Comprehensive Study Guide: Foundations of Business Informatics and Digital Economics

Information Foundations & Data Fundamentals

• Definition of Information, Data, and Knowledge:

  • Data: Unprocessed, raw facts, numbers, symbols, or signals without contextual meaning.

  • Information: Data that has been processed, organized, structured, and contextualized to convey meaning, purpose, and relevance to a recipient.

  • Knowledge: The synthesis of information, context, experience, skills, and critical understanding that enables decision-making and action.

  • Wisdom: The higher-level ability to apply deep knowledge, ethical judgment, and practical insight to complex, novel scenarios.

• Analogue Data vs. Digital Data:

  • Analogue Data Characteristics:

    • Continuous Value ($\text{wertkontinuierlich}$): Can take an infinite number of intermediate values within a given continuous range.

    • Continuous Time ($\text{zeitkontinuierlich}$): Signal changes seamlessly and uninterruptedly across time.

    • Transmission & Reproduction Disadvantages: Analogue data cannot be copied or transmitted across long distances without signal degradation, quality loss, or accumulated noise.

    • Concrete Examples: Clocks with traditional dial faces and moving hands (Zeitanzeige mit Ziffernblatt und Zeiger); mercury column glass thermometers (Quecksilbersäule im Thermometer); vinyl LP records; analogue audio tapes.

  • Digital Data Characteristics:

    • Discrete Value ($\text{wertdiskret}$): Represented by distinct, finite numerical levels (e.g., binary values $0$ and $1$).

    • Discrete Time ($\text{zeitdiskret}$): Sampled at specific, distinct time intervals.

    • Digital Advantages: Lossless reproduction (reproduced arbitrarily many times without quality loss); rapid, lossless transmission over computer networks; highly searchable; easily decomposed, edited, and reassembled into new structures; devices become progressively smaller and more efficient.

    • Quality Misconception: Digital quality is not inherently or always superior in absolute terms to analogue quality, but it guarantees lossless replication and manipulation.

    • Concrete Examples: MP3 audio files; Compact Disks (CDs); digital photos; smartphone media files.

• Potential Factors of Information Technology:

  • Ubiquity ($\text{Ubiquität}$): Availability of systems everywhere and at any time, requiring widespread, standardized protocols.

  • High Searchability ($\text{Durchsuchbarkeit}$): Direct result of structured digital data organization.

  • Multimediality ($\text{Multimedialität}$): Integration of text, audio, video, and interactive elements (does not decrease media richness; it increases it).

  • Reproducibility ($\text{Reproduzierbarkeit}$): Consequence of discrete digital structure, allowing zero-loss copying.

• Levels of Communication (Communication Process):

  • Syntactic Level ($\text{Syntaktische Ebene}$): Refers to the technical transmission of physical signs, symbols, signals, and structural syntax between sender and receiver.

  • Semantic Level ($\text{Semantische Ebene}$): Refers to the shared meaning, interpretation, and conceptual understanding of signs and symbols.

  • Pragmatic Level ($\text{Pragmatische Ebene}$): Refers to the intention, purpose, context, and practical effect of the communication on the sender and receiver.

  • (Note: Rationale Ebene is NOT a formal communication level in this framework).

• Information Value & Information Perception Filters:

  • Determination of Information Value: The value of information is defined by its time dependence (time sensitivity / $\text{Zeitabhängigkeit bzw. Zeitigkeit}$) and its information richness ($\text{Reichhaltigkeit der Information}$), and it is ultimately determined by the recipient ($\text{Empfänger}$).

  • Subjective Perception: Humans always perceive a subjective slice of reality and evaluate it subjectively. Academic education aims to broaden students' perception filters and enable multi-perspectivity.

  • O'Reilly's Organizational Context Principles:

    • Information is more likely to be considered if it is easily accessible (organizationally, spatially, intellectually).

    • Information is more likely to be considered if it favors the personal goals of the decision-maker.

    • Information is more likely to be considered if it creates fewer conflicts with required cooperation partners.

    • Information is more likely to be considered if there is higher trust in the source and direct/personal contact with the informant (mündlicher Vorteil).

    • Information is less likely to be considered if it is presented in an overly complex, obscure, or difficult-to-understand manner.

