utd tacua data analytics

Improving Your Data Analysis Program

1

Ray Khan

Ali Subhani,

CIA,CISA, GSNA

Speakers:

Agenda

Introduction

Benefits and Challenges

Roadmap

Real World Examples

2

Introduction

Background

PeopleSoft

IDEA

Staffing

3

7 departmental

users

2 Designated

Champions

Data Analysis Definition

Data analytics is defined as the process of

inspecting, cleaning, transforming, and

modeling data with the goal of highlighting

useful information, suggesting conclusions,

and supporting decision making.

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Source: Pune University, Vishwakarma Institute of Technology

Points of Contention

Benefits

More Comprehensive Assurance

Efficiency

Reporting

Challenges

Time

Training

Data

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Are we failing our stakeholders?

6

SOURCE : PWC 2013 State of the Internal Audit Profession Study

Plan to expand use of data analytics but

do not have a well developed plan

69 %

Data analytics are used regularly

Use of Analytics

7


Use of Analytics

8


Challenges To Developing An Analytics Program

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Roadmap

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Vision Structure Data Pull

Methodology Talking to IT

Finding Data Standard Query

Language (SQL) Basics

Developing a Process

Ready to Start

Vision

Agree on what is most important

Formal discussion with CAE

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Structure

Define a structure

Designated Analytics Champion within the

department?

OR

Each Project Manager expected to lead analytics?

Identify key contacts for each source system

Get access to data dictionary if it exists

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Data Pull Methodology

How are you going to pull Data from source systems?

From within the application?

From the database?

Open Database Connectivity (ODBC) ?

Relying on auditee to give you a file

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Data Pull: Application

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Benefit Challenge

No additional licensing cost Normally results limited to a certain

maximum number of records

Auditors do no not have to structure

SQL themselves

Can potentially „burden‟

application server

Results dependent on access

Data Pull: Database

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Benefit Challenge

Free form ability to structure SQL

allows more flexibility

Additional licensing cost

No limitation on number of records

that are pulled in

Initial buy in from IT to get read-only

access to databases.

Learning curve if unfamiliar with SQL

Data Pull: ODBC

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Benefit Challenge

Data imported directly into data

analytics tool

Limited to tables exist within the

database.

No query to create; easiest Need to get IT to create custom

views for each unique need

No cost generally

Talking to IT

Schedule a discussion

Request read-only access

Production Vs. Test Environment

Security of data

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Identify PeopleSoft Page with Data 18

Finding Data PeopleSoft

CTRL+SHIFT+J

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Query Table PSPNLFIELD

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SELECT PNLNAME,LABEL_ID,LBLTEXT,RECNAME,FIELDNAME

FROM PSPNLFIELD

WHERE PNLNAME='JOB_DATA3'


Result

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PeopleSoft Page Database Values

Finding Data Banner

Go to the form with the information

Move cursor to field you are interested in

Help menu >Dynamic Help Query

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What is SQL?

Structured Query Language

Language utilized for getting information from and

updating a database.

Can get complex ……….. BUT

3-4 main sections normally for our purposes

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SQL Basics

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SQL STATEMENT ‘ SECTION BRIEF DESCRIPTION

SELECT Defines the fields that will be displayed within the

results

FROM identifies tables where fields are stored within the

database

WHERE specifies limiting criteria (if any)

GROUP BY

ORDER BY

Groups information

Used for sorting

Sample Query

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SELECT A.EMPLID, A. DEPT, B.ADDRESS, B.ZIPCODE

FROM PS_Employee A, PS_BIO B

WHERE A.EMPLID=B.EMPLOYEE

AND A.EMPLID=123456789

Developing Data Analytics Process

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Understand Business Process

Understand How Business Process Data Stored in ERP

„Interesting‟ questions can

you answer with the data?

Pull Data

Validate you have right

sources BEFORE beginning

analysis

Engagement: Procure to Pay

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Source: “Automating the Audit” Price Waters House Coopers July 2010


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How do I start?

