INFS5700 Introduction to Business Analytics
Week 1: Business Analytics in Context (T2 2022)
Details and Office Hours
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Dr. Jacky Mo
Room: Quad 2119
Phone: +61 2 9065 1481 Email:
Jacky’s consultation time – Thursday 12pm-1pm (by appointment)
➢ Itwillbeconductedvia‘WeeklyConsultationChannel’ on Teams
➢ BestwaytocommunicatewithJacky:Email
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Course Materials
• Knowledge Activities
➢ Recommended Textbook – Business Analytics: A
Management Approach
➢ Good Charts: HBR Guide to Making Smarter, More Persuasive Data Visualizations
➢ Articles/Videos from Harvard Business Publishing
• Problem Solving Activities
➢ SAS Virtual Analytics activity books that students will need to work through to be prepared for class
➢ Power BI workshop activities
➢ Several cases that will be analysed in and out of class
Course Aims
Equip students with the foundations and business knowledge needed for a career in business analytics.
• Develop skills to produce business insights and make data driven decisions, in particular use of SAS
Viya Visual Analytics and Microsoft Power BI.
Course Learning Outcomes
• Criticallyevaluatetheroleofdatainsupporting management decision-making and gaining competitive advantage.
• DiscussandevaluateBusinessAnalyticsframework, techniques and tools used in gathering, analysing and managing data and apply them to enhance decision making.
• Examinedatasetsusingvisualanalytictechniquesand communicate findings using dashboards and data driven visual reports.
• Analysetheethicalimpactofbigdataandanalyticson responsible business practices
Course Schedule
Activities
Assessment
Business Analytics in Context
Digital Business Transformation workshop
Individual Assignment Released
Data Visualization and Communication 1
SAS VA workshop 1
Data Visualization and Communication 2
SAS VA workshop 2
Data Visualization and Exploration 1
Power BI workshop 1
Individual Assignment Due
Data Visualization and Exploration 2
Power BI workshop 2
Team Assignment Released
Break week
Self-study
Business Analytics Methodology
BAM workshop
Design Thinking for Business Analytics
Design thinking workshop
Analytics and Ethics
Analytics & ethics workshop
Course review
Course review and exam preparation workshop
Team Assignment Due
Assessments (1)
Assessment Task
Occurrence
Individual Assignment
Team Assignment
Final Exam
University Exam Period
Assessments (2)
In order to pass this course, you must:
• attain an overall mark of at least 50%.
• attain a satisfactory performance in each component of the course (A mark of 45 percent or higher is normally regarded as satisfactory); and
• achieve satisfactory attendance to lectures and tutorials.
Individual Assignment (15%)
• Details of the assignment will be provided in Week 1
• Due date: 4pm Friday, 24th June (week 4)
• Designed to test ability to explore and critically evaluate the current and potential usage of analytics (e.g., opportunities, limitations, challenges associated with business analytics).
Team Assignment (30%)
• Industry Sandbox Group Project – real-world challenges proposed by industry experts from Microsoft Australia
• Details of the project will be provided by Microsoft Australia team in week 5, Tuesday 28 June 6-8pm (This session will be recorded)
• Due date: 4pm Monday, 1st August (week 10)
Communication Channel for INFS5700
Communication Channel for INFS5700
What is Business Analytics?
Type in any key terms come into your mind in relation to business analytics
Go to www.menti.com and use the code 4376 9135
Word Cloud Live Results
Definition of Business Analytics
According to a succinct and widely adopted definition provided by Davenport and Harris (2007):
Business analytics is concerned with
“the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions”.
Definition of Business Analytics
According to a succinct and widely adopted definition provided by Davenport and Harris (2007):
Business analytics is concerned with
“the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions”.
Overview of Business Analytics
Adopted from “Business Analytics: A Management Approach”
The Four V’s of Big Data
Types of Business Analytics (1)
Types of Business Analytics (2)
Descriptive Analytics
• The conventional/simplest form of data analytics.
