1. as presented on that date, with special formatting removed
What Would Constitute the Data Component in a Business Analytics Course?
David Stephan, Baruch College, New York City (prev.)
Kathy Szabat, La Salle University, Philadelphia
DSI 2016 DASI Session, “Laying the Right Foundation for Analytics by Properly Framing the Business Problem and Getting Usable Data”
Sunday, November 20, 2016, 8:30-10 AM,
as presented on that date, with special formatting removed

2. Data in an introductory business statistics course vs
Data in an introductory business statistics course vs. a business analytics course
Comparison to instant coffee or teabag vs. expresso machine

3. What Would Constitute the Data Component in a Business Analytics Course?
Operating assumptions for such a course:
Business analytics with an emphasis on management decision-making (not data science)
Students entering course have varied backgrounds

4. Why does a Business Analytics Student Need to Know About Data?
Choosing and preparing data may be difficult because data may not be well-known or have been collected with the direct oversight of those analyzing the data
Possible complexity of data to be analyzed
Need to manipulate the data in ways not done when using basic statistical methods

5. What does a Business Analytics Student Need to Know About Data?
Concepts and skills from business statistics
Concepts and skills from information systems
Not considered today: practical skills
Related: must know problem formulation basics

6. What does a Business Analytics Student Need to Know About Data?
Problem formulation basics (from business statistics or elsewhere):
State the problem/opportunity
Specify of business objective(s)
Specify of business questions
Specify the business analytics questions (fine-tune business questions as necessary)

7. What does a Business Analytics Student Need to Know About Data?
Concepts from (introductory) business statistics such as:
Importance of operational definitions
Data cleaning
Outliers, missing values, and inconsistent values (categories)
Recoding variables
Data encoding and type

8. The Parable of the ASCII Table
DEC
HEX
BIN
Symbol
Description 00
NUL
Null char 1 01
SOH
Start of Heading 2 02
STX
Start of Text 3 03
ETX
End of Text 4 04
EOT
End of Transmission 5 05
ENQ
Enquiry 6 06
ACK
Acknowledgment 7 07
BEL
Bell 8 08 BS
Back Space 9 09 HT
Horizontal Tab 10 0A LF
Line Feed

9. Lessons from the Parable
Minimize technical descriptions of how things work (they change over time!)
Hadoop, NoSQL, MapReduce
Emphasize descriptions of how to apply things to make them work for you
Emphasize the conceptual and use non-technical examples

10. What does a Business Analytics Student Need to Know About Data?
Concepts and skills from business statistics
Concepts and skills from information systems
Not considered today: practical skills
Related: must know problem formulation basics

11. Cross-discipline Problems
One Example:
What is a “data model?”

12. Some
Data
Models

13. “Real”
Data
Models:
Some may be more complete than others

14. What does a Business Analytics Student Need to Know About Data?
Concepts from information systems curriculum?
Require/borrow a second course in IS?
ACM/AIS IS “Data & information management”
Draft MSIS 2016 (refers to IS as a bridge or foundational course); curriculum to be developed

15. What does a Business Analytics Student Need to Know About Data (from IS)?
Awareness of :
How data is organized in an information system
The ways data can be stored
The ways data can be manipulated before and during analysis

16. Specifics: Storing and Retrieving Data
Concept of a fixed record and file and its equivalence to worksheet data table of 20 rows and 10 columns
Static data led to duplication of data
Reducing duplication of stored data involves building relationships that remove, or factor out, variables and placing those removed variables in separate, new tables

17. Specifics: Connecting Data from Different Tables
There must be at least one way back that connects or links the removed variable from the original table from which it came.
“Key” concept: A new variable that uniquely identifies each row of the original table could be duplicated in the second (new) table.
How to explain this?

18. Specifics: Matching without keys
Adding keys not always an option.
What to do?

19. Specifics: Subsetting
Examine data by excluding rows or variables that contain particular values.
Determing the best level of “grain” requires understanding the business context
Basic statistical methods permit subsetting before an analysis begins; business analytics analysis permit subsetting during analysis

20. Specifics: Aggregating
Aggregating data reduces the number of rows or columns of the data being analyzed, thereby easing both data processing and computational requirements
Descriptive statistics can be used to aggregate data
Other transformations, particularly those associated with predictive analytics methods can be more abstract or mathematically complex, but also result of having fewer rows (or columns) to analyze

21. Specifics: Influence of Problem Being Analyzed
Aggregation may be driven by calculation complexity or by the nature of the business problem being analyzed.
Aggregation can also be necessary because the data being analyzed has not been collected in a way that best serves the business problem being analyzed.

22. Specifics: Sensible Manipulation
Decision-maker must be certain that the aggregating or subsetting is consistent to the business and the business problem.
Automated processes may aggregate or subset in way that make no practical sense to the decision-maker

23. Specifics: Data Retrieval and “Structure”
skipped

24. Specifics: Conceptualizing Data that is Highly Structured
skipped

25. Specifics: Is there such a thing as semistructured data?
skipped

26. Specifics: Unstructured data
To be truly unstructured, data must be values that are not comprehensible without additional interpretation
Unstructured data: Pictures, videos, and audio tracks as well as unstructured text such as product reviews posted online unstructured data
Business analytics methods are more developed for unstructured text than for other types of unstructured data.

27. “Couldn’t a IS course teach these things”
Would emphasis be the same?
The “Excel” Challenge
Would all of the “data concepts” be found/taught in a typical IS course.

28. Specifics: Training Data
skipped

29. Open Question: When should the data component be introduced?
skipped

30. Other open questions
skipped

31. What Would Constitute the Data Component in a Business Analytics Course?
Thank you!
Kathy Szabat,
David Stephan,
DSI 2016 DASI Session, Sunday, November 20, 2016, 8:30-10 AM