Purpose and description of the course
Most companies have accumulated a surfeit of business data from their enterprise systems: enterprise resource planning (ERP) customer relation management (CRM), web analytics, manufacturing, industrial internet, and dedicated information systems for business functions such as finance, accounting, management, and marketing etc. Yet, only few organiszations can make extensive use of business data to drive digital transformation of their business processes and digitization of their products and services. Why so? In addition to the need of owning data of a reasonable quality, developing a solid ground for evidence-based decisions requires having high-skilled technical employees, with the advanced analytical skills and scientific rigor in modeling and interpretation of the results.
The course provides participants with a deep understanding of the nature of business data analytics, and a practical toolkit on how to transform internal (in-house) and external big datasets into business assets. Our focus is on exploring and exploiting different strategic and tactical frameworks to generate business value from in-house and external big data sets for increasing competitiveness in the local and global marketplaces.
After having attended this course, you will gain knowledge about three specific aspects of business data analytics (a) the three paradigms for generating competitive advantages and business value from new technologies, (b) the strategic framework for transforming big data sets into business assets by creating meaningful facts, actionable insights, valuable outcomes, and sustainable impacts co-developed by the Centre for Business Data Analytics (cbsBDA), Copenhagen Business School and Centre for Digital Enterprise Analytics and Leadership (DEAL) at the Ted Rogers School of Management, Ryerson University, and (c) selected illustrative examples of business data analytics and business value creation. The course furthermore focus on the importance of providing evidence to sustain managerial claims, applying an analytical process that covers all activities from problem formulation to result communication, reflection on the potential pitfalls, and how to manage or mitigate them.
Key themes in the course
- Conceptual Framework for Business Data Analytics
- Framework for Value Generation from New Technologies
- Business Data Analytics Framework
- Methods and Tools for Visual Analytics, Text Analytics and Predictive Analytics
- Pilot Project on your own company’s business data set.
Your learning outcome
In this course, you will learn to:
- Obtain conceptual knowledge about and differences between big data, business data, big data analytics and business data analytics
- Apply the framework for business value generation from new technologies to your own company
- Acquire procedural skills about methods and tools for business analytics in terms of visual, text and predictive analytics (classical statistics vs. modern algorithms)
- Analyze a business data set to generate meaningful facts, actionable insights, valuable outcomes and sustainable impacts
- Create a business analytics report, present and reflect on the learning experiences
Who should attend this course
We target managers and specialists working or interested in working with external and internal data for different business functions such as finance, accounting, strategic management, digital marketing, business analytics, research & development and customer service. The course will cover conceptual, methodological and procedural topics in and aspects of visual analytics, text analytics and predictive analytics (traditional statistics and newer computational approaches such as machine learning & deep learning). The course is also open for the professionals who would like to understand big data analytics from a business development perspective to transform internal and external big datasets into business assets.
Each day from 9.00 – 17.00
Address: CBS, Dalgas have 15, Frederiksberg 2000
Oral test based on project report in the form of a PPT. Fall 2022:
Date of submission: TBA
Date of oral defense: TBA