When Data Visualizations and Data Imports Just Don’t Work – Importing data is a dirty job as can painting user final pictures with that data. This webinar will explore the dirty little secrets that ensure data is imported completely and accurately, as well as, painting scenarios when a visualization may not be the best approach to meeting an audit objective. Specific learning objectives include: o Walk through case studies of “dirty” data and how to improve then using improved data requests and cleansing tools. o Watch case study examples of top tests to validate data tables to ensure data quality. o Discover a host of baseline tests and other baseline statistics to validate, understand and possibly extract key trends for review. o Understand visualization and dashboard types along with their associated analytical strengths from an audit perspective. o Identify situations where statistics may be more effective audit extractors than relying on the human eye to spot notable events.

1. When Data Visualizations and
Data Imports Just Don’t Work
Slide 0
About Jim Kaplan, CIA, CFE
 President and Founder of AuditNet®,
the global resource for auditors
(available on iOS, Android and
Windows devices)
 Auditor, Web Site Guru,
 Internet for Auditors Pioneer
 IIA Bradford Cadmus Memorial
Award Recipient
 Local Government Auditor’s Lifetime
Award
 Author of “The Auditor’s Guide to
Internet Resources” 2nd Edition
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2. About AuditNet® LLC
• AuditNet®, the global resource for auditors, serves the global audit
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Introductions
2
The views expressed by the presenters do not necessarily represent the views,
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presentation accompanying them, are for educational purposes only and do not
constitute accounting or legal advice or create an accountant-client relationship.
While AuditNet® makes every effort to ensure information is accurate and
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Any mention of commercial products is for information only; it does not imply
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3. AuditNet® and cRisk Academy
If you would like forever access
to this webinar recording
If you are watching the
recording, and would like to
obtain CPE credit for this
webinar
Previous AuditNet® webinars
are also available on-demand for
CPE credit
http://criskacademy.com
http://ondemand.criskacademy.com
Use coupon code: 50OFF for a
discount on this webinar for one week
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Richard B. Lanza, CPA, CFE, CGMA
• Managing Director in Innovation for Grant Thornton, LLP
• Over 25 years of ACL, Excel and other software usage
• Received the outstanding achievement in business award by the
Association of Certified Fraud Examiners for developing the publication
Proactively Detecting Fraud Using Computer Audit Reports as a
research project for the IIA
• Recently was a contributing author of:
• Detecting Corruption with Analytics: A Roadmap – The
International Institute for Analytics
• Global Technology Audit Guide (GTAG #13) Fraud In An
Automated World – Institute Of Internal Auditors.
• Cost Recovery – Turning Your Accounts Payable Department
Into A Profit Center – Wiley And Sons.
• Data Analytics: A Roadmap for Expanding Capabilities
(published 2018 in partnership with the IIA's Internal Audit
Foundation)
• In 2015, discovered a new textual analytic technique using letters
called the Lanza Approach to Letter Analytics (LALA)TM
5
The views expressed by the
presenters do not necessarily
represent the views, positions, or
opinions of Grant Thornton, LLP.
These materials, and the oral
presentation accompanyingthem,
are for educational purposes only
and do not constitute accounting
or legal advice or create an
accountant-client relationship.
rich.lanza@us.gt.com
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4. Today’s Agenda
Walk through case studies of “dirty” data and how to improve then
using improved data requests and cleansing tools.
Watch case study examples of top tests to validate data tables to
ensure data quality.
Discover a host of baseline tests and other baseline statistics to
validate, understand and possibly extract key trends for review.
Understand visualization and dashboard types along with their
associated analytical strengths from an audit perspective.
Identify situations where statistics may be more effective audit
extractors than relying on the human eye to spot notable events.
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Our perspective on the technology landscape
Source: Adapted from Forrester – Create A Road Map For A Real-Time, Agile, Self-Service Data Platform (Nov. 2017); Grant Thornton Analysis
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So Much Time, / So Little Technology
Scratch That and Reverse It!
