destacado: Marcelo Barreto Rodrigues
Auditor Destacado: Marcelo Barreto Rodrigues CaseWare IDEA
 
Auditrice Sous Les Projecteurs: Bistra Dimitrova
Auditrice Sous Les Projecteurs: Bistra Dimitrova Auditrice Sous Les Projecteurs: Bistra Dimitrova
Auditrice Sous Les Projecteurs: Bistra Dimitrova CaseWare IDEA
 
Auditor Descado - Robert Berry
Auditor Descado - Robert BerryAuditor Descado - Robert Berry
Auditor Descado - Robert BerryCaseWare IDEA
 
Auditeur sous les Projecteurs - Robert Berry
Auditeur sous les Projecteurs - Robert BerryAuditeur sous les Projecteurs - Robert Berry
Auditeur sous les Projecteurs - Robert BerryCaseWare IDEA
 
Auditor Spotlight: Robert Berry
Auditor Spotlight: Robert Berry Auditor Spotlight: Robert Berry
Auditor Spotlight: Robert Berry CaseWare IDEA
 
The Data Behind Audit Analytics
The Data Behind Audit AnalyticsThe Data Behind Audit Analytics
The Data Behind Audit AnalyticsCaseWare IDEA
 
Auditora Destacada - Anke Eckardt
Auditora Destacada - Anke EckardtAuditora Destacada - Anke Eckardt
Auditora Destacada - Anke EckardtCaseWare IDEA
 
Auditeur sous les Projecteurs - Anke Eckardt
Auditeur sous les Projecteurs - Anke EckardtAuditeur sous les Projecteurs - Anke Eckardt
Auditeur sous les Projecteurs - Anke EckardtCaseWare IDEA
 
Auditor Spotlight - Erin Baker
Auditor Spotlight - Erin BakerAuditor Spotlight - Erin Baker
Auditor Spotlight - Erin BakerCaseWare IDEA
 
Auditeur Sous Les Projecteurs: Fred Lyons
Auditeur Sous Les Projecteurs: Fred LyonsAuditeur Sous Les Projecteurs: Fred Lyons
Auditeur Sous Les Projecteurs: Fred LyonsCaseWare IDEA
 
Auditeur Sous Les Projecteurs: Erin Baker
Auditeur Sous Les Projecteurs: Erin BakerAuditeur Sous Les Projecteurs: Erin Baker
Auditeur Sous Les Projecteurs: Erin BakerCaseWare IDEA
 
Auditor Destacado - Fred Lyons
Auditor Destacado - Fred LyonsAuditor Destacado - Fred Lyons
Auditor Destacado - Fred LyonsCaseWare IDEA
 
Auditor Spotlight - Fred Lyons
Auditor Spotlight - Fred LyonsAuditor Spotlight - Fred Lyons
Auditor Spotlight - Fred LyonsCaseWare IDEA
 
The Three Lines of Defense Model & Continuous Controls Monitoring
The Three Lines of Defense Model & Continuous Controls MonitoringThe Three Lines of Defense Model & Continuous Controls Monitoring
The Three Lines of Defense Model & Continuous Controls MonitoringCaseWare IDEA
 
Integrating Data Analytics into a Risk-Based Audit Plan
Integrating Data Analytics into a Risk-Based Audit PlanIntegrating Data Analytics into a Risk-Based Audit Plan
Integrating Data Analytics into a Risk-Based Audit PlanCaseWare IDEA
 
Effective Framework for Continuous Auditing
Effective Framework for Continuous AuditingEffective Framework for Continuous Auditing
Effective Framework for Continuous AuditingCaseWare IDEA
 
Positioning Internal Audit for the Future
Positioning Internal Audit for the FuturePositioning Internal Audit for the Future
Positioning Internal Audit for the FutureCaseWare IDEA
 
Developing a Preventative and Sustainable P-card Program
Developing a Preventative and Sustainable P-card ProgramDeveloping a Preventative and Sustainable P-card Program
Developing a Preventative and Sustainable P-card ProgramCaseWare IDEA
 
Using Benford's Law for Fraud Detection and Auditing
Using Benford's Law for Fraud Detection and AuditingUsing Benford's Law for Fraud Detection and Auditing
Using Benford's Law for Fraud Detection and AuditingCaseWare IDEA
 

