Panel AACSB Business Analytics Curriculum Design DSI 2015

Panel AACSB Business Analytics Curriculum Design DSI 2015 - Seattle, WA Patrick Cullen, Associate Vice President AACSB International Michael Goul, Associate Dean for Research W. P. Carey School of Business Arizona State University Paul Cronan & David Douglas Sam M. Walton School of Business University of Arkansas

Decision Sciences Institute 2015: 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, certificate, tracks, … • Program Development Challenges – Curriculum, Faculty, Resources, Placement • Next Steps

Decision Sciences Institute 2015: 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. interactive discussion

Decision Sciences Institute 2015: AACSB Business Analytics Curriculum Development • Data and Demand for Business Analytics • Volume, Velocity, Variety, Io. T/Io. E, 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 (Mc. Kinsey 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

Current State

Decision Sciences Institute 2015: AACSB Business Analytics Curriculum Development • Current Program Designs and Approaches – Capabilities & Mindset; Program Types – specialty, undergraduate, certificate, tracks, …

BSc Business Analytics Dr Michael Mac. Donnell Dr. Peter Keenan

MS - Campus-Wide Analytics Tracks Statistics Calculus II Data Structures Linear Algebra Business Operational Computation Ed Stat & Analytics 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) (0 or 2)

Undergraduate • Business Analytics Track – ISYS 4193: – ISYS 4293: Business Analytics and Visualization Data Mining • 15 hour Business Analytics Interdisciplinary Minor Copyright © 2015 Sam M. Walton College of Business

Masters and Bachelors Degree Programs in the W. P. Carey School 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

MBA Core Course: CIS 503: Decision Making with Data Analytics Learning Outcomes • The course builds on the knowledge gained in all of the prior courses in the MBA curriculum – information management, quantitative modeling, statistical analysis and decision-making tools – and shapes a holistic view of the field. At the end of this course, students should be able to: – Use data analytics and to influence managerial decision-making at the individual, process, and enterprise levels. – Identify individual biases and traits that impede recognition, communication and implementation of data analytics driven decisions. – Develop the skills needed to identify the fluid nature of data analytics requirements in business processes to facilitate decision-making and continuous improvement. – Implement data analytics based experimentation strategies to facilitate continuous learning and improvement. – Design the various components of an analytics governance infrastructure to realize an organizational culture that values data analytics driven decisionmaking.

Decision Sciences Institute 2015: AACSB Business Analytics Curriculum Development • Program Development Challenges – Curriculum, Faculty, Resources, Placement Copyright © 2015 Sam M. Walton College of Business

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 21

Walton College recently received several years of Walmart and Sam’s Club transaction data Executive M. B. A. Program http: //enterprise. waltoncollege. uark. edu

On Analytics Programs: Development Challenges Michael Goul Associate Dean for 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 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, 7/26/2014] • 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 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 multi-disciplinary 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. ) • Build from database, OR and information management strengths • Partner with the strongest; build a joint vision, and engage faculty members who ‘get it!’

Graduate Programs: Our Experience with three MS Cohorts • • Interdisciplinary program design fine-tuning Duration and course length Embrace marketing 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? • The return of OR/Management Science? • Step 1: The killer elective – a project-based perspective; without the data, the others are just blowing smoke… • Have fun! – “Analytics can change business strategy”

Decision Sciences Institute 2015: AACSB Business Analytics Curriculum Development • Next Steps & More Discussion