1. Data Analytics Conference
June 2019

2. bios Data Analytics Group Manager, Audit Finance, Microsoft
Achievement oriented finance technology leader with solid credentials (Gold medalist and 2 times rare CFO Award winner) & 14+ years of global financial experience in setting the vision, and ability to execute on that vision by leading teams. An enthusiastic leader with organizational aptitude, deep technical and financial experience & proven ability to lead high-performing teams, build strong relationships, foster efficiency and create an impact. Recipient of Highest Award for Leadership 2018, Dale Carnegie’s “Advanced Leadership Program” and received many presentation awards in the last 2 decades and on a journey to learn it all. Currently leading Data Analytics Team for Microsoft’s Audit Group, Pooja Sund's Personal Quote is “Strive for Excellence, never be satisfied with the second best.” 
Data Science Business Intelligence Manager - Nordstrom
Rachel Nelson joined Nordstrom’s Internal Audit team in February 2016 as a Business Intelligence Manager. Rachel leads business intelligence strategy and operations and employs state of the art data science practices for continuous uncovering, review and analysis of risk and controls. Prior to joining Nordstrom, spent 9 years at Verizon Wireless where she supported various initiatives and projects to build scorecards, dashboards, reporting solutions as well as what-if scenarios and analytics for key business performance indicators.
Rachel has a Bachelor’s of Science in Management and Operation from Bellevue University. She holds more than 20 certifications which include Microsoft Excel Specialist, SQL Development, Robotic Process Automation, Alteryx Designer Core, Data Science in R and is a trained Lean Six Sigma Black Belt.

3. Highest risk and greatest value considerations when quantifying risk
Financial Impact
Operational Impact
Legal Impact
Compliance Impact
Enterprise Risk Management Risks

4. What the data tells us using the 4 types of analytics to tell a story
Diagnostic Analytics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Descriptive Analytics
what happened in the past
Diagnostic Analytics
why something happened
Predictive Analytics
predicts the future based on the past
Prescriptive Analytics
determine what to do to mitigate a risk

5. What data do I need taking what you have and identifying where you want to dig in deeper
Scientific Method
make an observation that describes a problem
create a hypothesis and key controls
test the hypothesis and key controls
draw conclusions and refine
Build data requirements on what data you need in order to test a hypothesis
When doing data science, one very important step within the scientific method is to understand the process so you can form a hypothesis. In order to understand the process, you need to partner and communicate with the business. How is the process supposed to work?
Science is the process that takes us from confusion to understanding
– Brian Green, Theoretical Physicist, Mathematician and String Theorist

6. Know your data
Demo

7. Who do I get data from? database administrator analyst data scientist
engineer/developer
data steward
report owner
Internal Sources
3rd party
open source resource
External Sources

8. Understanding the process what are the inputs, process and outputs?
Having a solid process map and understanding the flow of data will increase the value of your analysis
Questions to Consider:
How is the data transformed throughout the process? What rule exist in the process? What’s automated vs manual in the process?
Example: How inventory data moves from the moment a PO is created, to receipt of the product to distribution of the product, to the selling of a product

9. Data sourcing strategy getting the data you want
Tips and Tricks to help get the data you want: Have your hypothesis and testing method well defined – use this as your business case Build relationships with data owners throughout the company prior to needing the data Build trust by knowing the dos and don’ts of pulling data Be aware of any standard data access requests processes and start the conversation as early as possible

10. Data accuracy and completeness getting the data you want
Understand parameters used to pull the data
Date Ranges
What is filtered out of data? (WHERE statements)
How is the data aggregated? Does it make sense?
Compare to known or related reports
Confirm data types are consistent throughout the report
Size of the data
Look at min/max/count/length
Nulls/Blanks
Leading and trailing whitespaces
Duplicates/Unique values

11. Hands on workshop
Auditing Purchase Cards

12. Workshop agenda
Review of P_Card_Import Data : 6 years Data ( )
1. What risks/controls are associated with P-Cards?
A. Spending Controls –Cardholder Spending Limits (Single cardholder –not to exceed $5000), Merchant Type activities – MCC allowed
B. Split Purchases
C. State Tax does not get charged
D. Gifts, Gift Cards, Gift Certificates are not allowed
E. No Gasoline purchase
F. Service awards – not allowed
G. Moving Expenses –now allowed
2. Forming and testing controls
3. Completeness and Accuracy Check
4. Quick Analysis using Excel & Pivot Tables
5. Conclusions

13. Checking completeness and accuracy
What data is contained within each record?
Compare to known or related reports
Confirm data types are consistent throughout the report
Size of the data
Look at min/max/count/length
Nulls/Blanks
Leading and trailing whitespaces
Duplicates/Unique values

14. Test to perform and desired output
Testing of controls
Internal control
Test to perform and desired output
User shall not spend more than $50,000 per year.
Display the name and total amount spent during the year for all employees who spent more than $50,000 in Sort by the total amount spent with the larger amounts listed first.
User shall not spend more than $10,000 per month without approval.
Display the name, total amount spent during the month and the month for all employees who spent more than $10,000 per month in Sort by month (January listed first) and then total the amount spent with the larger amounts listed first.
User shall not spend more than $5,000 per transaction.
Display all transaction details (Amount, Name, Description, Vendor, TransactionDate, PostedDate and MCC) for any transaction in that was for more than $5,000. Sort by the total transaction amount.
Users shall not split purchases to evade the P-card single transaction limit of $5,000.
Test for each type of split purchase by doing the following
An amount more than $5,000 should not be split between two or more swipes of the card by the same person.
Display all transaction details where the vendor and purchaser are the same on a specific day, there is more than one transaction for the day and the combined total of the transaction was more than $5,000. Sort them in ascending order by the TransactionDate.
Purchases should not be split between two or more cardholders.
To simplify, we only will consider splitting these between two people. Display all transaction information in which the combined total for a vendor on a day was more than $5,000 and there were two different cardholders who made a purchase from that vendor (make sure the query excludes people who may have made double payments). Sort them in ascending order by the TransactionDate.

15. conclusions