Business Intelligence and Data Warehousing Concepts

False (B)

Facts and Dimensions

<p>Star</p> Signup and view all the answers

<p>Facts = Quantitative metrics like sales revenue Dimensions = Qualitative attributes providing context ETL Processes = Transform raw data for analysis OLAP Cubes = Multi-dimensional data structures for quick analysis</p> Signup and view all the answers

<p>Creating transactional systems (A)</p> Signup and view all the answers

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

<p>It is intuitive and optimized for high-performance data retrieval.</p> Signup and view all the answers

<p>To achieve integration and consistency within the data warehouse (D)</p> Signup and view all the answers

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

<p>Analyzing advertising effectiveness on revenue (A)</p> Signup and view all the answers

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

<p>Data marts allow for faster results by addressing specific business needs early.</p> Signup and view all the answers

<p>Data Granularity Alignment</p> Signup and view all the answers

<p>drilling across</p> Signup and view all the answers

<p>Conformed</p> Signup and view all the answers

<p>Slowly Changing Dimensions = Handling changes over time in data Data Mart = A smaller, functional component of a data warehouse Conformed Dimensions = Dimensions that are shared across multiple fact tables Data Governance = Ensuring data integrity and standardization</p> Signup and view all the answers

<p>Use pre-aggregated views (B)</p> Signup and view all the answers

<p>Integration Techniques (B)</p> Signup and view all the answers

<p>Sales and Marketing = Analyze impact of promotional campaigns on sales Inventory and Fulfillment = Link inventory levels with order fulfillment rates Financial Metrics = Combine financial performance with operational data Operational Metrics = Assess profitability across business operations</p> Signup and view all the answers

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

<p>To develop a clear architectural plan for shared dimensions and business processes.</p> Signup and view all the answers

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

<p>Regularly validate and enforce conformity</p> Signup and view all the answers

<p>Provides actionable insights sooner (D)</p> Signup and view all the answers

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

<p>Utilize conformed dimensions and incremental design</p> Signup and view all the answers

<p>flexibility</p> Signup and view all the answers

<p>Balancing Speed and Architectural Integrity = Develop a strong foundational architecture Managing Technical Debt = Regularly revisit and refactor earlier implementations Ensuring Data Consistency Across Increments = Use standardized ETL processes Stakeholder Engagement = Establish clear roles and feedback sessions</p> Signup and view all the answers

<p>Sprints (D)</p> Signup and view all the answers

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

<p>flexibility</p> Signup and view all the answers

<p>Continuous feedback and adaptation (B)</p> Signup and view all the answers

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

<p>The amount of work completed in a sprint</p> Signup and view all the answers

<p>sprints</p> Signup and view all the answers

<p>Technical Debt = Short-term trade-offs in development Kanban = Visual workflow management system User Stories = Requirements framed from the user's perspective Incremental Design = Building the data warehouse one component at a time</p> Signup and view all the answers

<p>Business Value Delivered (D)</p> Signup and view all the answers

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

<p>How effectively users are utilizing delivered features</p> Signup and view all the answers

<p>To facilitate organizational decision making (D)</p> Signup and view all the answers

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

<p>Subject-oriented, Integrated, Time-varying, Non-volatile</p> Signup and view all the answers

<p>Bus Matrix</p> Signup and view all the answers

<p>ETL is used for collecting, cleaning, and loading data into a data warehouse. (B)</p> Signup and view all the answers

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

<p>capture</p> Signup and view all the answers

Data warehousing consolidates data from various sources to support business decisions, performance measurement, and predictive analytics.

Systems designed for everyday tasks like order entry and customer management, capturing data for everyday use.

A table that stores quantitative measures in a data warehouse.

A table that stores descriptive attributes of the data (e.g., time, product, customer).

A relational database design where a central fact table links to multiple dimension tables.

Processes that transform raw data into structured data format for data warehousing.

The process of collecting and analyzing data to find solutions and answers to business questions like sales trends.

A method to organize data into facts and dimensions to easily analyze data and aid fast data retrieval.

Standardized dimensions used across multiple data marts or subject areas in a data warehouse.

Combining data from different fact tables to analyze interconnected business processes.

Ensuring fact tables have data at a consistent level of detail for effective analysis.

Challenges related to query speed when joining large fact tables.

Maintaining the same meaning and structure for dimensions across different tables.

Enhanced reporting features that can answer complex business questions, like forecasting.

Using correlated data across fact tables to reveal patterns for better business decisions.

Building a data warehouse by creating data marts one at a time, each focusing on a specific business area.

A flexible, iterative approach to software development where work is done in short cycles, with continuous feedback and adaptation.

Agile development prioritizes continuous customer feedback, flexibility, and rapid delivery in short cycles.

Short, focused development cycles aimed at delivering specific functionalities or features.

A visual workflow management system that helps prioritize and track tasks in a project or process.

A metric that tracks the amount of work a team completes in a sprint, often measured in story points or tasks.

A metric that measures the impact of delivered components, like data marts or reports, on business operations.

Metrics that evaluate how well delivered features, like dashboards or analytical tools, are used and how satisfied users are.

Dimensions used consistently across multiple fact tables, ensuring data integration and consistency in a data warehouse. This promotes a unified view of data and avoids inconsistencies between different analytical reports.

A dimension serving multiple purposes in analysis (e.g., 'Date' as both 'Order Date' and 'Ship Date'). It enables different perspectives on the same data and enhances flexibility in queries.

A planning tool used in data warehousing to identify dimensions and business processes that will share data. It helps visualize the relationships between dimensions and fact tables, promoting efficient data integration.

Querying multiple fact tables through shared dimensions to gain a holistic view of data, allowing for integrated analysis across different business areas.

Smaller, focused data warehouses that address specific business requirements or departmental needs. They serve as building blocks for the larger enterprise data warehouse, providing quick access to relevant data.

Reduces complexity and risk by breaking down the project into manageable phases, enabling faster delivery of value by addressing high-priority needs first, and facilitates flexibility to adapt to changing business needs.

Start with high-priority business needs, build smaller data marts that align with the broader EDW architecture, and gradually expand to a comprehensive data warehouse over time.

Engaging business users early and continuously to ensure alignment with business needs throughout the data warehouse project. This involves seeking their feedback frequently to refine and improve solutions.

Refining and enhancing the data warehouse and BI tools based on user feedback and changing requirements. This iterative approach ensures the system stays relevant and meets evolving business needs.

A short, time-boxed development cycle (typically 2-4 weeks) used in Agile development. Sprints focus on delivering a specific set of features or functionalities.

Compromises made in design or code for faster delivery but require fixing later. This can lead to long-term maintenance issues and hinder the system's efficiency.

Maintaining a strong foundational architecture while iterating quickly. This ensures the data warehouse is scalable and maintainable while still delivering value promptly.

A data warehouse stores all information about subjects across the entire organization, while a data mart is a subset focused on a single business process. Data marts are faster to implement but require more complex integration in the long run.

A system designed for capturing data quickly and efficiently during daily operations. It's not optimized for complex analysis.

Data warehouses are subject-oriented, integrated, time-varying, non-volatile, and used for decision-making. They are separate from operational databases.

This framework connects a 'Theme' (central goal) to 'Critical Success Factors' (data elements crucial for achieving the goal) and then to specific 'Business Questions' that can be answered using data to evaluate progress toward the goal.

Analyzing sales data can reveal trends, customer preferences, and market patterns, helping to make informed decisions about pricing, promotions, product development, and market segmentation.