Business Processes and E-Tailing Quiz

A series of related activities that produce a valuable product or service for the organization, its partners, or customers.

Materials, services, and information that are changed or transformed during the process.

People and equipment that perform process tasks.

The finished product or service created by the process.

Doing tasks well without wasting resources or time.

Creating outputs that are valuable to the customer.

Business processes that involve multiple functional areas within an organization.

The intensity of competition among existing firms in an industry.

A way for a company to outperform its rivals, usually by allowing customers to use data from their products.

The cost to produce additional digital products is almost zero once the product is developed.

A model used to identify competitive strategies in an organization by examining the sequence of activities.

A series of activities that transforms inputs into valuable outputs.

Activities directly related to producing and distributing a firm's products or services.

Activities that do not add value directly to a product/service but support primary activities.

Activities involved in receiving and processing incoming materials, e.g., raw materials.

Activities related to getting products ready for delivery.

Prepaid cards that allow you to store a fixed amount of money and spend it as needed, reducing the balance with each transaction.

Cards with a chip that stores information and often includes a magnetic stripe for compatibility with older systems.

Selling products and services directly online through virtual storefronts or electronic malls.

The elimination of intermediaries (middlemen) from a supply chain, often due to automation or technology.

Services provided by intermediaries that enhance the core product or service, and are often difficult to fully automate.

A repository of historical data organized by subject to support decision-making. It's designed for complex queries and analysis, but can be expensive to implement.

A scaled-down, less expensive version of a data warehouse focused on specific business units or departments. It's faster to implement and provides targeted data analysis.

Organizations build data warehouses and marts to overcome limitations of transactional databases, which are designed for individual record transactions. These systems offer better data organization, efficient analysis, and historical data access.

Transactional databases use OLTP (Online Transaction Processing) for fast, individual transactions. Data warehouses/marts utilize OLAP (Online Analytical Processing) for analyzing large data sets to support decision-making.

Data warehouses and marts combine data from multiple sources into a consistent view around specific subjects (e.g., customers).

They store historical data, allowing for the analysis of trends and long-term patterns over time.

Data in warehouses and marts cannot be altered by users, ensuring data integrity and historical accuracy.

Data in warehouses and marts is organized by subject (e.g., customer, product), unlike transactional systems that focus on business processes.

They provide better organization, insights through analysis, and access to historical data, supporting business decisions.

They play a crucial role in gaining valuable insights from data, which can be used to improve sales, marketing, and overall business performance.

A multidimensional data structure that organizes data in more than two dimensions, often used in data warehouses.

Subjects like product, location, or time that define the different axes of a data cube in data warehouses.

Databases or applications that provide data to a data warehouse or data mart.

Analyzing data quality and characteristics before loading it into a warehouse to understand its structure and potential problems.

Data organized in multiple dimensions, typically stored in a data cube, unlike relational databases with two-dimensional tables.

Data about data in a repository, describing its structure, meaning, and relationships.

Processes and policies for managing data quality, security, and accessibility in a data warehouse.

Inaccurate, incomplete, or inconsistent data that can lead to faulty analysis and decision-making.