Download Business Analytics

Panel
AACSB Business Analytics Curriculum Design
DSI 2016 - Austin, TX
Decision Sciences Institute 2016:
AACSB Business Analytics Curriculum Development
• Panelists & Objectives of the Session
• Data Analytics and “Data” in business curricula
– “Various Flavors”; Importance; Market
• Current Program Designs and Approaches
– Capabilities & Mindset; Program Types – specialty, undergraduate,
graduate, certificate, tracks, …
• Program Development Challenges
– Curriculum, Faculty, Resources, Placement
• Next Steps
Panel
AACSB Business Analytics Curriculum Design
Dan LeClair,
AACSB Executive Vice President
Michael Goul, Associate Dean for Research
W. P. Carey School of Business
Arizona State University
Paul Cronan, Professor
Sam M. Walton School of Business
University of Arkansas
Decision Sciences Institute 2016:
AACSB Business Analytics Curriculum Development
• Panelists & Objectives of the Session -- Data Analytics & ‘Data’
in business curricula –
what is this?
why?
importance …
some approaches and curricula models
challenges …
AACSB resources …
• Time has been allotted for an interactive discussion among session attendees.
Decision Sciences Institute 2016:
AACSB Business Analytics Curriculum Development
• Data and Demand for Business Analytics
• Volume, Velocity, Variety, IoT/IoE, In-Memory Computing
• Competitive Advantage
Analytics Skills Gap
• By 2018, the US alone could face a shortage of 140,000 to 190,000 people with
deep analytics skills (McKinsey Global Institute)
• There will be a 24% increase in demand for professionals with management
analysis skills over the next eight years (U.S. Bureau of Labor Statistics)
• Data scientists being hired in droves, command premium over software engineers
• 250% ROI for analytics (International Data Corporation - IDC)
• $10.66 return for each $1 spent on analytics (Nucleus Research)
• One of the biggest skills gaps on these teams today is the ability to tell the story –
someone who can take data or a statistical model and explain it to business
leaders in terms of what it means, why they should care, and what they should do
about it (Deloitte)
Data (Byte, Kilobyte, Megabyte, Gigabyte, Terabyte, Petabyte, Exabyte, Zettabyte,
Yottabyte)
<1% being analyzed
A New Approach Is Needed
to Reach and Analyze that Data
Structured Data
Analytics 1.0
Days/Hours
Unstructured Data
Analytics 2.0
Hours/Minutes/Seconds
Data Streaming at the Edge
Analytics 3.0
Seconds/Milliseconds
Decision Sciences Institute 2016:
AACSB Business Analytics Curriculum Development
• Current Program Designs and Approaches
– Capabilities & Mindset; Program Types – specialty, undergraduate,
graduate, certificate, tracks, …
Business Analytics & Education
Business
Source: Davenport, T.H. & Patil, D.J. Data Scientist: The Sexiest Job of the 21st
Century, Harvard Business Review, October (2012)
11
Analytics Education
• Convergence of skills
Math &
Statistics
Computer
Science
Business
12
Analytics Education
• OR, another view
Database
Statistical
Methods
Tools/Skills
13
Capabilities vs. Mindset
Retail
SCM
Finance
Hospitality
Marketing
Healthcare
Analytics
Capabilities
Finance/
Accounting
HR
Manufacturing
14
Business Analytics Program Design
a different view
Tools/Skills
15
Program Design - Database
Database Principles
SQL
Data Modeling
Normalization
Dimensional Modeling
Disparate Sources
Metadata – dirty data
Tools
“Big Data”
16
Program Design – Statistics/Analytics
Statistical Methods
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Cognitive
Insights
Data Mining
Sentiment Analysis
Tools
17
Program Design – Tools/Skills
Tools/Skills
Communication
Presentation
‘R’
SAS Enterprise Miner
SPSS Modeler
IBM Watson Analytics
Tools
Tableau
SAS Visual Analytics
Hadoop
SQL
18
More Comprehensive Approach
Statistics/Analytics
Database
Business
Application
Analytics
Competency
Technical
Tools
Communication
Skills
19
By learning objectives
• Analytics - foundational analytical & statistical
techniques to gather, analyze, & interpret
information – “what does the data tell us?”