More from CaseWare IDEA (20)

Auditor Sous Les Projecteurs: Marcelo Barreto Rodrigues
Auditor Sous Les Projecteurs: Marcelo Barreto Rodrigues Auditor Sous Les Projecteurs: Marcelo Barreto Rodrigues
Auditor Sous Les Projecteurs: Marcelo Barreto Rodrigues
 
Auditor Destacado: Marcelo Barreto Rodrigues
Auditor Destacado: Marcelo Barreto Rodrigues Auditor Destacado: Marcelo Barreto Rodrigues
Auditor Destacado: Marcelo Barreto Rodrigues
 
Auditrice Sous Les Projecteurs: Bistra Dimitrova
Auditrice Sous Les Projecteurs: Bistra Dimitrova Auditrice Sous Les Projecteurs: Bistra Dimitrova
Auditrice Sous Les Projecteurs: Bistra Dimitrova
 
Auditor Descado - Robert Berry
Auditor Descado - Robert BerryAuditor Descado - Robert Berry
Auditor Descado - Robert Berry
 
Auditeur sous les Projecteurs - Robert Berry
Auditeur sous les Projecteurs - Robert BerryAuditeur sous les Projecteurs - Robert Berry
Auditeur sous les Projecteurs - Robert Berry
 
Auditor Spotlight: Robert Berry
Auditor Spotlight: Robert Berry Auditor Spotlight: Robert Berry
Auditor Spotlight: Robert Berry
 
The Data Behind Audit Analytics
The Data Behind Audit AnalyticsThe Data Behind Audit Analytics
The Data Behind Audit Analytics
 
Auditora Destacada - Anke Eckardt
Auditora Destacada - Anke EckardtAuditora Destacada - Anke Eckardt
Auditora Destacada - Anke Eckardt
 
Auditeur sous les Projecteurs - Anke Eckardt
Auditeur sous les Projecteurs - Anke EckardtAuditeur sous les Projecteurs - Anke Eckardt
Auditeur sous les Projecteurs - Anke Eckardt
 
Auditor Spotlight - Erin Baker
Auditor Spotlight - Erin BakerAuditor Spotlight - Erin Baker
Auditor Spotlight - Erin Baker
 
Auditeur Sous Les Projecteurs: Fred Lyons
Auditeur Sous Les Projecteurs: Fred LyonsAuditeur Sous Les Projecteurs: Fred Lyons
Auditeur Sous Les Projecteurs: Fred Lyons
 
Auditeur Sous Les Projecteurs: Erin Baker
Auditeur Sous Les Projecteurs: Erin BakerAuditeur Sous Les Projecteurs: Erin Baker
Auditeur Sous Les Projecteurs: Erin Baker
 
Auditor Destacado - Fred Lyons
Auditor Destacado - Fred LyonsAuditor Destacado - Fred Lyons
Auditor Destacado - Fred Lyons
 
Auditor Spotlight - Fred Lyons
Auditor Spotlight - Fred LyonsAuditor Spotlight - Fred Lyons
Auditor Spotlight - Fred Lyons
 
The Three Lines of Defense Model & Continuous Controls Monitoring
The Three Lines of Defense Model & Continuous Controls MonitoringThe Three Lines of Defense Model & Continuous Controls Monitoring
The Three Lines of Defense Model & Continuous Controls Monitoring
 
Integrating Data Analytics into a Risk-Based Audit Plan
Integrating Data Analytics into a Risk-Based Audit PlanIntegrating Data Analytics into a Risk-Based Audit Plan
Integrating Data Analytics into a Risk-Based Audit Plan
 
Effective Framework for Continuous Auditing
Effective Framework for Continuous AuditingEffective Framework for Continuous Auditing
Effective Framework for Continuous Auditing
 
Positioning Internal Audit for the Future
Positioning Internal Audit for the FuturePositioning Internal Audit for the Future
Positioning Internal Audit for the Future
 
Developing a Preventative and Sustainable P-card Program
Developing a Preventative and Sustainable P-card ProgramDeveloping a Preventative and Sustainable P-card Program
Developing a Preventative and Sustainable P-card Program
 