• Knowledge Conversion: Nonaka's SECI Model:

  • Socialisation ($\text{Sozialisation}$): Tacit knowledge to Tacit knowledge (e.g., shared experience, observation, apprenticeship).

  • Externalisation ($\text{Externalisierung}$): Tacit knowledge to Explicit knowledge (e.g., articulating concepts into manuals, figures, or written models).

  • Combination ($\text{Kombination}$): Explicit knowledge to Explicit knowledge (e.g., synthesizing reports, databases, and existing documentations).

  • Internalisation ($\text{Internalisierung}$): Explicit knowledge to Tacit knowledge (e.g., learning by doing, internalizing operational standards).

  • (Note: "Asozialisation" is a distractor and false).

  • Explicit vs. Implicit Knowledge Categories:

    • Explicit Knowledge: Theories, formulas, documented sequential knowledge, technical specifications.

    • Implicit (Tacit) Knowledge: Mental models, deep understanding ($\text{Verständniswissen}$), personal craftsmanship, cognitive intuition.

• Media Competence & Information Reduction:

  • Subject Competence ($\text{Sachkompetenz}$): Knowledge of media business models, understanding manipulation risks, awareness of international media offerings and alignments.

  • Self-Reflection Competence ($\text{Selbstreflexionskompetenz}$): Critical questioning of one's own media usage, examining if media consumption causes distorted/one-sided viewpoints, evaluating if one operates inside an filter bubble/echo chamber.

  • Complexity Reduction: Forms of complexity reduction include boulevardization, delegating curation to chief editors, censorship, and exercising high individual media competence.

Enterprise Systems & Business Software

• Enterprise Resource Planning (ERP) Systems:

  • Core Purpose: ERP describes the operational task of efficiently utilizing an enterprise's available resources (capital, personnel, materials, machinery) to optimize business processes, operational workflows, and information flows across departments.

  • Structural Characteristics:

    • Modular and integrated architecture built from interconnected ERP apps/modules.

    • Provides cross-departmental transparency.

    • Integrates internal processes (e.g., Procurement, Warehousing, R&D, Accounting, Production, Sales/Marketing) and supports external workflows with business partners via B2B Gateways.

  • Terminology Clarification: ERP stands for Enterprise Resource Planning (it does NOT stand for "Enterprise Resonance Programs").

  • ERP Success & Failure Drivers:

    • Success or failure depends on both organizational and technological factors before, during, and after implementation.

    • A common misconception is that an ERP implementation was a complete success simply because errors remain hidden internally or are patched later via software updates.

    • Software selection is highly time-intensive, complex, and costly. Vendor channels (blogs, white papers, YouTube) are promotional and not objective data sources.

    • If applications are too complex for employees, productivity drops, severely damaging the Return on Investment (ROI).

    • System failure can lead to multi-million euro losses, corporate insolvencies, and protracted legal battles.

• Master Data ($\text{Stammdaten}$) vs. Transactional Data ($\text{Bewegungsdaten}$):

  • Master Data ($\text{Stammdaten}$): Core, foundational business entities that remain relatively static over long periods (e.g., customer master records, vendor data, article/product details, employee files, chart of accounts).

  • Transactional Data ($\text{Bewegungsdaten}$): Dynamic, event-driven, process-specific data generated from operational transactions (e.g., customer orders, inventory movements, production orders, invoices). Transactional data always reference underlying master data.

• Integration of Legacy Systems & External Interfaces:

  • Legacy Systems: Older, existing business systems that are often critical to core operational processes. Key problems include low connectivity with modern software, monolithic/unflexible architecture, high maintenance costs, and shortage of specialized staff.

  • Migration Motivations: Legacy systems suffer from poor flexibility and difficult integration with web applications. Replacing or migrating them carries disruption risk, initial training costs, and temporary productivity drops.

  • External Integration: Necessary because companies must interact with external stakeholders (suppliers, customers, state authorities). B2B Gateways and EAI middleware ensure secure document and data exchange.

• Supply Chain Management (SCM) Systems:

  • Three Core Flows of a Supply Chain:

    1. Information Flow ($\text{Informationsfluss}$): Bi-directional flow of demand signals, order status, capacity, and tracking data.

