E-commerce and Decision-Making Quiz

Business Information Systems

• Information systems are used in daily life across various sectors, including grocery stores (POS), banks (ATM), and educational institutions (e-services).
• Organizations utilize information systems to cut costs and gain a competitive edge in marketplaces.

Investments in Information Technology

• Investments in IT yield superior returns, increasing productivity and revenue while boosting strategic positioning.
• Revenue expenditure (like salaries) is recurring, while capital expenditure (like facility upgrades) is one-time.
• Firms invest in IT to improve efficiency and profitability.

New Business Models

• Traditional business models (like linear TV) are contrasted with new models that leverage data-driven performance.
• Innovation in technology is central to the development of new models and their success.
• Data-driven performance, sponsorship, and partnerships are critical components of modern models which help businesses to make money.

Perspectives on Information Systems (OPT)

• An example is presented of how tracking systems, such as those used by FedEx, manage inventory and provide information.
• Aspects of information systems that are important include organizational structures, monitoring service quality and cost, technology, and business drivers of information systems.
• Firms invest in information systems to achieve operational excellence and strategic objectives.

New Products, Services, and Business Models

• Businesses use information systems and technology to create new products and services and new business models such as Netflix.
• Businesses use technology to improve customer and supplier intimacy, improve decision-making, and achieve a competitive advantage or even just to survive

Information Systems: What Is It?

• An information system consists of components that work together, accepting input and producing output to support decision-making.
• Information systems comprise interconnected and interdependent elements that gather, process, store, and distribute data, facilitating decision-making and coordination.

Major Components of an Information System

• Raw facts or data provide the fundamental input for the system.
• Data is gathered internally from sales records and externally from clients, suppliers, and other sources.
• A system's process transforms data into useful information, making it valuable for decision-making.
• Information is evaluated according to its usefulness and qualities, including timeliness, consistency, accuracy, relevance, verifiability and validity,

Database Management Systems (DBMS)

• DBMS is software for creating, storing and retrieving data from a database.
• It separates the logical and physical views so that users can interact with the data in a way that suited their needs.

Relational Databases

• Relational databases store data in two-dimensional tables with rows and columns.
• Tables store data for each specific entity (e.g., CUSTOMER, SUPPLIER, PART, SALES ), attributes are stored in Fields (columns), and rows represent specific records or tuples.
• Each record is identified uniquely by a key field.

Establishing Relationships

• Establishing relationships between tables through entity-relationship diagrams is essential for clarity in relational databases.
• Referential integrity rules ensure the consistency of relationships.

Big Data

• Large quantities of unstructured and semi-structured data from the internet.
• Big data requires new technologies and tools to process its enormous volume, variety, and velocity (3Vs).

Non-Relational Databases

• NoSQL databases are designed to handle large, unstructured data sets, unlike relational databases that use tables, columns, and rows.
• NoSQL uses a flexible data model and can effectively handle unstructured data types like social media and graphics.

Cloud Databases and Distributed Databases

• Cloud computing environments offer pre-built relational database services based on usage, ideal for small and medium-sized businesses.
• Distributed databases store data across multiple locations for higher availability and resilience.

Blockchain

• Blockchain is a decentralized and transparent distributed database for transactions where each record (called a block) is secured by cryptography.
• Blocks are sequenced, and once recorded, they cannot be altered, making the system tamper-proof.
• It reduces processing costs and enhances security of transactions.

Business Intelligence Infrastructure

• Tools like data warehouses, data marts, Hadoop, in-memory computing, and analytical platforms support data analysis to extract usable information from various sources.

Data Warehouses

• A data warehouse is a central repository of integrated data from multiple sources, both transactional and operational, to assist decision-makers.
• Data marts are subsets of data warehouses, tailored for specific user groups.

Hadoop

• Hadoop is an open-source framework that processes large amounts of diverse data by breaking tasks into smaller sub-problems and distributing them across multiple inexpensive computers.
• Hadoop leverages distributed file systems and MapReduce processing.

