Master-Studium Digital Business Innovation and Transformation

At the end of this module students are able to:

• illustrate the difference between standard and agile strategy development approaches,
• carry out a selected business analysis and strategy development methods,
• illustrate how to bring sustainability aspects into the process of strategy development and business analysis,
• apply a suitable approach to assess a company’s business capabilities and especially digital maturity and digitization potential (customer value or cost advantages derived from digitization),
• develop a transformation strategy and plan.

At the end of this module students are able to:

• classify and discuss different business model types, business logics and value creation mechanism,
• identify and discuss key drivers for business model innovation and performance including trends and expectations at the societal level such as the UN Sustainable Development Goals,
• explain the influence of new technologies on business models and their effects on business strategies, value creation and business ecosystems,
• apply and evaluate central concepts and terminology within the field of digital economy, digitalised businesses and digital markets,
• analyse and compare digial ecosystems by consideration power distribuion and rules, roles and hierarchies, user participation, potentials in data driven businesss modelling and predictions,
• analyse and apply business model innovation concepts and examine the transformation of value creation machanism in the context of digital technologies and processes,
• analyse and evaluate digitalisation of business processes along the industrial value chains,
• Identify and critically evaluate the economic, ecologic and social impact of different business models.

At the end of this module students are able to:

• categorise different types of innovation, and evaluate the demands they place on the innovation process,
• explain the central elements of an innovation process,
• apply foresight methods to identify future opportunities and risks,
• analyse a variety of approaches to developing innovation strategies,
• determine the efficiency and effectiveness of innovation activities using controlling instruments,
• Identify and critically evaluate the social and environmental impact of innovations.

At the end of this module students are able to:

• explain the emergence of technologicall trends and identify suitable means of measuring them,
• identify current technological trends, present key technologies behind them, and describe their possible impact on businesses,
• evaluate and forecast the development of technologies in connection to specific business areas based on suitably chosen methods,
• apply basic techniques of evaluating user experience to technology-driven innovations,
• generate and apply a suitable approach for assessing the sustainability of trends,
• explain human and sociotechnical systems (STS) principles and practically apply them in assessing the short and long term impact of digital technology applications in business and workplace settings,
• communicate and discuss analysis findings.

At the end of this module students are able to:

• apply the data analytics methods and processes to solve business problems,
• apply tools to assist in decision-making in different circumstances (e.g. high uncertanty),
• solve business problems using statistics by hand and by using a programming language (ex. R),
• define data requirements based on an application problem statement,
• apply Exploratory Data Analysis,
• analyse datasets using the right Classification, Regression and Clustering models.

• Self-assessment techniques
• Career assessment and prognosis techniques
• Goal-setting and goal-monitoring techniques
• Techniques for efficient information search and content identification
• Learning, leanring goals, and learning monitoring
• Learning agreement and learning controlling

At the end of this module students are able to:

• develop a growth strategy plan exploiting digital opportunities,
• plan global digital marketing activities to achieve a range of objectives and outline how they can be implemented,
• illustrate how new digital technologies can be applied in developing the business to increase reach and revenue,
• evaluate the digital marketing and sales maturity of companies,
• interpret marketing and sales analytic data and derive actions to optimize processes, customer experience and product development,
• develop actionable KPIs for business growth and explain how to implement and manage them,
• apply the theories of persuasion and communication in written.

Upon completion of this course students are able to:

• apply a formal modelling language to complex processes,
• analyse processes using structured methods,
• plan process improvement using structured methods,
• develop suitable indicators for process monitoring,
• describe currently available technologies for process automation and process analysis, explaining their application context, advantages and disadvantages.

Upon completion of this course students are able to:

• analyse and evaluate digital complex business processes along the value chain, selecting and applying suitable methods and evaluating findings from them,
• determine unused potential that could be leveraged using alternative digital processes and structures,
• design a suitable digitalisation approaches and plan their implementation,
• communicate and discuss analysis findings.

At the end of this module students are able to:

• define, distinguish between and apply traditional and agile project management, and describe the attributes of each,
• select and apply modern management techniques suited to a particular situation to support strategy development, strategy communication and successful performance improvement,
• describe the core aspects of a transformation process and analyse various change management approaches,
• identify and critically evaluate sustainability and ethical issues pertaining to organisational transformations,
• develop actionable strategies to master the challenge of a dynamic environment and to drive change at different levels of the organisation,
• apply change and transformation models to digital transformation,
• name conflicts and contribute constructively to clarification as well as give constructive critique to team members and stand by their own mistakes,
• apply persuasion techniques in role plays.

At the end of this module students are able to:

• apply and adapt the iterative human centred process to innovation projects,
• select, evaluate, adapt and apply the appropriate methods related to the stages of innovation projects,
• evaluate the market desirability, the technical feasibility and business viability of the innovation,
• evaluate sustainable as well as ethical, legal and security implications of digitalization and innovation,
• plan and distribute work in the course of the capstone project among the group,
• manage their working time effectively.

