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Questions and Answers
What is the primary goal of Business Intelligence?
Which of the following is considered a main challenge in building Business Intelligence solutions?
Which process is involved in consolidating data and often consumes a majority of development time?
Which of the following elements is not a component of the data warehouse?
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Who drives what should be included in the data warehouse?
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Which ETL tool is specifically mentioned as an option?
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What type of users are typically associated with needing static reports or limited analytical power?
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Which of these factors complicates data consolidation in a Business Intelligence framework?
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How do line workers typically interact with Business Intelligence?
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Which step is not part of the ETL process?
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What term describes the structures in which data is stored within a data warehouse?
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Which of the following is NOT typically considered a measure?
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Which of these best describes what dimensions represent in a data warehouse?
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In a cube, how are users able to access data?
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Which of the following is considered an attribute within a dimension?
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What is the primary purpose of a hierarchy in BI analysis?
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Which of the following statements about KPIs is FALSE?
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What does the acronym ETL stand for in the context of data warehousing?
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Which of the following is NOT a dimension type commonly used in BI analysis?
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Which of the following reflects a common hierarchy structure for temporal data?
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Study Notes
Business Intelligence (BI)
- BI encompasses data warehousing, business analytics and knowledge management.
- BI is a process used to convert data into information, information into knowledge, and knowledge into actionable plans that drive profitable business actions.
Challenges of Building BI Solutions
- Data is scattered across various locations.
- Data formatting does not support complex analysis.
- Different data needs for various workers.
- Determining what data to analyze and the level of detail required.
- Defining user interaction with data.
Data Consolidation
- The process of consolidating data involves moving, making consistent, and cleaning up as much data as possible.
Extraction, Transformation, and Loading (ETL)
- The data consolidation process is referred to as Extraction, Transformation, and Loading (ETL).
- ETL extracts data from multiple source systems.
- Data is then transformed to ensure consistency and improve data quality.
- Consolidated, consistent, and cleaned data is loaded into a data repository.
- ETL development typically consumes 80% of the development time.
ETL Tools
- Common ETL Tools:
- Oracle Data Integrator (ODI)
- Informatica
- IBM Ascential
- Abinitio
Business Considerations for Data Consolidation
- Business users should drive the content of the data warehouse.
- Business stakeholders are responsible for deciding how to consolidate inconsistent data.
- The business must determine how to handle additional requirements, such as currency conversions.
Users of Business Intelligence
- Executives and decision makers: Utilize high-level business views with limited analysis.
- Analysts: Perform complex, in-depth data analysis.
- Information workers: Require static reports or limited analytical capabilities.
- Line workers: No analytical capabilities, BI is integrated into their daily tasks.
Components of a Data Warehouse
- Cubes: Structures used for data storage. Users navigate through dimensions to access data in cubes.
- Measures: Represent the desired data. Typically numeric and often additive (e.g., sales, profit, expenses).
- Key Performance Indicators (KPIs): Quantifiable metrics used to track performance.
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Dimensions: Used to view data. Common dimensions include time, geography, product, account, employee, etc.
- Attributes: Individual values within a dimension (e.g., Month, Year, Country, Region, City, Part Number, Size, Color, Manufacturer).
- Hierarchies: Structures that organize attributes, enabling drill-down analysis (e.g., Year to Quarter to Month to Day).
Asking a BI Question
- BI facilitates multidimensional thinking.
- Users typically want to see a specific value in a particular context (e.g., "Show me sales by month by product for North America").
- Measures represent the desired data (sales in this example).
- Dimensions define how the data is viewed (month, product, North America in this example).
ETL Process Summary
- The ETL process is responsible for extracting data from source systems, transforming it, and loading it into a data warehouse or data mart.
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Description
Explore the key concepts of Business Intelligence (BI) including data warehousing, analytics, and knowledge management. Learn about the challenges faced in building BI solutions and the importance of Extraction, Transformation, and Loading (ETL) processes in data consolidation.
