Annual Accounting Educator’s Conference (AAES) March 1, 2019 Pamela J. Schmidt, PhD.

1. Annual Accounting Educator’s Conference (AAES) March 1, 2019 Pamela J. Schmidt, PhD. School of Business, Washburn University Pamela.Schmidt@Washburn.edu

2. Announcement for Panel Session: Data Analytics Curriculum • Why has “Data Analysis” become the buzz word and necessity of today? Data represents facts and information which decision-makers can use to support better decisions. Evidence in form of real-time data, is useful across disciplines, in all levels of degrees and majors… Data is everywhere, vital to business success, so the most productive seek to use it effectively. Come to this panel to learn: • How to include a little data analytics into an introductory accounting course. • How are other schools integrating analytics into their courses? (at Associates, Bachelor’s and Master’s degree levels) • Does data analytics matter if my school isn’t AACSB accredited? • What resources are available? Tips on a favorite case, data set, course design, training opportunity for faculty… • Get insight and startup tips from those who have already addressed Data Analytics in Curriculum at their school: • The panelists represent diverse perspectives: community college introductory skillset, a graduate theoretical mindset and an administrative strategic approach to integration across business and accounting programs.

3. Data Analytics Curriculum Panel Session Moderator: Pamela Schmidt, Washburn University Panelists: Cheryl McConnell, Ph.D., Rockhurst University, cheryl.mcconnell@rockhurst.edu Roger McHaney, Ph.D., Kansas State University, mchaney@ksu.edu Suzanne Smith, Johnson County Community College, ssmit348@jccc.edu

4. Data used in Businesses:per Dr. Barry Devlin, 9sight Consulting “Up until the late 1990s, Data used in business originated internally from operational applications built by IT according to predefined business requirements…” Today, much of the information used by business no longer originates internally

5. Definition of Data Analytics What is Data Analytics? Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. • Datais extracted and categorized to identify and analyze behavioral data and patterns • Analysis techniques vary according to organizational requirements. • Trend and pattern visualizations • Statistical analysis • Model building and fitting • etc. Definition from Techopedia https://www.techopedia.com/definition/26418/data-analytics

6. Four Types of Data Analytics to Improve Decision Making

7. Four Types of Data Analytics • Descriptive analytics. “WHAT?” • answers the question of “what” happened. • Diagnostic analytics. “WHY?” • “why” something happened. • comparison with historical data • Predictive analytics: “What” is likely? • “what is likely” to happen. • Prescriptive analytics: “Best Action” • recommendations or “best course of action”. Majordifference between predictive and prescriptive: • Predictive analytics forecasts potential future outcomes, while • Prescriptive analytics helps you draw up specific recommendations

8. Four V’s of Big Data • Volume: Massive data scale, Unstructured • Velocity: Rapid data flow and dynamic data systems • Seek real-time analysis, automated • Variety: Diverse Data Types: • 90% unstructured: email, images, videos, IoT data streams • Veracity: Judging the accuracy or truthfulness of data without context

9. Four V’s: Data Types at High Volume and Velocity

10. 03

11. Different Analytical Methods for Different Data: Examples What is the change in risk profiles by age group over the past 6 months? What is the typical path to purchase for a policy with increased deductions? What can text based service forms tell us about potentially larger safety issues? How many customers that called Customer Service expressed a frustrated tone of voice? Which customers are highly influential on social media and regularly post about our claims service? SQL ANALYTICS PATH / TIME SERIES ANALYTICS TEXT ANALYTICS RICH MEDIA ANALYTICS GRAPH ANALYTICS

12. Deloitte’s Model: Internal Audit (AI) Analytics Audit Data Analytics: Consider … • Multidisciplinary, insights-driven audit approach • Core IA professionals working with: • data science and analytics professionals • calling on subject matter specialists • Co-developing scope, risk objectives, and approach for the internal audit • Internal auditors enhance effectiveness of the analytics. Source: Deloitte “Internal Audit Analytics: The journey to 2020- Insights-driven auditing”

13. Refreshing the Audit Approach: Embedding Analytics Integrated Data Analysis steps Source: Deloitte “Internal Audit Analytics: The journey to 2020- Insights-driven auditing”

14. Change to Analytics Mind: Different Point of View Need: Data / Analytic Centric • Identify subject area • Model data for relationships • Collect and store data • Access and cast for output • Optimize performance for often run analytics based on business value Past: Application/Process Centric • Define known data • Define access and output • Model for performance • Collect data • Transform and Store

15. Resources – See Handout Conference: AAA Intensive Data Analytics II June 10-13, 2019, Hyatt Regency Orlando Airport, FL (Register now) http://aaahq.org/Meetings/2019/SummerWorkshop2019 Conference hours are 8:00 am to 9:00 pm Monday June 10-Wednesday June 12 and 8:00 am to 6:30 pm Thursday June 13. (39 hours of CPE)

16. Questions for our Panelists ! Panelists: • Cheryl McConnell, RockhurstUniv. • Roger McHaney, KSU • Suzanne Smith, JCCC Moderator: • Pamela Schmidt, Washburn University

17. Thank you, Panelists ! Panelists: • Cheryl McConnell, RockhurstUniv. cheryl.mcconnell@rockhurst.edu • Roger McHaney, KSU mchaney@ksu.edu • Suzanne Smith, JCCC ssmit348@jccc.edu Moderator: • Pamela Schmidt, Washburn University