Using Benford's Law for Fraud Detection and Auditing
Using Benford's Law for Fraud Detection and AuditingUsing Benford's Law for Fraud Detection and Auditing
Using Benford's Law for Fraud Detection and Auditing
 

Recently uploaded

Amazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptx
Amazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptxAmazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptx
Amazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptxAbdelrhman abooda
 
{Pooja: 9892124323 } Call Girl in Mumbai | Jas Kaur Rate 4500 Free Hotel Del...
{Pooja:  9892124323 } Call Girl in Mumbai | Jas Kaur Rate 4500 Free Hotel Del...{Pooja:  9892124323 } Call Girl in Mumbai | Jas Kaur Rate 4500 Free Hotel Del...
{Pooja: 9892124323 } Call Girl in Mumbai | Jas Kaur Rate 4500 Free Hotel Del...Pooja Nehwal
 
DBA Basics: Getting Started with Performance Tuning.pdf
DBA Basics: Getting Started with Performance Tuning.pdfDBA Basics: Getting Started with Performance Tuning.pdf
DBA Basics: Getting Started with Performance Tuning.pdfJohn Sterrett
 
EMERCE - 2024 - AMSTERDAM - CROSS-PLATFORM TRACKING WITH GOOGLE ANALYTICS.pptx
EMERCE - 2024 - AMSTERDAM - CROSS-PLATFORM  TRACKING WITH GOOGLE ANALYTICS.pptxEMERCE - 2024 - AMSTERDAM - CROSS-PLATFORM  TRACKING WITH GOOGLE ANALYTICS.pptx
EMERCE - 2024 - AMSTERDAM - CROSS-PLATFORM TRACKING WITH GOOGLE ANALYTICS.pptxthyngster
 
代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改
代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改
代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改atducpo
 
GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]📊 Markus Baersch
 
Customer Service Analytics - Make Sense of All Your Data.pptx
Customer Service Analytics - Make Sense of All Your Data.pptxCustomer Service Analytics - Make Sense of All Your Data.pptx
Customer Service Analytics - Make Sense of All Your Data.pptxEmmanuel Dauda
 
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.pptdokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.pptSonatrach
 
Brighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data StorytellingBrighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data StorytellingNeil Barnes
 
04242024_CCC TUG_Joins and Relationships
04242024_CCC TUG_Joins and Relationships04242024_CCC TUG_Joins and Relationships
04242024_CCC TUG_Joins and Relationshipsccctableauusergroup
 
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Serviceranjana rawat
 
Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝soniya singh
 
Predictive Analysis - Using Insight-informed Data to Determine Factors Drivin...
Predictive Analysis - Using Insight-informed Data to Determine Factors Drivin...Predictive Analysis - Using Insight-informed Data to Determine Factors Drivin...
Predictive Analysis - Using Insight-informed Data to Determine Factors Drivin...ThinkInnovation
 
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130Suhani Kapoor
 
RadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfRadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfgstagge
 
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一fhwihughh
 
Industrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdfIndustrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdfLars Albertsson
 

Recently uploaded (20)

Amazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptx
Amazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptxAmazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptx
Amazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptx
 
{Pooja: 9892124323 } Call Girl in Mumbai | Jas Kaur Rate 4500 Free Hotel Del...
{Pooja:  9892124323 } Call Girl in Mumbai | Jas Kaur Rate 4500 Free Hotel Del...{Pooja:  9892124323 } Call Girl in Mumbai | Jas Kaur Rate 4500 Free Hotel Del...
{Pooja: 9892124323 } Call Girl in Mumbai | Jas Kaur Rate 4500 Free Hotel Del...
 