    2. Financial Flow ($\text{Finanzfluss}$): Payment processing, credit terms, invoicing, and financial settlements.

    3. Material / Product Flow ($\text{Material- oder Produktfluss}$): Upstream and downstream movement of raw materials, components, and finished goods.

  • Prerequisites for Value Generation: SCM software requires reliable real-time data and an integrated ERP system in the background.

  • SAP SCM Core Components:

    • Supply Chain Planning: Plans needed materials/products for upcoming months to satisfy forecasted market demand.

    • Supply Chain Execution: Manages daily operations and identifies/prevents supply chain bottlenecks.

    • Supply Chain Visibility: Monitors and tracks real-time daily activities across the entire supply chain network.

    • Supply Chain Collaboration: Facilitates coordination, alignment, and communication with suppliers and partners.

• Data Warehousing, OLTP, and OLAP:

  • Data Warehouse Characteristics: Subject-oriented (organized by themes such as customer, product, vendor), integrated/consistent, time-variant (stores historical data to identify long-term trends and forecasts), non-volatile.

  • OLTP ($\text{Online Transaction Processing}$): Operational databases designed for fast, high-volume, transactional read/write operations in real time. A Data Warehouse is NOT an OLTP system.

  • OLAP ($\text{Online Analytical Processing}$): Multidimensional data analysis designed for complex analytical queries and reporting operating on historical Data Warehouse data.

  • ETL Process: Extract, Transform, Load — the standard pipeline used to move operational data into a Data Warehouse.

  • Master Data Management (MDM): Acts as middleware to harmonize and centralize master data across disparate systems, maintaining data quality and acting as a single repository of truth.

• Standard Software vs. Individual Software:

  • Standard Software: Development costs are distributed across many users, yielding lower per-unit licensing fees. Offers lower adaptability to unique processes compared to custom software, and may contain residual bug loads.

  • Individual Software: Custom-tailored to specific company workflows, highly adaptable, but carries high individual development and maintenance expenses.

Customer Relationship Management (CRM) & E-Procurement

• Customer Relationship Management (CRM):

  • Core Objective: Encompasses all management processes aimed at building, expanding, retaining, and reactivating profitable, long-term customer relationships.

  • Economic Value of Long-Term Relationships: Long-term relationships are generally significantly more profitable than short-term transactions due to base profit, revenue growth per customer, operational cost savings, and organic customer referrals. Acquisition of new customers is drastically more expensive than retaining existing ones.

  • Customer Relationship Lifecycle Phases:

    1. Prospect Management ($\text{Interessentenmanagement}$): Attracting and identifying potential target clients.

    2. New Customer Management ($\text{Neukundenmanagement}$): Converting prospects into active buyers.

    3. Satisfaction & Complaint Management ($\text{Zufriedenheits- und Beschwerdemanagement}$): Ensuring product/service quality and resolving post-purchase friction.

    4. Churn Prevention Management ($\text{Kündigungspräventionsmanagement}$): Detecting retention risks and preventing customer loss.

    5. Reactivation & Revitalisation Management ($\text{Reaktivierungs- und Revitalisierungsmanagement}$): Re-engaging former or inactive customers.

  • Operational vs. Analytical CRM:

    • Operational CRM: Front-office systems supporting daily marketing, sales automation, customer service, and contact points.

    • Analytical CRM: Back-office analysis using Data Warehouses, Data Marts, and Data Mining to analyze customer behavior, segmentation, and lifetime value.

  • Management Support Systems (MSS): Systems designed to assist management decision-making. Includes Management Information Systems (MIS) and Decision Support Systems (DSS). (Transaction Processing Systems / TPS are operational systems, not MSS).

• E-Procurement & B2B Marketplaces:

  • Rationalization Effects of E-Procurement: Optimizes the procurement process within the value chain, shortens order process times and delivery lead times, reduces processing costs per transaction, and enables well-informed purchasing decisions under optimal supplier contracts. It does not maximize the total number of suppliers.

  • B2B Marketplace Models:

    • Sell-Side Marketplace: A single seller (or group of suppliers) sells to multiple corporate buyers, frequently using forward auctions or structured online catalogs.

