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11.0 Big Data
11.3 Applications of Big Data
11.3.1 Business intelligence
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What is Business Intelligence (BI)?
Data analysis for informed decisions
The key components of BI include data collection, data analysis, reporting, and
visualization
Match the BI component with its purpose:
Data Collection ↔️ Centralizes and unifies data
Data Analysis ↔️ Reveals valuable insights
Reporting ↔️ Communicates key findings
Visualization ↔️ Enhances understanding
BI improves
decision-making
by providing data-driven insights.
Automated data collection and reporting in BI lead to increased
efficiency
How does BI enhance competitiveness for businesses?
Insights into market trends
Match the BI application with its description:
Sales Forecasting ↔️ Predicting future sales trends
Customer Segmentation ↔️ Identifying distinct customer groups
Performance Monitoring ↔️ Tracking key performance indicators
What are the three key characteristics of Big Data in BI?
Volume, variety, and velocity
Traditional data in BI is typically structured, while Big Data can be structured or
unstructured
Big Data processing in BI is slower compared to traditional data.
False
Steps in how Big Data enhances BI
1️⃣ Provides a complete view of the business
2️⃣ Enables advanced analytics and machine learning
3️⃣ Supports real-time decision-making
What is one advantage of using Big Data in BI?
Supports real-time decision-making
What are the three characteristics of Big Data?
Volume, variety, and velocity
Big Data has a faster velocity than traditional
data
Match the characteristic of Big Data with its description:
Volume ↔️ The amount of data
Variety ↔️ The type of data
Velocity ↔️ The speed of data processing
How can Big Data inform marketing strategies?
By analyzing customer behavior
By leveraging Big Data, businesses can improve operational
efficiency
Big Data enables advanced analytics and
machine learning
techniques in Business Intelligence.
What are the key components of Business Intelligence?
Data collection, analysis, reporting, visualization
Data analysis in BI reveals valuable patterns and
trends
Steps in the data collection process within Business Intelligence:
1️⃣ Gather data from multiple sources
2️⃣ Centralize data
3️⃣ Unify data
Match the BI component with its purpose:
Data Collection ↔️ Centralizes and unifies data
Data Analysis ↔️ Reveals valuable insights
Reporting ↔️ Communicates key findings
Visualization ↔️ Enhances understanding
Business Intelligence improves
decision-making
by providing data-driven insights.
Sales forecasting in BI uses historical sales data to predict future
trends
What is Business Intelligence (BI)?
Data analysis for decisions
Which characteristics of Big Data enhance its role in BI?
Volume, variety, and velocity
Match the BI technique with its example:
Data Mining ↔️ Identifying frequently bought together products
Machine Learning ↔️ Predicting customer churn
Statistical Analysis ↔️ Comparing sales performance using ANOVA
Data Visualization ↔️ Creating dashboards to monitor KPIs
What statistical test can be used to compare sales performance across regions?
ANOVA
Machine learning in BI is used for tasks like predicting customer
churn
Data visualization transforms
complex
data into clear and understandable formats.
What BI application predicts future sales trends based on historical data?
Sales forecasting
Customer segmentation in BI identifies distinct groups of customers for targeted
marketing
What is the primary goal of Business Intelligence (BI)?
Enhance decision-making
Sales forecasting in BI uses historical data to predict future sales
trends
Customer segmentation in BI divides customers based on
demographics
and purchase behavior for targeted marketing.
What does performance monitoring in BI track to assess business health?
Key performance indicators
Operational efficiency in BI involves analyzing production process data to reduce
waste
What are the four key components of Business Intelligence (BI)?
Data Collection, Analysis, Reporting, Visualization
Business Intelligence (BI) differs from traditional reporting by providing advanced analytics and
predictions
.
Match the BI feature with its description:
Advanced Data Analysis ↔️ Identifies patterns and trends
Predictive Analytics ↔️ Forecasts future outcomes
Interactive Dashboards ↔️ Presents data visually
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