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5. Overcoming Data Challenges
Normalizing data is 80% of the time (in the beginning)
 “By most accounts, 80 percent of the development effort in a big data project
goes into data integration and only 20 percent goes toward data analysis.” —
Intel Corporation
Data is in every process
 It may not be ERP / It may be in your “Big Data”
 90% of data is text
Audit (Internal & External) is the best partner to get the data
 They are independent / Not proving the data is a scope limitation
 Tend to establish the most secure data warehouses
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Completely Requesting Data
Slide 9
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6. Quick Process to Running
Data
1. Know your audit objectives
2. Align reports to the objectives
3. Use past reports to model /refine reports
4. Set data requirements based on reports
5. Obtain, validate, and normalize data
6. Edit scripts for data needs
7. Run reports and document results
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Audit Objectives and
Questions
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7. Process to Report Mapping
Clear Data Request
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8. Importing Various Data
Slide 14
Data Table Types (Sample)
ASCII Text
Unicode Text
EBCIDIC (IBM)
Mainframe (Cobol)
Microsoft (Excel, Access, SQL)
Quickbooks and QBO
PDF Files
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9. Data Import Exercise
Importing Various File Types
Fixed Length Fields
Delimited - Tab / CSV (Variable)
Excel (Variable)
Report (Multiple Record Fixed)
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Data Field Definition Flowchart
Is it a
date?
Do you add or
subtract the
field?
Define as a
date format
Divide by 100 or
multiply by .01
Yes
Yes
Define as a
character format
Define as a
Numeric format
Are there any
decimal places?
Yes
No
No No
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10. Unicode Considerations
Readable in Excel
Where to select the Unicode:
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PDF Conversion Options
• Free Online PDF to Excel Converter: http://www.pdftoexcelonline.com
• ABBYY PDF Transformer: http://pdftransformer.abbyy.com
• PDF2XL: http://www.cogniview.com
• Able2Extract PDF Conversion: http://www.able2extract.com
• Import Wizard: http://www.beside.com
• PDF Converter: http://www.pdfconverter.com
• Monarch: http://www.datawatch.com – Most popular, ease of use, powerful,
• Free Beta - http://www.informationactive.com/videos/misc/PrintFileScraper
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11. Free Quick Books Converter
http://bit.ly/1bm0nyT
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Working With Difficult Data
Slide 21
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12. Text Editors
Combining Tables
 PAID FILE
Copy of PAID FILE
Cleaning Tables
 Tabs, Delimiters, Titles, Etc.
• (Visualize Spaces in EditPadPro)
– www.editpadpro.com
– http://notepad-plus-plus.org/
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Text Functions
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LEFT()
With a determined set of characters in a set string, the first character
is returned from, the left of the cell
LEN()
Identifies the number of characters in a set and returns that as a
number in the cell
LOWER()
UPPER()
Lower case letters substitute upper case letters (reverse for upper)
MID()
Identifies characters in a set based on a start position and number of
characters which is determined by the user
PROPER()
Capitalizes the first letter of each character and the remaining are
lowercase
RIGHT()
Pulls the last set of character from a set and the number of character
is set by the user
TEXT()
Returns the text version of a number with a specified number format
(i.e., =TEXT(C999,"0.00") will convert 12345 into a text version of
12345.00)
TRIM()
Keeps single spaces only between words and removes all other
spaces
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13. Text Functions
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CONCATENATE()
Combines numerous strings to one stream (You can also
use the & character to combine/merge/join fields)
REPT()
Repeats a character as many times as the user wants i.e. :
=REPT (“J”,10) wll lead to ‘JJJJJJJJJJ
CHAR()
Identifies a character with a number which is presented in
the cell.This number then can be used to convert other
CHAR to the number translate code from other sources
(i.e., CHAR(127) is a nonprintable character and CHAR(9)
is a tab)
CLEAN() From a text string, it removes the nonprintable characters
SUBSTITUTE()
Works to substitute characters such as carriage returns or
other characters with blanks or valid characters for data
processing.
Date and Time Functions
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DATE() A date is created with a year, month, and day (i.e. : 2015,10,7)
DAY() By pointing to a specific field the function identifies the day
HOUR()
Identifies a hour in a time field which is set to military time (0:00-
23:00 hrs.)
