Business Intelligence Overview

<p>By managing the acquisition and dissemination of new information (C)</p> Signup and view all the answers

<p>Making informed decisions based on accurate and current information (C)</p> Signup and view all the answers

<p>Specific areas or roles within the organization that require support (C)</p> Signup and view all the answers

<p>Social media management (A)</p> Signup and view all the answers

<p>Staying ahead of the competition through informed decision-making (C)</p> Signup and view all the answers

<p>To support reporting, analysis, and decision making (B)</p> Signup and view all the answers

<p>Increased data redundancy (A)</p> Signup and view all the answers

<p>A consensus among stakeholders on data interpretation (A)</p> Signup and view all the answers

<p>Fact-based decision making (C)</p> Signup and view all the answers

<p>A multinational corporation with extensive data needs (A)</p> Signup and view all the answers

<p>BI systems enable dynamic reporting and analysis (C)</p> Signup and view all the answers

<p>Watching informative videos and case studies (B)</p> Signup and view all the answers

<p>Overview of Business Intelligence (A)</p> Signup and view all the answers

<p>Widespread usage for better decision making (B)</p> Signup and view all the answers

<p>BI must be aligned with the company's business strategy. (C)</p> Signup and view all the answers

<p>Transformation to data-driven decision-making (A)</p> Signup and view all the answers

<p>To encourage interaction between users and IT (B)</p> Signup and view all the answers

<p>Benefit to the enterprise as a whole (D)</p> Signup and view all the answers

<p>Benefits only specific departments (D)</p> Signup and view all the answers

<p>By enabling data-driven decision-making (B)</p> Signup and view all the answers

<p>They are solely a technical responsibility. (C)</p> Signup and view all the answers

<p>Additive (A)</p> Signup and view all the answers

<p>Semi-Additive (A)</p> Signup and view all the answers

<p>Average Sales Price (B)</p> Signup and view all the answers

<p>They occur in event facts (C)</p> Signup and view all the answers

<p>In all types of facts (B)</p> Signup and view all the answers

<p>Non-Additive (A)</p> Signup and view all the answers

<p>Cannot be aggregated across certain dimensions (C)</p> Signup and view all the answers

<p>Semi-Additive measures can be summed but not over time (C)</p> Signup and view all the answers

<p>A small data mart designed for a specific business unit without relying on an EDW (D)</p> Signup and view all the answers

<p>A bottom-up approach focusing on individual departments and then integrating later (A)</p> Signup and view all the answers

<p>Enterprise-wide warehouse strategy by Inmon (D)</p> Signup and view all the answers

<p>They focus on day-to-day operations of a business (A)</p> Signup and view all the answers

<p>To quickly meet specific business unit needs without waiting for an enterprise solution (B)</p> Signup and view all the answers

<p>Sales with $1,000,000 (D)</p> Signup and view all the answers

<p>To achieve a comprehensive enterprise-wide data warehouse (C)</p> Signup and view all the answers

<p>Integrates data from all parts of an enterprise (A)</p> Signup and view all the answers

<p>Decision support functions (B)</p> Signup and view all the answers

<p>A standardized format of cleansed, integrated data (D)</p> Signup and view all the answers

<p>The data does not change frequently (D)</p> Signup and view all the answers

<p>Data cleaning processes (B)</p> Signup and view all the answers

<p>They provide context and categorization for facts (D)</p> Signup and view all the answers

<p>Dependent data marts are directly linked to data warehouses (D)</p> Signup and view all the answers

<p>To facilitate data retrieval and management (D)</p> Signup and view all the answers

<p>Legal compliance auditing (D)</p> Signup and view all the answers

<p>Structured data for analytical processing (D)</p> Signup and view all the answers

<p>Data is organized around key subjects rather than applications (C)</p> Signup and view all the answers

<p>To guarantee the accuracy and reliability of data (D)</p> Signup and view all the answers

<p>Temporary data storage for transactions (D)</p> Signup and view all the answers

<p>Data extraction (B)</p> Signup and view all the answers

<p>It provides a reduced scope of data for specific analysis (B)</p> Signup and view all the answers

A system that uses machines to automatically extract, encode, and organize information from documents.

A collection of information that helps people understand their responsibilities and activities within an organization.

A profile that captures a person's interests and needs for information within an organization.

The process of collecting, storing, organizing, and distributing information to support specific activities within an organization.

The goal of providing the right information to the right people at the right time.