• Data – store, manage, and present data for
decision making – “How do I get the data – big
data”
• Data Mining - Move beyond analytics to
knowledge discovery and data mining – “Now,
let’s use the data; putting the data to work”
20
BSc Business Analytics
Dr Michael MacDonnell
Dr. Peter Keenan
Information Systems Programs





BSBA, Information Systems Degree –
Undergraduate Concentrations
•
Business Analytics
•
Enterprise Information Systems
•
Enterprise Resource Planning
BSBA -- Interdisciplinary Minors
•
Business Analytics
•
Enterprise Resource Planning
Graduate Certificate Programs
•
Business Analytics
•
Enterprise Information Systems
•
Enterprise Resource Planning
Master of Information Systems
•
Business Analytics
•
Enterprise Information Systems
•
Enterprise Resource Planning
MS, Statistics and Analytics & MBA
•
Business Analytics Track
Business Analytics Concentration
BSBA
INFORMATION SYSTEMS
Business Analytics Concentration
 Required Courses:
Principles of Information Systems
Systems Analysis and Design
ERP Fundamentals
Business Application Development
Fundamentals
• Business Database Systems
• Business Project Development
• Concentration Courses
•
•
•
•
Concentration in
BUSINESS ANALYTICS
• Business Analytics &
Visualization
• Business Intelligence
• Practicum (recommended)
 What does the data
tell us? Insights;
Visualization;
Descriptive Analytics
 How do we manage
“data”? Data
Management; Data
Warehouse; Big Data
 How can we put the
data to work? Data
Mining; Predictive
Analytics;
Prescriptive Analytics
Graduate Certificate in Business Analytics
MASTER OF
INFORMATION SYSTEMS
Graduate Certificate in
BUSINESS ANALYTICS
• IT Toolkit*
• Decision Support &
Analytics
• Data Management
• Business Intelligence &
Knowledge Management
 Required Courses:
• Systems Development
• Data Management
• Management of IT
Seminar
• Concentration Courses
*Credit earned in the IT Toolkit
course may not be applied toward
MIS degree requirements.
 Analytics: “What does
the data tell us?” Learn
foundational analytical
and statistical techniques
to gather, analyze, and
interpret information.
 Data: “How do we
manage the data?” Learn
foundational concepts
modern data
management, data
warehousing, and big
data.
 Data Mining: “How can
we put the data to
work?” Move beyond
analytics to knowledge
discovery and data
mining.
Graduate Certificate in Business Analytics
MASTER OF SCIENCE,
STATISTICS AND ANALYTICS
Subject Core
OPERATIONAL
ANALYTICS
EDUCATIONAL
STATISTICS AND
PSYCHOMETRICS
•
•
•
•
Statistical Methods
Regression
Multivariate
Experimental Design
BUSINESS
ANALYTICS
Track Options
COMPUTATIONAL
ANALYTICS
QUANTITATIVE
SOCIAL SCIENCE
Graduate Certificate in
BUSINESS ANALYTICS
•
•
•
•
IT Toolkit*
Decision Support & Analytics
Data Management
Business Intelligence &
Knowledge Management
 Analytics: “What does the data tell
us?” Learn foundational analytical
and statistical techniques to gather,
analyze, and interpret information.
 Data: “How do we manage the
data?” Learn foundational concepts
modern data management, data
warehousing, and big data.
STATISTICS
 Data Mining: “How can we put the
data to work?” Move beyond
analytics to knowledge discovery
and data mining.
