Analytics for Competitive Advantage

Online Certification

Michigan State University

MSU Programs > Business AnalyticsAnalytics for Competitive Advantage

Course Overview

Thought Leader: Dr. Cheri Speier-Pero

Instructor: Dr. Bronlyn Wassink

Course Objectives 

After successfully completing this course, students will be able to:

  • Determine the realities of the business analytics landscape

  • Recognize the four Vs of data

  • Recognize how analytics is transforming industries and companies

  • Define analytical modeling tools

  • Evaluate Porter's Five Forces and Value Chain from a business analytics perspective

  • Explain Search engine optimization, web analytics, and social media analytic

  • Define Supply chain management in manufacturing and logistics

  • Review Enterprise analytics and securing data

  • Review Visualization techniques used in business analytics

Students will meet the course objectives through the following actions:

  • Completing learning content pages, which includes

    • Watching videos

    • Reading text and studying charts and tables

    • Listening to audio

  • Posting to the discussion board

  • Completing eight exams

Course Requirements

 

  • Internet connection (DSL, LAN, or cable connection desirable)

  • Access to Canvas

  • Read content, watch videos, listen to audio, and complete assignments

  • Microsoft Excel

Course Structure

The content will be delivered in Canvas with a variety of media components. Students will need to be able to play video and download files if indicated.

Course Outline/Schedule

Module 1: Competing With Analytics

  • The three foundational areas of analytics

  • The evolution of analytics within the enterprise

  • The skills associated with a data scientist

  • The analytics landscape

Module 2: Using Analytics to Create Value

  • The four Vs of data

  • Different types of data sources generated by the "Internet of Things"

  • The creation and/or magnitude of change analytics can create within an industry and a firm

Module 3: Enterprise Data Management and Analytical and Modeling Tools

  • The strengths and challenges of ERP systems as an enterprise platform

  • Relational database and its strengths and weaknesses in supporting organizational data

  • The four layers of unstructured data processing

  • Tools for data storage and processing, analytical modeling, and statistical programming

  • Different algorithms that could be considered when applying data mining techniques

Module 4: Value Chain and Business Analytics

  • Porter's Five Forces model

  • Real-time monitoring of machine data

  • Computational computing could be used to support the healthcare industry

 

Module 5: Marketing/Sales and Valuation Processes

  • Analytics tools can support customer acquisition, such as search engine marketing and social media optimization

  • Search engine optimization is an important organizational capability

  • The manner in which analytics can support channel management and web analytics

  • The challenges associated with quantifying social media value

Module 6: Supply Chain, Enterprise Risk, and HR Processes

  • The elements which make up enterprise risk management

  • The analytics that support risk assessment evaluations

  • The thought leadership of the Committee of Sponsoring Organization of the Treadway Commission (COSO)

  • How your finance function can benefit from data analytics

  • Supply chain management procurement processes and analytics

  • Human resources processes and possible analytics activities to address those processes

  • Analytics projects associated with specialized business or industry processes

Module 7: Identifying and Defining Organizational Problems and Opportunities

  • The five organizational structures to consider when implementing enterprise analytics

  • The six sub-processes of the CRISP-DM model

  • The four dimensions of privacy that should be considered

  • Data breaches, wireless networks, and cloud computing

Module 8: Telling the Story With Data – Communications and Visualization

  • The role of visualization and the different ways it can be used in analytics

  • Visualization techniques to support the answers to different types of questions

  • Examples of visual displays used to support significant amounts of data

  • The role of visual data display as the primary mechanism to convey analytic findings

  • Privacy-invasive and privacy-enhancing technology

  • Software tools that can be used to support the visualization of data

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