DBA Basics: Getting Started with Performance Tuning.pdf
DBA Basics: Getting Started with Performance Tuning.pdfDBA Basics: Getting Started with Performance Tuning.pdf
DBA Basics: Getting Started with Performance Tuning.pdf
 
EMERCE - 2024 - AMSTERDAM - CROSS-PLATFORM TRACKING WITH GOOGLE ANALYTICS.pptx
EMERCE - 2024 - AMSTERDAM - CROSS-PLATFORM  TRACKING WITH GOOGLE ANALYTICS.pptxEMERCE - 2024 - AMSTERDAM - CROSS-PLATFORM  TRACKING WITH GOOGLE ANALYTICS.pptx
EMERCE - 2024 - AMSTERDAM - CROSS-PLATFORM TRACKING WITH GOOGLE ANALYTICS.pptx
 
代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改
代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改
代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改
 
GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]
 
Customer Service Analytics - Make Sense of All Your Data.pptx
Customer Service Analytics - Make Sense of All Your Data.pptxCustomer Service Analytics - Make Sense of All Your Data.pptx
Customer Service Analytics - Make Sense of All Your Data.pptx
 
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.pptdokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
 
Brighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data StorytellingBrighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data Storytelling
 
04242024_CCC TUG_Joins and Relationships
04242024_CCC TUG_Joins and Relationships04242024_CCC TUG_Joins and Relationships
04242024_CCC TUG_Joins and Relationships
 
E-Commerce Order PredictionShraddha Kamble.pptx
E-Commerce Order PredictionShraddha Kamble.pptxE-Commerce Order PredictionShraddha Kamble.pptx
E-Commerce Order PredictionShraddha Kamble.pptx
 
VIP Call Girls Service Charbagh { Lucknow Call Girls Service 9548273370 } Boo...
VIP Call Girls Service Charbagh { Lucknow Call Girls Service 9548273370 } Boo...VIP Call Girls Service Charbagh { Lucknow Call Girls Service 9548273370 } Boo...
VIP Call Girls Service Charbagh { Lucknow Call Girls Service 9548273370 } Boo...
 
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
 
Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝
 
Predictive Analysis - Using Insight-informed Data to Determine Factors Drivin...
Predictive Analysis - Using Insight-informed Data to Determine Factors Drivin...Predictive Analysis - Using Insight-informed Data to Determine Factors Drivin...
Predictive Analysis - Using Insight-informed Data to Determine Factors Drivin...
 
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
 
RadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfRadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdf
 
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
 
Industrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdfIndustrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdf
 
Call Girls in Saket 99530🔝 56974 Escort Service
Call Girls in Saket 99530🔝 56974 Escort ServiceCall Girls in Saket 99530🔝 56974 Escort Service
Call Girls in Saket 99530🔝 56974 Escort Service
 