    • Buy-Side Marketplace: A single buyer seeks quotes/bids from multiple suppliers (reverse auctions).

    • Vertical Marketplace: Serves all steps of the procurement and supply chain within a single specific industry.

    • Horizontal Marketplace: Provides functional services or standard products across multiple distinct industries.

E-Business Models, Digital Platforms & Digital Economics

• Business Model Canvas & Layers:

  • Core Elements: Value Proposition, Revenue Model ($\text{Ertragsmodell}$), Architecture of Value Creation ($\text{Architektur der Leistungserstellung}$), User Interface / Customer Channels.

  • Social Layer Elements: Evaluates social impacts, employee impacts of systems, and social culture ($\text{Social Culture}$) of platforms like WeChat.

  • Environmental Layer Elements: Life cycle considerations (e.g., comparing EV vs. combustion vehicles requires End-of-Life evaluation; comparing Bitcoin electricity consumption directly to national consumption is invalid due to fundamentally different value propositions).

• Economic Theories of Digital Markets:

  • Move-to-the-Market Thesis: The internet drastically lowers market transaction and coordination costs, driving economic activities away from rigid internal corporate hierarchies toward open, market-based coordination (e.g., eBay).

  • Disintermediation: Eliminating traditional sales intermediaries or middle-men between producer and final consumer (e.g., Dell selling computers directly to consumers; music artists like Madonna leaving traditional record labels to distribute digitally and profit from live tours/sponsorships).

  • Goods vs. Information Objects:

    • Information Objects: Characterized by high fixed production costs (Sunk Costs) but near-zero marginal costs ($\text{Grenzkosten}$) for reproduction. This cost structure leads to natural market concentration and high scalability. Non-rivalry in consumption applies.

    • Physical Goods: High variable production costs, subject to rivalry in consumption, and easier to enforce physical exclusion.

• Network Effects & Platform Economics:

  • Positive Network Effect: The utility of a platform or service increases for each existing user as the total number of users (or complimentary side actors) increases (e.g., more sellers attract more buyers on AirBnB or eBay).

  • Negative Network Effect: Utility decreases as user volume grows beyond capacity (e.g., network congestion, platform clutter, overload).

  • Digitally Enabled Business Models: Driven by platform access, scalability, predictive analytics, and high integration to guarantee real-time delivery estimates. Analytics frequently creates a direct conflict with user privacy.

• Customer Journey & Retail Technology:

  • Channel Configurations:

    • Single-Channel: Legacy approach where customers interact with the company via a single isolated touchpoint.

    • Multi-Channel: Multiple independent sales channels operated without integrated data sharing.

    • Cross-Channel / Omni-Channel: Seamlessly integrated customer experience across physical store (Brick-and-Mortar) and digital touchpoints (e.g., IKEA as a pioneer).

  • Technology Rollouts in Retail: Predictive analytics and IoT support initial demand discovery; automatic replenishment handles routine repeat purchases (approx. $80\%$ of grocery shopping is repeat replenishment).

• Mobile Commerce & Payment:

  • Micro-payments: Capability to transfer very small financial sums efficiently, making mobile payment systems highly viable for daily micropurchases.

  • Starbucks Mobile Model: Utilizes mobile app barcodes scanned by POS cashiers to tie loyalty rewards directly to pre-funded payments.

  • QR Codes: Function as a bridge between the physical and digital worlds; highly successful in Asia due to deep integration into daily utility (identity verification, payments, service booking), unlike western markets where QR codes were initially deployed as static marketing gimmicks.

• Crowdfunding & Crowdsourcing:

  • Crowdsourcing: Outsourcing tasks, problem-solving, or idea generation to a large online crowd.

  • Crowdfunding Types:

    1. Donation-based: Unrequited contributions for social/creative causes.

    2. Debt-based (Peer-to-Peer Lending): Capital provided as a loan to be repaid with interest.

    3. Equity-based (Crowdinvesting): Investors receive company equity or shares of future overall earnings.

    4. Reward/Revenue-share based: Contributors receive early products or project-specific revenue shares.

Digital Marketing, Social Media & Web Horizons

• Search Engine Advertising (Google Ads vs. Organic Search & AdSense):

  • Organic Search Results: Entries listed naturally by search engine algorithms based on relevance criteria, content structure, and backlink authority. Search engine positions in organic search cannot be purchased.