Graduate Certificate in Business Analytics
Masters of Science,
Statistics and Analytics
Business Analytics
Concentration
 Required Courses:
Master of
Information Systems
Graduate
Certificate in
BUSINESS
ANALYTICS
• IT Toolkit*
• Decision Support
& Analytics
• Data
Management
• Business
• Business Analytics Practicum Intelligence &
Knowledge
• Approved Electives
Management
• Statistical Methods
• Regression
• Multivariate Analysis
• Experimental Design
 Elective Courses:
 Required Courses:
• Systems Development
• Data Management
• Management of IT
 Elective Courses:
• Concentration
Electives
*Credit earned in the IT Toolkit
course may not be applied toward
MIS degree requirements.
 Analytics: “What
does the data tell
us?” Learn
foundational
analytical and
statistical techniques
to gather, analyze, and
interpret information.
 Data: “How do we
manage the data?”
Learn foundational
concepts modern data
management, data
warehousing, and big
data.
 Data Mining: “How
can we put the data
to work?” Move
beyond analytics to
knowledge discovery
and data mining.
Analytics Masters and
Bachelors Degree Programs
MS in Business Analytics
•
•
•
•
•
•
•
•
•
•
Introduction to Enterprise Analytics
Introduction to Applied Analytics
Data Mining I
Data-Driven Quality Management
Analytical Decision Making Tools I
Data Mining II
Analytical Decision Making Tools II
Business Analytics Strategy
Marketing Analytics
Applied Project
BS in Business Data Analytics
• Introduction to Business Data Analytics
• Big Data Analytics and Visualization in
Business
• Business Data Mining
• Business Data Warehouses and
Dimensional Modeling
• Business Database Systems Development
• Business Decision Models
• Business Information Systems
Development
• Enterprise Analytics
CONTENT MIX APPROACH &
PEDAGOGICAL METHODS
Strategy
Methodology
Technology
Business
Analytics
…
…
Applied
Projects
Lectures
Case
Studies
Handson
Exercises
Group
Projects
Quizzes
Invited
Speakers
ONSITE MSBA
9-Month Program
Fulltime / MBA-Dual / MBA-Specialization
Q1 Fall
Q2 Fall
Q3 Spring
Q4 Spring
Introduction to Enterprise
Analytics
Data Mining I
Data Mining II
Business Analytics Strategy
Analytic Decision Making II
Marketing Analytics
Introduction to Applied Analytics
Data Driven Quality Management
Applied Project
Analytic Decision Making I
- Courses are 7.5 or 15 weeks long.
- Applied Project is 15 weeks long.
R
Workshop
SAS
Workshop
App Stats
Workshop
ONLINE MSBA
16-Month Program
Spring
Introduction to
Enterprise
Analytics
Summer
Introduction to
Applied
Analytics
Data Mining I
Business
Analytics
Strategy
Analytic Decision
Making II
Analytic Decision
Making I
Data Driven
Quality
Management
Spring
Fall
Marketing
Analytics
Applied Project
Data Mining II
- Courses are 5 weeks long.
- Applied Project is 10 weeks long.
MSBA Onsite
MSBA Online
(2015-16)
• Program Length: 9 Months
• Program Length: 16 Months
• Applied Project: 15 Weeks
• Applied Project: 10 Weeks
• 150 Students => 36 Teams
• 20 Students => 6 Teams
22
2
18
Client
Projects
Public
Datasets
Kaggle
MSBA Applied Projects
Competitions
Client Projects
Project
Definition,
Deliverables,
Data
RFP
Process
NDAs
Signed
Project
Execution
Project
Deliverables
Dec
Jan
Jan
Feb - Apr
Apr
Education
Healthcare
Client Projects
Health and Fitness
Law Enforcement
Consumer Goods
Retail
Computer Software
Internet
Electronics
Visualization and Forecasting Models … to Optimize Allocation of Limited Resources
Time-Series Anomaly Detection … to Build Monitoring and Alerting Systems
Client Projects
Customer Loyalty Models and Customer Volume Forecasting Models … to Plan New Facilities
Estimating the Value of Social Media Engagement … to Optimize Content Marketing Strategy
User Behavior Analysis … to Optimize Product Features and Improve Customer Engagement
Upstream Delay Prediction … to Improve Downstream Service Levels
Recommendation Systems … to Improve Customer Propensity to Buy
Product Segmentation and Optimize Safety Stock … to Reduce Cost while Maintaining Service Levels
Customer Lifecycle Models … to Optimize Customer Acquisition Strategy
W. P. Carey MBA Core Course:
Decision Making with Data Analytics
Presents frameworks and approaches to
consume and interpret results obtained from
data analytics and equips you to recognize
patterns in data and models, recommend
actions, and implement necessary
organizational changes. Readings and case
studies address various decision-making
dilemmas and challenges facing managers in
an analytics-rich business environment.