IDEA 10.3 Launch Webinar

  • 2. Presenters Rohit Kundu, CIDA Enterprise Account Executive Jeffery Sorensen, CIA, CIDA, CISA Industry Strategist
  • 3. • Introduction • About CaseWare • Working with IDEA • Introducing IDEA 10.3 Agenda
  • 5. What’s New in IDEA 10.3 • Character Field Stats • Duplicate Key Detection in Visualization • Stratified Random Sampling • Python Integration • IDEAScript Commands and @Functions • Other Enhancements
  • 6. What’s New in IDEA 10.3 • IDEA 10.3 Benefits: • One-Click Insights • Faster Results • Deeper Analysis • Built for practitioners • Raises the bar for audit analytics
  • 7. IDEA 10 Release Evolution IDEA 10 New Look and Feel / Discover and Visualize ● Pie, Bar, Tree map charts ● Dashboards Other ● Fuzzy duplicate detection ● Passport IDEA 10.1 Discover and Visualize ● Scatter, Line charts ● Drill-down ● Create IDEAScript from dashboards Other ● Windows 10 compatibility IDEA 10.2 Discover and Visualize ● Multi-series charts ● Dashboard reuse SmartAnalyzer ● Merge & share apps ● Pivot tables ● Batch run of audit tests ● Dialogue to prepare VAT data IDEA 10.3 Discover and Visualize ● Duplicate key detection ● Chart zooming ● SmartAnalyzer tests can now include Visualizations Other ● Character field stats ● Python integration ● Performance improvements ● Windows Server 2016 compatibility ● SmartAnalyzer enhancements
  • 8. Field stats is used to get statistics on Numeric, Time and Date fields in database For example, find... 1. Earliest and latest date in a Date field 2. Average of a Numeric field 3. Transactions on days when business is closed Field Stats (Prior to IDEA 10.3)
  • 9. IDEA 10.3 added the ability to generate field stats on Character fields: • Number of Blanks (NumBlanks) • Number of Categories (NumCategories) Character Field Stats allows users to find potentially: • Incomplete records • Anomalies • Deficiencies (e.g., transactions where there is no authorizer) Character Field Stats
  • 10. Character Field Stats • Click on the COUNT, and drill-down into the data • Results can be saved as their own databases and used as audit findings
  • 11. Discover tool in Visualization shows the first field it detects with duplicate items as part of the dashboard it builds for you Duplicate Key Detection in Visualization
  • 12. From the Field Statistics panel, drill-down to view the specific records Duplicate Key Detection on Dashboards
  • 13. More Duplicate Key Detection Further customize the dashboard to show information about other fields with duplicate keys or other relevant field statistic
  • 14. IDEA Tech Tip Not all duplicates are audit issues To find the potential issues, IDEA’s Discover uses its audit intelligence to evaluate your data and look for duplicates that are exceptions rather than the rule.
  • 15. • Stratified Random Sampling is unique to IDEA • Users can take a more informed audit sample • Saves time during the sampling process • Break transactions down into strata before selecting the sample • Ensuring the sample is truly representative of the entire population Stratified Random Sampling
  • 16. Stratified Random Sampling by % Auditors can specify the percentage of records within each stratum, making the process more effective when populations and file sizes vary greatly The actual percentage in the strata may change as IDEA snaps to the closest whole number of records for the sample size
  • 17. • IDEA 10.3 (Desktop only) includes a Python Interpreter and key packages to let you leverage the power of this tool • Extend the functionality of IDEA by building your own advanced analytics or use existing Python scripts Python Integration
  • 18. Python Integration Augment your analysis by calling Python using @Python* *A working Python script with this name must exist in the Custom Functions Library group
  • 19. IDEAScript Commands and @Functions RunPython Belonging to the Client object, this command lets IDEAScript call the Python script of your choice, after which it resumes processing in IDEAScript. RunPythonEx Similar to RunPython, this command lets IDEAScript call the Python script of your choice with multiple parameters. It then resumes processing in IDEAScript. StratifyOnBandWithPercent Similar to StratifyOnBand, this command lets IDEAScript specify the percentage of records to extract for each stratum. NumCategories This is a new way for the FieldStats object to report the number of unique categories for a given Character field. NumCategoriesOutputDB This command acts as a new way for the Database object to create an instant summarization database from the Character Field Stats of a database with field statistics. NumBlanks This new command lets the FieldStats object report the number of cells with blanks for a given Character field. @Python A new @Function, @Python lets auditors extend IDEA’s existing library of @Functions with their own creations written in Python. It is ideal for Virtual fields. @FieldStatistics New FieldStatistics indices have been created: “110” for # of Blanks, “111” for # of Categories.
  • 20. Chart Zooming Zoom in directly from the axis scroll bar or by using your pointer to select an area within the chart
  • 21. • Windows Server 2016: IDEA and IDEA Server are compatible • Microsoft SQL Server 2016: IDEA Server is compatible • Citrix XenApp: 7.6 and 7.12 supported for IDEA and IDEA Server • UTF-8: IDEA 10.3 automatically detects files with international characters encoded in UTF-8 format and imports data without the need to change the encoding of the source files • Fuzzy Duplicate: Supported on IDEA Server • Report Reader: Support imports of PDF formats listed as PDF/A (objects within PDF). Users now have the option to preserve as a text file to save time in subsequent imports Additional Features
  • 22. • Improved Performance: IDEA’s processing engine is upgraded, reducing processing time, depending on file size, data structure, the nature of data and hardware used • CaseWare Working Papers: IDEA can send Results output to CaseWare Working Papers 2017 • Send to Excel: Save Results outputs to the Results Library group. Supports locally installed spreadsheet software associated with the .XLS extension, not just Microsoft Office • SmartAnalyzer Enhancements: Many enhancements included in IDEA 10.3. IDEA 10.3 and SmartAnalyzer App SDK 3.2, can generate apps that are FIPS compliant Additional Features
  • 23. • Not a customer? Interested in a Demo? • salesidea@caseware.com • 1-800-265-4332 Ext:2800 Coming Soon: • CaseWare Cloud Analytics • Data Analytics & Fraud Apps • Much more - Stay tuned for 2018 release IDEA 10.3 Additional Information