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

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,
Roger McHaney, Ph.D., Kansas State University,
Suzanne Smith, Johnson County Community College,

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.
Data is 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

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

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”.
Major difference between predictive and prescriptive:
Predictive analytics forecasts potential future outcomes, while
Prescriptive analytics helps you draw up specific recommendations
Descriptive analytics.
answers the question of “what” happened.
Diagnostic analytics.
historical data can be measured against other data, to uncover “why” something happened.
Predictive analytics:
“what is likely” to happen.
Prescriptive analytics
Indicates recommendations or “best course of action”.
Major difference 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: , images, videos, IoT data streams
Veracity: Judging the accuracy or truthfulness of data without context
Volume: Massive data scale, Unstructured data growing exponentially
Velocity: Rapid data flow and dynamic data systems
Seek real-time analysis, automated
Variety: Diverse Data Types:
90% of new data will be unstructured: , files, images, videos, IoT data streams
Veracity: Judging the accuracy or truthfulness of data without context
Social Media posts – social desirability, posing, self-reports

9. Four V’s: Data Types at High Volume and Velocity
Volume: Massive data scale, Unstructured data growing exponentially
Velocity: Rapid data flow and dynamic data systems
Seek real-time analysis, automated
Variety: Diverse Data Types:
90% of new data will be unstructured: , files, images, videos, IoT data streams
Veracity: Judging the accuracy or truthfulness of data without context
Social Media posts – social desirability, posing, self-reports

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?
Examples: Bus. Question – Analysis method
SQL ANALYTICS
PATH / TIME SERIES
ANALYTICS
TEXT
ANALYTICS
RICH MEDIA
ANALYTICS
GRAPH
ANALYTICS
© 2014 Teradata

12. Deloitte’s Model: Internal Audit (AI) Analytics
Audit Data Analytics: Consider …
Deloitte’s Model: Internal Audit (AI) Analytics
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 Insights-driven auditing”

13. Refreshing the Audit Approach: Embedding Analytics
Integrated Data
Analysis steps
Figure 2: Enhanced In-sights Driven Audit Methodology
Source: Deloitte “Internal Audit Analytics: The journey to Insights-driven auditing”
Source: Deloitte “Internal Audit Analytics: The journey to Insights-driven auditing”

14. Change to Analytics Mind: Different Point of View
Past: Application/Process Centric
Define known data
Define access and output
Model for performance
Collect data
Transform and Store
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

15. Resources – See Handout
Conference:
AAA Intensive Data Analytics II
June 10-13, 2019, Hyatt Regency Orlando Airport, FL (Register now)
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)
Intensive Data and Analytics II
Summer Workshop for Accounting Courses and Programs
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)
Consider adding a day before and after the workshop to your hotel reservation due to the start and end times of the workshop.
The workshop is the place for:
Faculty to focus their teaching and research talents on the why and how-tos of data and analytics.
Program leaders to focus on curricula innovation and their agility to meet the needs of an accounting profession transformed.
The multi-day workshop will feature:
D&A basics, including data models, analytic approaches, popular tools, and research methodologies.
Decision making: how to ask the right questions and answer them with data.
Implications for academics and professionals as accounting transforms.
Hands-on workshops using D&A tools for data visualization, cleansing, analyses, and emerging tech topics, i.e., Blockchain, AI.
Collaboration time with peer mentors to craft course syllabi, program innovation, and research agendas.
Peer-reviewed sessions: Master classes and Posters of D&A activities, including projects, cases, and modules that faculty can implement.

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

17. Thank you, Panelists ! Panelists:
Cheryl McConnell, Rockhurst Univ.
Roger McHaney, KSU
Suzanne Smith, JCCC
Moderator:
Pamela Schmidt, Washburn University