• Business Layer· Business drivers, goals and objectives
  • Capability-Based Planning
  • Operational excellence, enhance customer/user experience and new business models
• Data Layer
  • Types of Data (structured/unstructured, master/transactional, historical/real-time, internal/external) – Data in use, at rest and in transit
  • Data Value Chain & Life-cycle (CRISP-DM). The Data Value Map
  • Databases (relational, non-relational), Enterprise data hubs: Data warehouse, Data lake
  • The concept of BIG Data (areas of applicability, storage & analysis architectures and technologies)
  • Data Governance, Data Management and Data Protection
  • Data availability, consistency, integrity and lifecycle management
  • Intellectual property management and data loss prevention
• Application Layer
  • Application Design (Front-end, Back-end/Service Layer, Database/Data Layer)
  • Application interfaces, integration and standardization
  • DevSecOps
• Technology Layer
  • Libraries, Frameworks and Technology Stacks (Ex. MEARN, MERN, Flutter)
  • Enterprise Architecture (EA)
  • Best practice frameworks (ITIL, COBIT, TOGAF)
• Security Layer
  • ISO OSI-Layers of Communication
  • POPIT Security Framework and CIA (Confidentiality, Integrity, Availability)
  • Organizational and operational security
  • Information security guidelines, governance & protocols (ISO 27001)
  • Typical security threats in IoT, Cloud, Mobiloe, AI, eCommerce…
  • Enterprise digital resilience

• Theory of cognition and learning
• Evidence-based learning methods
• Evidence-based note-taking methods
• Techniques for motivation and self-assessment
• Techniques for tracing and monitoring (esp. in context of learning and personal development)

Upon completion of this course students are able to:

• illustrate and exemplify new work environments and their implication
• explain organisational and people development concepts,
• judge core HR models and concepts, as well as their applicability in agile, digitized work environments
• develop a leadership vision (role model) which adresses the needs of people in an organization (structure, processes, infrastructure, etc.).
• apply persuasion techniques in role plays
• name conflicts and contribute constructively to clarification as well as give constructive critique to team members and stand by their own mistakes.

Upon completion of this course students are able to:

• explain the duties of a manager to act in a socially responsible manner,
• explain ethical issues related to digital applications in companies,
• compare and contrast ethical models, values, reasoning, and processes related to managerial decision making in the contemporary business enterprise,
• identify and critically evaluate ethical problems linked to digitalisation and design potential solutions.

Upon completion of this course students are able to:

• plan exemplarily the implementation of proactive, company-wide risk management systems,
• use practical tools for identifying and assessing corporate risks arising from innovation and digitalisation,
• identify and analyse the role of risk management in value-based management, planning and reporting,
• explain optimisation processes and timely consideration of a risk/opportunity profile.

Upon completion of this course students are able to:

• interpret the legal framework in areas of decision-making that are important for digital business,
• explain how to make practical use of intellectual property rights in order to protect innovations, intellectual achievements, patents, copyright, etc.,
• evaluate the importance of intellectual property as an asset in a business context,
• evaluate the legal implications of technical solutions and organisational changes.

• Requirements Engineering - elicitation and management of requirements
• Software Engineering Process Models
• Software Architecure - Styles, Patterns and Best Practices
• Modeling Notations and Languages, e.g., UML
• Documentation of Design Decisions
• Cost Estimation
• Test and Deployment Starategies, Operational Concepts

At the end of this module students are able to:

• apply data management and data analysis on a case-by-case basis,
• interpret the results and findings from analytical models and decide which actions should be derived from them,
• draw appropriate conclusions and develop strategic planning steps,
• plan integration of data use within the organisational context,
• develop data-based business plan for process optimisation and cost reducion,
• explain information and artificial intelligence (AI) ethics principles applied to specific business technology applications.

At the end of this module students are able to:

• describe the empirical research process,
• select and implement qualitative and quantitative research designs according to the requirements of the given problem,
• identify suitable data sources and evaluate their quality,
• use software applications to assist with qualitative and quantitative data analysis,
• contrast suitable data collection methods,
• formulate a research question and hypotheses,
• test hypotheses using statistical methods and discuss findings.

At the end of this module students are able to:

• analyse and communicate their learning achievements,
• evaluate and present complex technology-related topics,
• create a skill porfolio.

At the end of this module students are able to:

• identify individual problems in connection with the preparation of the master's thesis,
• to clarify the identified problems and to develop a solution,
• present and defend a thesis.

At the end of this module students are able to:

• examine and critically discuss literature related to the topic or research problem,
• compile and evaluate data for the purpose of addressing the research problem/s,
• discuss and critically reflect on findings,
• independently produce their master thesis.

At the end of this module students are able to:

• present their Master Thesis appropriately to the target audience, and defend the question they addressed, the research design they used and the results reached before an expert committee,
• work through a case study related to their degree programme, applying the content of the programme, and develop and justify possible solutions.