  • Google Ads: Sponsored text search listings priced primarily on a Cost-Per-Click (CPC) model. Highly scalable compared to traditional print media because Google aggregates vast amounts of third-party content and places targeted ads directly against user search intent.

  • Google AdSense: Allows third-party website owners to monetize their traffic by offering ad space. Google automatically contextually matches ads to the website's content. Ads displayed via AdSense are explicitly marked as sponsored ads ("Ad" / "Anzeige").

• Social Media Strategy & Monitoring:

  • Key Monitoring Metrics:

    • Time on Site: Duration a visitor remains active on a website.

    • Bounce Rate: Percentage of visitors who enter a site and leave immediately after viewing only a single page.

    • Conversion: Action completion rate (e.g., purchase, signup). (Note: Conversion is NOT average transaction volume).

  • Opinion Leaders & Influencers: Social monitoring aims to track brand mentions, identify key opinion leaders, and incentivize them to portray the brand positively to shape sentiment.

  • Social Network Analysis (SNA): Quantitative, graph-based analysis investigating relational structures and network ties between people, groups, or organizations.

• Content Provision & Media Bias:

  • Selection Bias: Phenomenon where content providers selectively report on specific topics while completely omitting others.

  • Sentiment Bias: Phenomenon where a content provider consistently reports on a specific subject with a biased, one-sided emotional tone or narrative.

  • Subjective Information Value: Information content carries no absolute objective value; every recipient constructs an individual subjective perception.

• Evolution of the Web:

  • Web 1.0: Static, read-only web pages with centralized content creation and passive readership.

  • Web 2.0 (Social Web): Interactive, user-generated content, participatory media, microblogging platforms (e.g., Tumblr), dynamic online communities, and dialogic communication. (Terms like "Blog 2.0" or "Public Internet" are NOT valid academic synonyms for Web 2.0).

  • Web 3.0 (Semantic Web): Machine-readable web where computers interpret the context, meaning, and semantic relationships of data using structured metadata formats. Features Semantic Search, which evaluates search intent and context beyond plain keyword matching. XML and HTML are markup languages, not Web 3.0 programming languages.

Emerging Technologies: AI, Blockchain, RFID & Ethics

• Artificial Intelligence (AI) Foundations:

  • Fundamental Operating Schema: AI senses input data from diverse sources (text, images, audio, video, financial numbers), processes it through algorithms/neural networks, and generates outputs (content, predictions, operational actions).

  • Predictive AI vs. Generative AI:

    • Predictive AI: Analyzes historical data to compute probabilities and generate forecasts (e.g., predicting credit default risks, demand forecasting).

    • Generative AI: Trained on large datasets to generate entirely new content (text, images, synthetic media) mimicking human output (e.g., Large Language Models like ChatGPT, Google Bard/Gemini).

  • Machine Learning (ML): Branch of AI where systems learn pattern representations directly from historical data without being explicitly programmed with hardcoded logical rules.

• Artificial Neural Networks (ANN / KNN):

  • Architectural Layers:

    1. Input Layer ($\text{Eingabeschicht}$): Receives raw input feature vectors.

    2. Hidden Layer ($\text{Verborgene Schicht}$): Intermediate nodes performing non-linear mathematical transformations.

    3. Output Layer ($\text{Ausgabeschicht}$): Yields final classification or generative output.

  • Parameters: Structural internal parameters adjusted during training consisting of Nodes ($\text{Krotent}$) and Edges ($\text{Kanten}$) along with their numerical Weights ($\text{Gewichte}$).

  • Hyperparameters: External settings configured by the user/developer prior to training that govern the network's architecture and learning behavior (e.g., learning rate, batch size, epoch count).

• AI Ethics & Labor Market Impact:

  • Labor Transformation: AI replaces repetitive tasks while simultaneously creating new roles; a vast proportion of the workforce will require reskilling. A unique technological risk is the rapid, zero-latency replication and instant internalisation of identical trained Neural Network models across the globe.