• It seeks to provide a depiction or “summary view” of facts and figures that based on historical data.
• Simply tell “what is” and describe relationships, do not tell managers what to do.
• Example:
➢ Summarizing past events such as regional sales, customer
attrition, or success of marketing campaigns.
➢ Tabulation of social metrics such as Facebook likes, Tweets,
or followers.
Predictive Analytics
• It uses information from past to predict trends and behaviour patterns.
• It characterized by techniques such as data mining, predictive modelling, machine learning and AI.
• Example:
➢ Identify customers that are likely to abandon a service or
➢ Send marketing campaigns to customers who are most likely
Prescriptive Analytics
• It seeks to determine the best solution or outcome among various choices, given the known parameters.
• Specific techniques used include optimization, simulation and decision-analysis methods.
• Example:
➢ Determine the best set of prices and advertising frequency to
maximize revenue.
➢ Offer doctors recommendations in the best possible
treatment for a patient.
Exercise: Retail Sales Decisions
Most department stores clear seasonal inventory during the sales season (e.g., Financial year sales, Christmas sales). What insights can be derived by using each of the following analytics?
• Descriptive analytics: ?
• Predictive analytics: ?
• Prescriptive analytics: ?
Difference between Descriptive, Predictive and Prescriptive Analytics
The Analytics Process
The Analytics Process
Q1: Why are we split data into training and test dataset when developing a model?
A framework of Business Analytics in Context
Core Elements of A Business Analytics Development Function
Adopted from “Business Analytics: A Management Approach”
Core Elements of A Business Analytics Development Function
Adopted from “Business Analytics: A Management Approach”
Analytics Methodology
Cross-Industry Standard Process for data mining (CRISP-DM)
Phases of the CRISP-DM reference model (Chapman et al. 2000, p.13)
CRISP-DM Reference Model
Step 1: Business Understanding Step 2: Data Understanding Step 3: Data Preparation
Step 4: Model Building
Step 5: Testing and Evaluation Step 6: Deployment
Accounts for 80% of total project’s time
The process is highly repetitive and experimental
Step 1: Business Understanding
• What is the problem/opportunity?
• What is your goal?
• Define the business objective (are they good objectives?)
➢ Understand Customer Better
➢ Improve Customer Satisfaction
➢ Understand performance of digital marketing spend
➢ Improve customer service
Step 1: Business Understanding
• What is the problem/opportunity?
• What is your goal?
• Define the business objective (are they good objectives?)
➢ Understand Customer Better
➢ Improve Customer Satisfaction
➢ Understand performance of digital marketing spend
➢ Improve customer service
Business Objectives Need to be Actionable (good examples)
• Improve the response rate for a direct marketing campaign
• Increase the average order size
• Determine what drives customer acquisition
• Identify the risk students of the course
• Choose the right message for the right groups of customers
• Target a marketing campaign to maximize incremental value
• Recommend the next, best product for existing customers
• Segment customers by behavior
Translate Business Objectives to Business Analytics tasks
• The problem
➢ Too many students dropped from the course
• Your goal/objective
➢ Improve the retention rate of the course
• Objective for your business analytics task?
Core Elements of A Business Analytics Development Function
Modelling Techniques
What is key difference between Supervised and Unsupervised learning?
Supervised vs. Unsupervised Learning
AI, Machine Learning, and Deep Learning
Modelling Techniques Examples
Core Elements of Business Analytics Development Function
Data Scientists
Adopted from “Business Analytics: A Management Approach”
Profile of the Data Scientist
Data scientist attributes ( 2019)
Data Scientist Tasks
(Adopted from Suda 2017, p.46)
Business Analytics Applications
• Marketing analytics
• HR analytics
• Finance analytics
• Procurement analytics
Business Analytics Challenges
Adopted from “Business Analytics: A Management Approach”
Business Analytics Strategy
Next Topic: Data Visualization & Communication
• SAS Viya
Questions?
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