“Quick Wins” to gain confidence

Identify critical processes/areas for review

Rinse/Wash/Repeat

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Purchasing Card Analysis

Starting Approach

Identify Cardholders and their transactions

Review monthly limits

Determine the average expense amount

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Intermediate Approach

Identify possible split purchases

Perform analysis on MCC codes

Determine if Cardholder is active employee

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Advanced Approach

High Risks Activities (holiday travel, luxury purchases)

Keyword Search

Credit Limit Utilization

Automation

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Keywords

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Keyword Script

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(@Isini(""Barney"", Merchant_Name )

@Isini

It searches for the occurrence of a specified string or piece of text in a Character

field, Date field, or string.

Syntax

@Isini(String1, String2)

Keyword Script

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(@Isini(""Barney"", Merchant_Name) .OR. @Isini(""Bergdorf Goodman"",

Merchant_Name ).OR. @Isini(""Dicks"", Merchant_Name ).OR.

@Isini(""Dillards"", Merchant_Name ).OR. @Isini(""JCPenny"",

Merchant_Name ).OR. @Isini(""Lord & Taylor"", Merchant_Name ).OR.

@Isini(""Macy"", Merchant_Name ).OR. @Isini(""Neiman Marcus"",

Merchant_Name ).OR. @Isini(""Nordstrom"", Merchant_Name ).OR.

@Isini(""Saks Fifth"", Merchant_Name ).OR. @Isini(""Sears"",

Merchant_Name ).OR. @Isini(""Von Maur"", Merchant_Name ))

Purchasing Card Tests Developed

Consistent purchases at same vendor by one cardholder

Weekend purchases

International purchases

Dormant Cards

Purchasing Trends

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Example 1: Departmental Analytics Tool

Objective: To obtain financial and human resource information for the

audit area

Our Process:

Quarterly pull of data from PeopleSoft Financials and PeopleSoft HR.

Auditor limits data using IDEA scripts

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Deposits Expense Reimbursements

Journal Expenses

Journal Revenue

Vouchers Labor

Distributions

Critical Risk Areas

Labor Distribution

Determine where the data is located

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Labor Distribution

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Labor Distribution


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Decide the tables needed

Tables

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Review Paycheck

Pay Check Pay Earning Distributions

Account Code

Personal Data

Labor Distribution



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Determine Required Fields

Fields Required

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Pay Check

• Employee ID

• Payment End Date

• Paygroup

• Paycheck Number

Pay Earning Distributions

• Department ID

• Employee Record

• Account

• Position Number

• Jobcode

• Earnings

• Earnings Code

Account Code

• Description

• Chartfield1

• Account Code

Personal Data

• Name

Labor Distribution

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Determine Required Fields

Identify Criteria for Join

Join Criteria

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Pay Check

• Company

• Paygroup


• Off Cycle

• Page Number

• Line Number

• Separate Check

• Employee ID

Pay Beginning Distributions

• Company

• Paygroup


• Off Cycle

• Page Number

• Line Number

• Separate Check

• Account Code

Account Code

• Account Code

Personal Data

• Employee ID

Labor Distribution SQL

SELECT B.DEPTID, D.NAME, A.EMPLID, B.EMPL_RCD, C.DESCR, C.CHARTFIELD1, B.ACCOUNT,

B.POSITION_NBR, B.JOBCODE, TO_CHAR(A.PAY_END_DT,'YYYY-MM-DD'), B.EARNINGS, A.PAYGROUP,

A.PAYCHECK_NBR, B.ERNCD, C.ACCT_CD, C.FUND_CODE

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FROM PS_PAY_CHECK A, PS_PAY_ERN_DIST B, PS_ACCT_CD_TBL C, PS_PERSONAL_VW D