From Rainsbotham, S., D. Kiron and P. Kirk
Prentice, “Minding the Analytics Gap” MIT
Sloan Management Review, March, 2015
Decision Sciences Institute 2016:
AACSB Business Analytics Curriculum Development
• Program Development Challenges
– Curriculum, Faculty, Resources, Placement
Resources - Everyone Wins!
IBM Academic Initiative
Microsoft Enterprise Consortium
Teradata University Network
SAP University Alliance
SAS Business Analytics
Datasets – Walmart, Acxiom, Dillard’s Department Stores,
Sam’s Club, Tyson Foods, and others
IBM – IBM SPSS Modeler, Cognos, etc.
Microsoft – Microsoft Data Analytics Tools
SAP –Business Objects, Lumira, & Predictive Analytics
SAS – Enterprise Guide, Enterprise Miner, Visual
Teradata System
Information Systems – University of Arkansas
40
Walton College recently received
several years of Walmart and Sam’s
Club transaction data
Executive M.B.A. Program
http://enterprise.waltoncollege.uark.edu
Analytics Programs:
Development Challenges and Opportunities
Michael Goul
Associate Dean for Faculty and Research
W. P. Carey School of Business
Arizona State University
CURRICULUM - Your First Decision Requires
Thinking Through the ‘Unicorn Hypothesis’
A renaissance person who can who can
single-handedly (and in a short time period)
deliver an organization’s capability to
compete on analytics
An illusive genius who possesses deep
knowledge across a wide array of disciplines
including IT, OR, MS, CS and statistics
Plus, this mythical, single-horned one is
current on best practice analytics in supply
chain, finance, auditing, marketing, etc.
To prepare them, we can just give them some
warmed over courses from several disciplines
taught by faculty who don’t otherwise cross
paths - all in a one year….
Data
Scientist
Reality Check - There Will be Some Unicorns No Matter What We Do –
But There’s a “Hydra Corollary”
[from http://www.rosebt.com]
•
Business architects: Team leaders
–
–
–
–
•
Data scientists: The top dogs in big data
–
–
–
–
•
Strong business acumen and ability to communicate with senior business leaders
and data scientists.
Develops the architecture for information management and integrating data science and
evidence based decision making.
A change agent who has great persuasion skills to get the organization - at all levels - to use data
science to make better decisions.
They have a combination of business knowledge, process experience, transformation talents,
methodology skills, and a winning personality that helps with communication and business
change management.
Many have backgrounds in math or traditional statistic
Some have experience or degrees in machine learning, artificial intelligence, natural language
processing or data management
Others are strong in the computer sciences with experience in high performance computing
architectures, data mining and designing algorithms.
Some are innovative modelers with strong business acumen.
Data architects: Programmers who are good at working with messy data, disparate types
of data, undefined data and lots of ambiguity.
–
They may be people with traditional programming or business intelligence backgrounds, and
they're often familiar with statistics.They need the creativity and persistence to be able to
harness data in new ways to create new insights.
•
Data visualizers: Technologists who translate analytics into
information a business can use. They harness the data and put it in
context, in layman's language, exploring what the data means and
how it will impact the company. They need to be able to understand
and communicate with all parts of the business, including C-level
executives.
•
Data change agents: People who drive changes in internal
operations and processes based on data analytics. They may come
from a Six Sigma background, but they also need the
communication skills to translate jargon into terms others can
understand.