The process of obtaining and adding new information to an organization's knowledge base.

A future where information is widely accessible to everyone in an organization, including employees, customers, and partners.

The ability to make informed decisions based on accurate and up-to-date information.

A business intelligence system's success is measured by its regular usage for better decision-making, impacting all management levels, not just executives.

BI should align with the company's strategy and not be a separate technical exercise. It should improve business processes and transform decision-making to be more data-driven.

A BI Competency Center facilitates the connection between BI and strategy, encourages communication between users and IT, and promotes best practices.

An enterprise-wide system that supports reporting, analysis, and decision making.

BI enables decisions to be based on facts, not just opinions or gut feelings.

BI ensures that everyone within an organization is working with the same, accurate data.

A data warehouse provides a central repository for large amounts of data from various sources.

DW enables businesses to extract meaningful insights from their data for better decision-making.

Power BI is a powerful tool used for data visualization and analysis.

Power BI can be utilized to process and present data extracted from various sources, such as Moodle.

DW and BI are essential tools for making well-informed, data-driven decisions in various industries.

A data warehouse specifically designed for a single department or business unit, often built independently of a central data warehouse.

A data warehouse strategy that focuses on building smaller, departmental data marts first and then gradually integrating them into a larger enterprise-wide warehouse.

A type of data warehouse that is designed to meet the specific needs of a single department or business unit, like sales, marketing, or finance.

A data warehousing strategy where a single, large data warehouse is built first for the entire organization, and data marts are then created as needed.

A data warehouse strategy created by Ralph Kimball, that emphasizes building data marts first before eventually integrating them into a larger enterprise-wide warehouse.

A data warehouse strategy where the entire enterprise-wide warehouse is built first by top management, often without much input from individual departments.

A top-down approach to data warehousing, where a single, enterprise-wide warehouse is built first, and data marts are then created as needed.

A bottom-up approach to data warehousing, where smaller data marts are built first for individual departments, and then integrated into a larger enterprise-wide warehouse.

A measure that can be aggregated across all dimensions, meaning you can add the measure values together for different dimensions to get a meaningful total.

A measure that can be aggregated only over some dimensions, but not others. For example, you can sum inventory across different stores, but not across different time periods.

A measure that cannot be aggregated across any dimensions. It doesn't make sense to add their values.

A fact that can be aggregated across all dimensions. For example, Total Fee Revenue can be summed across all students, time periods, and other dimensions.

A fact that can be aggregated only over some dimensions but not others.

A fact that cannot be aggregated across any dimensions. For example, ratios like Scholarship-to-Fee Ratio are non-additive.

A specialized repository that stores data from various sources in a structured format for analysis and decision-making. Data is cleaned, standardized, and organized for efficient querying and reporting.

A collection of integrated, subject-oriented databases designed to support decision-making, with non-volatile data and relevant time information.

A data warehouse is a repository of enterprise-wide, cleansed data in a standardized format, designed to support analytical processing activities like OLAP, data mining, querying, reporting, and decision support.

A data warehouse is a central repository of integrated data from multiple sources, designed specifically for analytical processing and business intelligence.

Data warehouses are often used for reporting and analytics, providing insights into business performance, customer behavior, trends, and other key areas.

OLAP (Online Analytical Processing) is a technology used to analyze and access multidimensional data, allowing users to view data from different angles and perspectives.

Data mining is a process of extracting useful information and patterns from large datasets, often used in Data Warehouses for identifying trends, uncovering hidden relationships, and predicting future outcomes.

Data warehouses are designed to be highly scalable and performant, allowing them to accommodate large volumes of data and handle complex analytical queries efficiently.

Data warehouses are often used to support decision-making at all levels of the organization, from operational analysis to strategic planning.

Data warehouses are designed to be consistent and reliable, ensuring data quality and accuracy, which is crucial for making sound business decisions.

Data warehouses are designed to be secure, protecting sensitive information from unauthorized access or breaches, ensuring data confidentiality and integrity.

Data integration involves combining data from multiple sources into a unified format within a data warehouse, ensuring consistency and accuracy for analysis.

ETL (Extract, Transform, Load) is a process used to extract data from source systems, transform it into a standardized format, and load it into a data warehouse or data mart for analytical purposes.

Metadata plays a vital role in data warehouses by providing contextual information about the data, such as data sources, data quality, and data relationships, enabling efficient data analysis and interpretation.