April 28, 2015

What is Data Analytics? How would you define it
“Analytics is the combustion engine of business, and it will be necessary for organizations that want to grow, innovate and optimize efficiency. Given its far-reaching impact, it’s one of the few software markets that thrive even in adversity”
Rita Sallam, BI Analyst, Gartner

Analytics; discovery and communication of meaningful patterns in data
Visualization
Draft
Big data; collection of large and complex data sets
Data
Financial, Operational
Structured, Unstructured,
Internal, External
Trending & comparisons
Speed and portability; available anytime, anywhere
Dashboarding
PwC

To have an effective analytics program for a complex organization, there is no “one size fits all” for technology. An analytics toolbox is necessary to bring the highest value
Draft
Core Technological Components and Key Vendors
Analytics Visualization
Lavastorm
Spotfire Qlikview Tableau
Data Management Monitoring

Tools
Capacity
Ease of use
Availability
Analytic
Capabilities
Documentation
Microsoft Excel
• 65,536 rows by
256 columns (pre ’07 versions)
• 255 chars per field
• Standard, easy to use office application
• Standard installation on PwC laptop
• Data analysis toolkit
• Built-in functions
• To be done manually by user
Microsoft
Access
•
• 2 GB database
255 fields
(columns)
• Training is required
• Standard installation on PwC laptop
•
• Built in functions
Great for joining tables
• To be done manually by user
ACL
• Unlimited
•
• Requires basic training
Menu based
• Standard installation on PwC laptop license
• Complete set of preprogrammed analysis
• Log file is documents every step continuously
Tool choice should depend on user expertise, data to analyze and analysis to be performed
Excellent Good Satisfactory Poor Very Poor
PwC

ACL
 Audit Command Language
 Point-and-click based software with ability to create executable scripts
 Commonly used for data analysis related to fraud detection among Big Four firms
ACL can be applied to assist with:
 Journal Entry Testing
 Internal Audit Testing
 Record Sampling
PwC

• Media
• Entertainment
• Retail
• Financial Services
• Insurance
• Pharmaceutical
• Healthcare
• Technology
• Industrials
• Consumer Products
PwC

Audit Support
• SAS99 Journal Entry Testing
• Testing of Unrecorded Liabilities
• AR Liquidation analysis
• Revenue Recognition Re-performance
• Vendor and Accounts Payable Analysis
• Fixed Asset Analysis
• Inventory Testing Analysis
Non-Audit Support
• Travel and Expense Investigation
• Vendor Master Data Analysis
• Inventory Analysis
• Database Development
• Process Automation
• Data Migration Validations
PwC

Analytics Methodology AVAD
A cquisition
Acquisition: Gather data from client systems; collect system business rules through interviews and available documentation
D elivery
Delivery and Documentation:
Reports, transformed data for target systems, graphic representation, models, etc.
Draft
V alidation
Validation: Assess data; ensure data adheres to business rules; reconcile
A nalysis
Analysis: Analyze data to address business issues (fraud, errors, buying patterns, target model support, etc.)
PwC

IA / BasePaks
Technology
Client Data
Why IA / BasePaks
 Repeatability, Sustainability
 Efficiency
 Reliability
Raw Data
Simplified Data Model
Draft
Standardized
Results
Standardized
Dashboards
Standardized
Reporting
Standardized
Analysis
PwC
12

Drivers of
Data
Auditing
Common
Data
Challenges
Types of
Data
Embedding
Analytics throughout the
Lifecycle
Data
Analytic
Tools and
Visualization
Benefits of
Data
Auditing
AICPA’s
Audit Data
Standards
“The transformational change occurring in organizations today is dependent upon adopting insight from data ... Everyone is now an analyst. ”
Data Science Central
How Can
Analytics
Help?
“The transformational change occurring in organizations today is dependent upon adopting insight from data ... Everyone is now an analyst. ”
Data Science Central
“Last year people stored enough data to fill 60,000 Libraries of Congress...”
The Economist
“90% of the world’s data was created in the past two years ”
IBM
“…Data volume will double over the next two years”
Gartner