Data Analytics Conference June 2019

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.

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

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

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

Know your data Demo

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

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

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

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

Hands on workshop Auditing Purchase Cards

Workshop agenda Review of P_Card_Import Data : 6 years Data (2010-2015) 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

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

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 2014. 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 2014. 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 2014 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.

conclusions