Data Mining

• Data mining uncovers hidden patterns from large datasets for insights to aid future behavior prediction.
• Methods include association rule mining, sequence analysis, classification, clustering, and forecasting.

Analytical Tools

• Statistical methods, multidimensional data analysis (OLAP), data mining tools are used to analyze relationships, patterns, and trends in data.

Online Analytical Processing (OLAP)

• OLAP enables multi-dimensional data analysis for better business insight.
• Views of the same data can be different perspectives.

Text Mining

• Text mining focuses on unstructured data, primarily text files, to extract key elements, identify patterns, and summarize large sets of textual data.
• Sentiment analysis is a text-mining technique for measuring customer sentiment from online feedback.

Web Mining

• Web mining uses various techniques to extract patterns and information from web data to understand customer behavior and assess marketing success.
• It includes content, structure, and usage mining.

Enterprise Systems

• ERPs are integrated software suites with a common database to collect, consolidate and share data across multiple business divisions for improved decision-making.

Supply Chain Management

• A network of organizations and processes involved in procuring, manufacturing, and delivering products.

Supply Chain Management Software

• Supply chain planning systems model existing supply chains, enabling demand planning, optimizing sourcing, and transportation strategies and helps manage flows of products.
• Supply chain execution systems manage product flows through distribution centers and warehouses.

Customer Relationship Management

• Customer relationship management (CRM) systems capture, integrate, and analyze customer data from various sources for a holistic view.
• CRM software tools typically include sales force automation (SFA), customer service tools, and marketing automation functionalities and typically help firms to achieve better customer intimacy.

Operational and Analytical CRM

• Operational CRM supports daily customer interactions through applications like customer service and sales force automation.
• Analytical CRM uses data warehousing and analytical tools to gain insights into customer behavior and preferences for targeted marketing decisions and improved revenue models.

Enterprise Applications: Challenges

• Enterprise applications are costly to purchase and implement and require significant time for development and organizational change.
• Adapting to evolving technology, business changes, and potential dependency on software vendors require continuous organizational learning and resources.

Next-Generation Enterprise Applications

• Enterprise solutions/suites, typically web-enabled, integrated with other systems, incorporating SOA standards, and leveraging open-source applications, on-demand/cloud-based versions, and functionality for mobile platforms.
• Next-generation applications also incorporate social CRM and business intelligence for improved decision-making and performance.

E-commerce

• E-commerce refers to the use of the Internet and web to conduct business transactions.

E-commerce Business Models and Revenue Models

• E-commerce business models include portal websites, online retailers (e-tailers), content providers, transaction brokers, market creators, and several others.
• E-commerce revenue models include advertising, sales, subscriptions, and transaction fees.

Key Concepts in E-Commerce, Digital Markets, and Digital Goods

• Internet and digital markets transformed business by reducing information asymmetry, menu costs, search costs, and transaction costs.
• E-commerce includes disintermediation (cutting out intermediaries), dynamic pricing, and delayed gratification.
• Digital goods, like software or ebooks, have reduced costs to deliver and high variable pricing due to low marginal costs to produce.

Mobile Commerce

• Mobile commerce, or m-commerce, is rapidly growing and accounts for a significant portion of e-commerce.
• It involves sales of digital content, mass-market retail, and in-App sales specific to mobile devices.

Location-Based Services and Applications

• Location-based services are used by smartphones to find nearby businesses, friends, and even services such as house prices.
• Many applications utilize GPS mapping and geosocial networks to provide accurate location-based services.

Other Mobile Commerce Services

• Financial apps from credit card companies and banks manage financial accounts.
• Mobile advertising has overtaken other channels due to easy access on mobile devices.

Improving Decision Making and Managing Artificial Intelligence

• Various decision types exist, ranging from structured to unstructured and semi-structured, each requiring different methods for solution implementation.
• Tools such as Business Performance Management (BPM)and Enterprise Support Systems (ESS) provide data for support of decision-making within organizations.
• Artificial intelligence techniques, such as expert systems, machine learning, and neural networks, support improved decision-making and overall business efficiency.