•
Data engineers/operators: The designers, builders and managers of
the big data infrastructure. They develop the architecture that helps
analyze and process data in the way the business needs it. And they
make sure those systems are performing smoothly.
Politics in Your University and School/College
• Politically assess what other university/school disciplines/areas will want to own a data
science program, and what university-wide niche they will possess (CS, OR, Engineering,
Statistics, Industrial Engineering, Healthcare, etc.)
• Decide to partner vs. go-it-alone vs. stand-alone elective course(s) that can fit multidisciplinary programs vs. specializations vs. certificates vs. a full program…
• At ASU, we focused on ‘the business domain’ vs. science, engineering, etc.; IS joined forces
with supply chain management
• My advice: reject the unicorn hypothesis, but do your homework on real job listings to use
as leverage
• Engage your professional advisory board, or create an ad hoc board for analytics; the board
will provide important leverage
• Avoid professional society certification/badging until political winds settle (e.g., TDWI vs.
Informs vs. Data Management, ASA, etc.)
• Take a leadership role in identifying the future vision for university infrastructure to support
analytics
Technology Issues
1. The integration of partner (corporate, foundation, agency, etc.) data and
analytics tools with the university’s analytics infrastructure
2. University infrastructure protocols for data sharing both internally (across
disciplines) and externally
3. Approaches to data ownership
4. Approaches for realizing intellectual property from discoveries that leverage
data and analytics
5. The need for training and ongoing consulting services that faculty members
and student teams will need to be able to leverage the university’s analytics
infrastructure
6. Need for ongoing venues, pathways and discovery capabilities to find
colleagues, projects, data sets and other potential silo’d analytics resources that
can be pooled, shared, reused – and that can catalyze new
collaborations/teaching excellence
Graduate Programs:
Our Experience with Three MS Cohorts
•
•
•
•
•
•
•
•
Interdisciplinary program design fine-tuning
Duration and course length
Embrace marketing; STEM designation is important!
Manage student expectations
Expect diversity in student domain expertise
Our applied projects experiences
Placement and career management
Growth – online insights
Undergraduate Programs
• Think through the Hydra Corollary as it applies to your
situation – our experiences
– What does your industry advisory board say?
– Listen for “Scotty” vs.“Spock” perspectives in IS
– Are your region’s CIO’s dealing with a ‘throw it over the fence’
situation – or will they be dealing with it soon?
– Listen to those seeking resolution to competing methodologies
– Finance, Econ, Acc, Mkt dual majors?
Tough Questions
• Just another e-commerce?
• Have fun! – “Analytics can change business strategy”
• Is there data in our future? Let’s quickly consider some
scenarios….
Dr. Michio Kaku, professor of theoretical physics at the
City University of New York and author of “The Future of
the Mind”
• We will see the gradual transition from an Internet to a brain-net,
in which thoughts, emotions, feelings, and memories might be
transmitted instantly across the planet
• Scientists can now hook the brain to a computer and begin to
decode some of our memories and thoughts
– This might eventually revolutionize communication and even
entertainment
– The movies of the future will be able to convey emotions and feelings,
not just images on a silver screen
– Teenagers will go crazy on social media, sending memories and
sensations from their senior prom, their first date, etc.)
– Historians and writers will be able to record events not just digitally, but
also emotionally as well
• Perhaps tensions between people will diminish, as people begin to
feel and experience the pain of others
Dr. Ray Kurzweil, inventor, pioneering computer scientist, and
Director of Engineering at Google
• 3D printers will print clothing at very low cost
– There will be many free open source designs, but people will still spend money to
download clothing files from the latest hot designer just as people spend money
today for eBooks, music and movies despite all of the free material available
• 3D printers will print human organs using modified stem cells with the
patient’s own DNA providing an inexhaustible supply of organs and no
rejection issues
• We will be able to repair damaged organs with reprogrammed stem cells, for
example a heart damaged from a heart attack.
