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Questions and Answers
What is the primary focus of business analytics?
Which type of analytics involves analyzing past data to explain why certain events occurred?
How does embedded analytics primarily differ from other types of analytics?
What is the purpose of predictive analytics?
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What is the primary function of prescriptive analytics?
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Which scenario best exemplifies web analytics?
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In which scenario is deep learning used?
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What is a key feature of cloud analytics?
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What distinguishes machine learning from other forms of analytics?
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What is the primary role of business intelligence in an organization?
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How does edge technology differ from traditional data processing?
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What is the role of data mining in retail?
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Which of the following describes decision intelligence?
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Which technology helps to overlay digital information onto the real world?
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What is the purpose of data storytelling?
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What does blockchain technology primarily ensure?
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What is the primary goal of data cleansing?
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What does data democratization involve?
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What does data quality management ensure?
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What is the purpose of data lineage?
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Which of the following describes data consumption?
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What is the function of data integration?
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What does data engineering primarily focus on?
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What does data warehousing involve?
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Study Notes
Text Analytics
- Involves analyzing text data to extract insights.
Business Analytics
- Uses data for informed business decisions focused on current and future outcomes.
- Example: Analyzing customer reviews to identify common complaints.
Business Intelligence
- Employs technology and data to assess business performance and aid decision-making.
- Example: Restaurant dashboard displaying popular meal selections.
Descriptive Analytics
- Examines historical data to comprehend past events.
- Example: A store reviews sales data for busy months.
Diagnostic Analytics
- Investigates data to uncover reasons behind trends.
- Example: Analyzing customer feedback after a sales decline for insights.
Predictive Analytics
- Utilizes data to forecast upcoming trends or events.
- Example: Weather app predicting rain based on patterns.
Prescriptive Analytics
- Offers data-driven recommendations to enhance decision-making.
- Example: Navigation app suggesting routes based on traffic conditions.
Real-time Analytics
- Analyzes incoming data as it becomes available.
- Example: Social media platforms showing current live video viewership.
Cloud Analytics
- Involves utilizing cloud technology for data storage and analysis.
- Example: Company using Google Drive for data analysis.
Edge Technology
- Processes data near its source to reduce latency.
- Example: Smartwatches analyzing fitness data locally.
Blockchain Technology
- A secure method for recording information, reducing the risk of tampering.
- Example: Cryptocurrencies like Bitcoin utilize blockchain for transaction tracking.
Data Mining
- The practice of discovering patterns within large datasets.
- Example: Online stores identifying frequently bought products.
Data Discovery
- Analyzes data to uncover new, significant insights.
- Example: Identifying seasonal product sales through analysis.
Data Consumption
- How organizations leverage data for decision-making.
- Example: Using sales data to inform inventory orders.
Data Integration
- Combining data from various sources for a cohesive view.
- Example: Collecting data from a website and physical store into one report.
Data Cleansing
- Involves correcting or eliminating inaccurate data.
- Example: Fixing customer contact errors before marketing campaigns.
Data Democratization
- Providing access to data for all employees, not limited to specialists.
- Example: Allowing all staff to utilize data dashboards.
Data Literacy
- Refers to the ability to effectively understand and use data.
- Example: Enhancing employee skills to enable data-driven decisions.
Data Governance
- Ensures data quality, security, and responsible use.
- Example: Establishing protocols for handling customer data.
Data Quality Management
- Focuses on maintaining data accuracy and usefulness.
- Example: Routine checks on customer data for integrity.
Data Engineering
- Concentrates on creating systems for data collection, storage, and analysis.
Data Steward
- Responsible for data management and protection within an organization.
- Example: Like a librarian, they ensure data security and compliance.
Data Lineage
- Tracks the origin and journey of data throughout a system.
Data Fabric
- A unified system that manages and connects data across different platforms.
Data Warehousing
- The process of storing extensive data collections for analytical purposes.
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Description
This quiz focuses on text analytics and business analytics, exploring how organizations can leverage data to derive insights and make informed decisions. It includes scenarios where companies analyze customer feedback and sales data for better business outcomes.