MINUTE() Identifies the minutes in a time field which is set at 0-59
MONTH() Identifies the month in a time field which is set as 1=January
NOW() Sets to the actual current date and time
TODAY() Sets to the actual date
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14. Date and Time Functions
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NETWORKS ()
Identifies the ACTUAL Network days in a whole number from start to
finish dates. (weekends and holidays are excluded unless specified)
SECOND() Identifies seconds in a time field ie:0-59
WEEKDAY()
Identifies a day during a specified week in a specific timeframe.
1=Sunday
WEEKUM() Identifies a specific week number in the calendar year
YEAR() Identifies a year with a specific number i.e: 1900-9999
Normalizing a Data File /
Looking for Simple Duplication
 Identifies simple duplication in left 8 characters of a field
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15. Validating Data to Avoid
Garbage In = Garbage Out
Slide 28
Sample Data Validation – Accounts
Payable Other Questions
Make a checklist (to make sure you follow it):
Statistical analysis (totals, strata, high / low)
Agreement to batch totals, sample data and hardcopies is critical
Better to do it now instead of after the analysis
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16. Factors That
Increase Complexity / Errors
Formulas
Nested IF formulas
Populated cells
Labels
Blank cells
Cells with formula errors
Text fields that should be numeric
Hidden worksheets and cells
External links
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Automating Data Imports
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17. Process Characteristics for RPA
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Automating Data ETL
• All of the Company's data is captured in an SAP G/L
• Audit team had to budget almost 100 hours just on
importing and combining various report extracts
• Data analytics and innovation were introduced in the
current year audit
• Data import process was reduced from 25 hours
/quarter to only 2 hours/quarter
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18. Automated Data Normalization
• Store procedures for data cleanup once
• Create a normalized set of data fields named by YOU
• Ensure data quality tests are run prior to analysis
• Automate these routine tasks to increase analyst’s time
• Enrich the data by organizing it by type codes
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Automated Data Normalization
and Validation
Data Mapping to
Common Fields Are
Converted to Final
Data Spec Using
ACL Scripts
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19. Robotic Process Automation Limitations
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RPA cannot read any data that is non-electronic with unstructured inputs
• An example would be input such as paper invoices. In this case, RPA will only work with a collection of other implemented
technologies (such as OCR) required to make it digital and structured.
RPA requires some form of static consistency
• For example, invoices may be received in different formats, with fields placed in different areas. For a ‘Bot’ to be able to read an
invoice, all supplier invoices must be received in the same format with the same fields.
• Although robots can be trained by exception to read different fields, they cannot read multiple different formats – unless these are
all digital and configured separately.
RPA is not a cognitive computing solution
• It cannot learn from experience and therefore has a ‘shelf life’.
• As processes evolve – for example, through the introduction and use of other technologies — they may become redundant and
require changes.
• It is therefore wise for a company to examine the process prior to building a ‘Bot’. Applied to a process that is inefficient and/or on
the way out, that shelf life may be reduced to just a year.
Applying RPA to a broken and inefficient process will not fix it – "paving cowpaths"
• RPA is not a Business Process Management solution and does not bring an end-to-end process view
• The same goes for out of date infrastructure – RPA will only mask the underlying issues.
• Clients should focus first on addressing the root causes of their process or technology inefficiencies and then apply RPA to
maximize the benefits.
Data extends beyond
accounting systems
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Structured Data
 Accounting records
 Sub ledger details
 Monthly performance
measures
Unstructured DataUnstructured Data
 Documents (Excel, PDF)
 Emails
 Network Logs
External DataExternal Data
 Geomap Service
 OFAC, SAM.Gov Watch
Lists
 IRS Tax ID Match
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20. The Value of Visualizations
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Data analytics defined – AICPA
"The science and art of discovering and analyzing
patterns, identifying anomalies, and extracting
other useful information in data underlying or related
to the subject matter of an audit through analysis,
modeling, and visualization for the purpose of
planning or performing the audit".
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21. Exploratory vs. Confirmatory
40
Exploratory analytics Confirmatory analytics
Bottom-up and inductive Top-down and deductive
What does the data suggest is happening? Is the subject matter consistent with my model
On what assertions should we focus? Are there deviations that are individually
significant or that form a pattern?
Most useful in audit planning Most useful with substantive or controls
assurance
Dashboards, Dashbaords,
Dashboards – Oh My
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Visualizations
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22. When Visualizations Work
Trending Revenue
 Store sales were expected to decrease year over year
 One store closed
 One store had 2.3% increase overall (but that tells only
part of the story)
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Visualizations Video: https://youtu.be/l70miMymW90
Social Network Analysis
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23. How is SNA useful?