• 3D printers will print inexpensive modules to snap together a house or an
office building, lego style
• We will be able to reprogram human biology away from many diseases and
aging processes, for example deactivating cancer stem cells that are the true
source of cancer, or retard the progression of atherosclerosis, the cause of
heart disease
Dr. Anne Lise Kjaer, founder of London-based trend forecasting
agency Kjaer Global
• The World Health Organization predicts that chronic diseases will
account for almost three-quarters of all deaths worldwide by
2020, so the evolution of M-Health (mobile diagnostics, biofeedback and personal monitoring) is set to revolutionize
treatment of conditions such as diabetes and high blood pressure
• Apps designed by medical professionals will provide efficient realtime feedback, tackle chronic conditions at a much earlier stage,
and help to improve the lifestyles and life outcomes of
communities in the developed and developing world
• This improvement to our physical well-being is exciting, but what
excites me even more is the parallel development of apps that
meet our under-served mental health needs
Dr. James Canton, CEO of the San Francisco-based Institute for
Global Futures, author of “Future Smart: Managing the GameChanging Trends that will Transform Your World”
• Wearable mobile devices will blanket the world
– By 2025, there will be a massive Internet of everyone and everything linking every nation,
community, company and person to all of the world’s knowledge
– This will accelerate real-time access to education, health care, jobs, entertainment and
commerce
• Artificial intelligence becomes both as smart as and smarter than humans
– AI will be embedded in autos, robots, homes and hospitals will create the AI economy
– Humans and robots merge, digitally and physically, to treat patients who may be around the
world
– Robo-surgeons will operate remotely on patients
– RoboDocs will deliver babies and treat you over the cellphone.
• Predictive medicine transforms health care
– Early diagnosis of disease with medical devices that sniff our breath, and free DNA sequencing
that predicts our future health will be common
– Personalized genetic medicine will prevent disease, saving lives and billions in lost productivity
Jason Silva, host of National Geographic Channel’s “Brain
Games”
• The on-demand revolution will become the on-demand
world, where biological software upgrades, personalized
medicine, artificially intelligent assistants will increasingly
transform healthcare and well-being
• Additionally, increased automation will continue to make
our day-to-day lives infinitely richer
– Self-driving cars will be ubiquitous, transportation itself will be
automatic, clean, and cheap
– We will move into a world in which access trumps ownership and
the world is at our fingertips
Mark Stevenson, author of “An Optimist’s Tour of the Future”
• The technologies aren’t the most important bit — although
they are super cool
• It’s what society does with them, and right now it’s
institutional change that’s the sticking point….
• What you really want to look at, in my opinion, is new ways
of organizing ourselves
• So, my next book covers, for instance, the renewables
revolution in a small Austrian town, open source drug
discovery in India, patient networks like PatientsLikeMe
and schools that are throwing out the curriculum in order
to get on with some actual learning
Decision Sciences Institute 2016:
AACSB Business Analytics Curriculum Development
• Next Steps & Discussion
MS - Campus-Wide Analytics Tracks
Statistics
Business
Analytics
Calculus II
Data Structures
Linear Algebra
Calculus I
Operational Computation Ed Stat &
Analysis
al Analytics Psychometr
ics
Calculus I
Basic Prob & Stat
Linear Algebra
Calculus I
Basic Prob & Stat
Data Structures
Quant
Social
Science
Calculus I
Linear Algebra
Calculus I
Basic Prob & Stat
Linear Algebra
Shared Subject Matter Core (Required)
Regression
Statistical Methods
Multivariate
Experimental Design
Specified Courses
Theory of
Statistics
Statistical
Inference
Analysis Categ.
Responses
Statistical
Computation
(0 or 2)
Database
Data Mining
Simulation
Optimization I
Data Mining
Database
Data Mining
Measurement
Educational
Assessment
Multivariate II
Extensions
Time Series
Panel Data
Analysis
(2 or 4)
(1 or 3)
(2 or 4)
(2 or 4)
(0 or 2)