  • Ethical Requirements: AI systems must be lawful, robust, non-biased, and maintain transparent, explainable relationships between inputs and outputs ($\text{XAI / Explainable AI}$).

• Blockchain Technology & Cryptography:

  • Cryptographic Key Pair: Consists of an Public Key (serves as the visible wallet address/account number) and a Private Key (secret digital signature key used to authorize transactions).

  • Wallet: Software/hardware tool that securely stores and manages cryptographic key pairs.

• Radio Frequency Identification (RFID):

  • Mechanism: Uses electronic tags (chips/stickers with miniature antennas) that transmit stored identifier data wirelessly over radio frequencies without requiring line-of-sight optical scanning.

  • Application: Replaces physical barcodes (Strichcodes) in automated warehousing, logistics, and SCM tracking.

Case Studies & Applied Business Scenarios

• Case C1: Grupo Farmacos (Data Management & Consolidation):

  • Problem: Rapid corporate growth combined with decentralized regional units resulted in widespread "dirty data", inconsistencies, and fragmented reporting.

  • Solution: Consolidated and centralized data management into a unified master repository.

  • Impact: Drastically improved data quality, reduced data cleaning overhead for business departments, lowered operational costs, and established trusted decision-making.

• Case C2: BMW Cloud Infrastructure:

  • Problem: BMW's highly automated automotive production lines and engineering workflows are heavily IT-dependent, making system downtime exceptionally costly.

  • Requirement: Demands $100\%$ cloud availability for critical operational processes.

  • Lagged Benefits: Benefits manifest with a delay because cloud transition requires extensive stress testing, system adaptations, phased migration, and operational stabilization.

• Case C3: ACR Group (In-Memory Analytics):

  • Architecture: Deployed SAP HANA in-memory database combined with Hitachi Unified Compute Platform (UCP).

  • Benefit: Enables near-real-time data processing, massive acceleration of reporting queries, and scalable storage infrastructure.

• Case C4: Coca-Cola "Black Book":

  • Application: An advanced algorithm/supply chain optimization model used for orange juice blending.

  • Benefit: Mitigates supply chain risks, compensates for weather fluctuations and raw material availability, and guarantees consistent global taste and product quality.

• Case C5: Nike Golf SEO Failure:

  • Problem: The nikegolf.com website was constructed extensively using JavaScript rendering.

  • Result: Search engine web crawlers could not read or index the textual content and keywords, leading to poor organic search engine rankings.

• Case C6: Baidu SEO Localization:

  • Challenge: Optimizing websites for China's leading search engine (Baidu) requires overcoming strict language barriers (Chinese characters) and cultural search habits.

  • Requirement: Baidu applies different ranking criteria than Google; professional linguistic and cultural content localization is essential.

• Case C7: ASOS China Expansion:

  • Context: ASOS expanded into China to capture massive market volume.

  • Key Adaptation: Chinese consumers exhibit radically different social media habits, digital ecosystem preferences (e.g., WeChat), and mobile-centric shopping behaviors compared to European consumers.

• Case C8: LLSC Social Media Management:

  • Strategy: Successfully professionalized social media communications through centralized management, employee training, strategic campaign planning, and platform aggregation via Hootsuite.

• Case C9: Louis Vuitton Mobile Commerce:

  • Strategy: Designed an intuitive, high-speed mobile purchase workflow with elegant visual design, catering to modern luxury consumers on mobile devices.

• Case C10: WeChat QR Codes in China:

  • Comparison: Unlike Western markets where QR codes were often underutilized marketing gimmicks, China integrated QR codes into everyday utility (digital identity, mobile payments, transit, dining).

• Case C11: 3D Printing as Disruptive Innovation:

  • Threat: Acts as a "Creative Destroyer" that disrupts traditional manufacturing, spare parts logistics, and supply chain intermediaries by enabling localized print-on-demand production.

• Case C12: AVON SAP SMT Implementation Failure:

  • Failure Cause: AVON attempted an overly radical, rushed transition from its legacy order management system to a new SAP system without adequate pilot testing phases.

  • Outcome: The system was not production-ready, causing massive operational disruption, sales force friction, and ultimate project abandonment.