Influencers
• The classic use case for SNA is analyzing the connections between people
on Social Networking websites ( Facebook, Twitter, LinkedIn)
Fraud
• SNA can be used to gain a better understanding of fraud
• Connections of individuals can be examined for potential collusion
• Ringleaders can be discovered quickly
• Connections between known fraud records and nonfraud records
can give auditors/investigators new leads to follow
44
The Limits of Visualizations
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24. 46
Audit procedures Data Analytic approach
Recalculation Using robotic process automation to check the mathematical
accuracy of documents and records
Reperformance The continuous reperformance and testing on a 100 percent basis
(i.e. account reconciliations)
Analytical
procedures
Focused and precise analytics utilized during the planning,
substantive and concluding phases of the audit that analyze the
plausibility and predictability of a given relationship and identify
differences that could give rise to a potential misstatement (i.e.
regression, volatility)
Confirmation Obtaining a information from a third party to test a specific condition
Inspection Utilizing the process of mining event logs to inspect and corroborate
the accuracy of information
Analytics to Obtain Audit Evidence
Transactional Risk Scoring
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25. New Functions To Learn
Page 48
Focus on 2 and 3 Std
Deviations
Consider Sampling
“Strata of Z”
Math Functions
Page 49
VALUE() A number is given to identify a string now as a number
ISNA()
Returns true if the formula leads to a NA solution – This is
normally combined with IF so =IF(ISNA(C3/D3),0,(D3/D3))
AVERAGE() Is the mean of a range of cells set in the average function
MEDIAN()
Provides the middle number between the largest set value of
numbers and the lowest value of numbers
MAX() Provides the top value in a set of values
MIN() Provides the lowest value in a set of values
STDEV() Calculates the standard deviation for a range of numbers
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26. Math Functions
Page 50
ABS() Turns a negative or positive number into a positive number
MOD()
Determines whether a number is round to a divisor set by the
user (i.e. : divisor equals 1000 and all numbers where MOD() are
=0 would be round to 1000)
ROUND() Sets a number to a specified decimal place
Journal Entry Stratification
In this case, 15 of the 65
largest journal entries
make up 94% of the net
income effect
Millions of journal entries
can be compressed into a
single view.
Each of these items can
be further explored by
location, segment, and
entry process/employee.
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27. Compressing the G/L Sequences
52
EXAMPLE DATA:
1,000 Journal Entries of:
• Debit: A/R
• Credit: Revenue
The account combination is then summarized into 1 unique account sequence:
Sequence Occurrences DR CR
ACCSEQ_1 1,000 A/R Revenue
Limits of Word Clouds
Predictive Analytic Times
Slide 53
http://bit.ly/1W0CAZO
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28. The First and Last Letters
Tell the Story
• It deosn't mttaer in waht oredr the ltteers in a wrod
are, the olny iprmoetnt tihng is taht the frist and lsat
ltteer be at the rghit pclae.
54
Unstructured Text and Letter Analytics
“The Benford’s Law of Words”
55
• Same words tend to occur year over
year
• Changes may indicate some change
in the client that could affect risk
assessment
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29. Letter Analysis
56
Scatter Chart
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30. Adding Labels to a Scatter
http://bit.ly/1K02UKW - Video on the scatter macro
http://bit.ly/1GbzM1S - Macro for running the scatter
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http://gt-us.co/2I2EK8f
Questions?
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31. AuditNet® and cRisk Academy
If you would like forever access
to this webinar recording
If you are watching the
recording, and would like to
obtain CPE credit for this
webinar
Previous AuditNet® webinars
are also available on-demand for
CPE credit
http://criskacademy.com
http://ondemand.criskacademy.com
Use coupon code: 50OFF for a
discount on this webinar for one week
60
Thank You!
Jim Kaplan
AuditNet® LLC
1-800-385-1625
Email: webinars@auditnet.org
www.auditnet.org
Richard B. Lanza, CPA, CFE, CGMA
Contact Information
D: +1 732 516 5527
M: +1 732 331 3494
Email: rich.lanza@us.gt.com
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