WHERE ( A.COMPANY = B.COMPANY

AND A.PAYGROUP = B.PAYGROUP

AND A.PAY_END_DT = B.PAY_END_DT

AND A.OFF_CYCLE = B.OFF_CYCLE

AND A.PAGE_NUM = B.PAGE_NUM

AND A.LINE_NUM = B.LINE_NUM

AND A.SEPCHK = B.SEPCHK

AND C.ACCT_CD = B.ACCT_CD

AND D.EMPLID = A.EMPLID )

Labor

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Live Demonstration

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Value Added

Easily able to focus on areas or

transactions that need more review

Consistent audit methodology regardless

of Auditor that is working on the audit

Enhanced sample selection process

Improved audit reporting

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Example 2: Return to Title IV Audit

Audit Objective: To ensure that institution was fully complying with

R2TIV regulations.

Return of financial aid funds when a recipient ceases to be enrolled

prior to the end of a payment period or period of enrollment.

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Withdrawals

Withdrawal Date

Date student began the formal withdrawal process or notified…

Mid-point, if no notification

Date of illness, accident, etc.

Beginning of an approved LOA if student does not return

Last date at an academically-related activity

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Requirements

The Institution Must:

Determine date of student‟s withdrawal

Calculate percent of period completed

Determine amount earned by applying percent completed to total of

amounts disbursed and amounts that could have been disbursed

Return unearned funds to Title IV programs, or pay student post-

withdrawal disbursement

Determine Title IV overpayment, if any

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Calculation

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Student System Background

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SQL

SELECT A.EMPLID,A.AID_YEAR,A.BGT_ITEM_CATEGORY,A.STRM,A.

BUDGET_ITEM_AMOUNT

,B.TOT_TIV_AID_RTRN,B.INST_CHRG_BOARD,B.INST_CHRG_OTHER,B.INST_CHRG_

TUIT_FEE , B.RTRN_TIV_CAL_PCT

FROM PS_STDNT_BGT_AD_VW A , PS_STDNT_RTN_TIV B

WHERE A.EMPLID=B.EMPLID AND A.AID_YEAR=B.AID_YEAR AND

A.STRM=B.STRM

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Test Performed

Validate accuracy of calculation

Verified completeness of calculations

Timeliness of calculation

Timeliness of returns

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Value Added

Highlighted progress department made in achieving compliance with

regulations

Institution able to return money to the respective programs without

being penalized during a federal review

Random sampling would not have been able to identify all potential

students with compliance issues

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Example 3: Executive Travel Background

Audit Objective: To ensure that executive travel expenses made by

executives, or on behalf of executives, were in compliance with

travel and entertainment policies and procedures

Our Process –Corporate Travel Planners (CTP) booking for flights,

hotels, and car rentals, Citibank Purchasing Card expenses,

Expense Reimbursements issued after travel

Critical Data Elements: Source Data

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Steps Performed

Step 1 – Identify University Executives

Step 2 – Obtain Source Data

Step 3 – Data Analytics

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Step 1: Identifying Executives

Determine Meaning of Executive

President, Vice President, Dean, Endowed Chair

Challenges

Payroll data title does not match actual job title

Identification of executive using title

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Title Issues

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Step 2: Obtain Source Data

Sources of Data

CitiBank Data

CTP Data

PeopleSoft Reimbursement Data

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Problems with External Data

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Formatting of Data

Missing Data from Fields

Standardization with University Data

Formatting of Data

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Missing Data

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Step 3: Data Analytics

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Compiling Completed Data

Summarizations

Visualizations

Sample Selection

Top Spenders

Trend Analysis

Standardization with University Data

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External Data University Data

Other Variants of Data

Cleaning the Data

Value Added

Improved audit planning activities

Performed analysis to identify top spending execs

Enhanced sample selection

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Expense Sums

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Top Spenders

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Key Takeaways

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Talk to your CAE

Designate Data Analytics Champion(s)

Data Pull Methodology

IT Access

Questions / Contact

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Ray Khan [email protected]

972-883-2695

Ali Subhani [email protected]

972-883-2540

mailto:[email protected]

mailto:[email protected]

utd tacua data analytics

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