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What is the primary function of business intelligence?
Which of the following techniques is NOT typically associated with predictive analytics?
Which component is essential for the infrastructure of business intelligence?
In which area can predictive analytics be particularly beneficial?
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Big data analytics often incorporates data from which of the following sources?
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What role do predictive analytics play in decision-support systems?
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Which of the following is a primary benefit of implementing big data analytics in retail?
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What is a key characteristic of the data used in predictive analytics?
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What is one of the main challenges faced when implementing enterprise applications?
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Which of the following is a characteristic of next-generation enterprise applications?
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What does Social CRM primarily incorporate?
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Which of the following is NOT a benefit of incorporating business intelligence with enterprise applications?
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What is a key feature of cloud-based enterprise applications?
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What is primarily required for effective data standardization, management, and cleansing?
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Why is organizational learning considered a challenge in enterprise application implementation?
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Which of the following best describes a database in the context of enterprise applications?
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What is the primary purpose of a data warehouse?
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How do data marts differ from data warehouses?
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What role does Hadoop play in big data processing?
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What is a significant advantage of in-memory computing in big data analysis?
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Which of the following is NOT a key service associated with Hadoop?
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What characterizes analytic platforms in the context of big data?
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Which aspect of data warehouses ensures that the information remains unchanged?
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What type of users typically benefit from data marts?
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Study Notes
Business Intelligence
- A system for collecting, storing, and analyzing data produced by businesses.
- Includes databases, data warehouses, and data marts
- Vendors create and sell the tools needed for BI
Business Analytics
- Tools and techniques for analyzing data
- Includes Online Analytical Processing (OLAP), statistics, models, and data mining
- These tools are often used to support business intelligence
Business Intelligence and Analytics for Decision Support
- Analyze company data to inform future business decisions
- Can be used by many roles within the organization
Predictive Analytics
- Uses data to predict future trends and behaviors
- Includes statistical analysis, data mining, and data from past experiences
- The goal is to create assumptions about the future
- Often used in sales, marketing, finance, fraud detection, and healthcare.
- Examples include: credit scoring, predicting marketing campaign responses
Big Data Analytics
- Analyzing massive datasets collected from sources like social media, online transactions, and customer data.
- Enables real-time, personalized shopping experiences for online retailers.
- Can also be used for the analysis of smart cities, using publicly available data, sensor information, and location data from smartphones.
Business Intelligence Infrastructure
- Consists of tools used for obtaining data from different business systems and big data sources.
Data Warehouse
- Stores a firm's current and historical data from its core operational transaction systems.
- Consolidates and standardizes information for use by the entire organization.
- Cannot be altered
Data Marts
- A subset of data warehouses
- Summarized and focused portion of data for specific users
- Typically focuses on a single subject or line of business
Hadoop
- Enables the distributed parallel processing of big data across inexpensive computers
- Includes several key services:
- Hadoop Distributed File System (HDFS) to store data
- MapReduce to break data into clusters for processing
- Hbase to store data in a NoSQL database
- Utilized by companies like Yahoo and NextBio
In-Memory Computing
- Used for Big Data analysis
- Stores data in the computer’s main memory (RAM) to avoid delays in retrieving data from disk storage
- Can reduce processing time from hours or days into seconds
- Requires optimized hardware
Analytic Platforms
- High-speed platforms that use relational and non-relational tools
- Optimized for large datasets
Enterprise Application Challenges
- Enterprise applications can be expensive to purchase and implement
- Many projects experience cost overruns and long development times
- Technologies, business processes, and organizational learning can all change, adding complexity
- Switching costs and dependence on software vendors present challenges
- Data standardization, management, and cleansing are critical for accurate analysis
Next-Generation Enterprise Applications
- Enterprise solutions/suites
- Makes applications more flexible, web-enabled, and integrated with other systems.
- Includes cloud-based versions and versions for mobile platforms
- Also available for small and medium-sized businesses
Next-Generation Enterprise Applications Continued
- Social CRM
- Incorporates social networking technologies
- Company social networks track social media activity
- Used for social media analytics and campaigns
- Business intelligence
- Integrated with other enterprise applications
- Enables flexible reporting, ad hoc analysis, "what-if" scenarios, digital dashboards, data visualization, and AI machine learning
Databases
- An organized collection of data stored centrally for various informational system applications
Why Collect Data?
- To enable and improve business operations
- To support decision making
- To increase efficiency
- To gain competitive advantages
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Description
This quiz covers the fundamentals of Business Intelligence and Analytics, including key concepts, tools, and techniques used to analyze data for decision-making in organizations. Learn about the role of predictive analytics, big data, and how these processes support strategic business goals.
