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Questions and Answers
Structured data makes up more than 20 percent of the total data landscape.
False
Unstructured data can be stored in a row-column database format.
False
Recent advancements in artificial intelligence have improved the ability to analyze unstructured data.
True
Examples of structured data include social media posts and emails.
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Data analytics methodologies primarily deal with structured data and do not encompass unstructured data.
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Unstructured data always lacks any form of structured information.
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Data analytics solely focuses on the analysis of pre-existing datasets.
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In manufacturing, data analytics can enhance process analysis through the use of sensors.
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Sentiment analysis is a statistical method used to evaluate logical fallacies in customer reviews.
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Geo tagging is an example of metadata that can be included with unstructured data, like digital images.
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Location-based marketing irrelevantly targets consumers regardless of their previous purchase behavior.
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Real-time data processing is not applicable in logistics and supply chain management.
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Data tagging provides a framework that helps in organizing and retrieving unstructured data effectively.
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Semi-structured data fully adheres to the strict structure of relational databases.
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RFID readings are considered unstructured data.
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Utility billing records are an example of structured data.
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Text files are a type of structured data.
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Telecommunications call detail records classify as structured data due to their fixed format.
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Metadata plays a role in the organization of semi-structured data.
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Healthcare claims are classified as unstructured data.
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Real-time data processing is unnecessary for most data analytics applications.
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Study Notes
Data Types
- Structured Data is organized into a row-column format, often used in relational databases
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Unstructured Data is not organized in a predefined format and lacks a data model
- Examples include email messages, multimedia content, social media posts, and machine-to-machine communications
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Semi-structured Data combines features of both structured and unstructured data
- Examples include email correspondence, telecommunications records, and electronic medical records
Data Analytics
- A comprehensive field utilizing analytical methods to extract information from data
- Involves data analysis, collection methodologies, and storage frameworks
Big Data
- 3Vs refer to volume, velocity, and variety of data, characterizing the Big Data environment
- Structured Data accounts for less than 20% of total data
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Unstructured Data constitutes the majority of data
- Previously disregarded but now processed using artificial intelligence and machine learning
Examples of Structured Data
- Financial data, accounting transactions
- Address details, demographic information
- Customer ratings, machine logs
- Location data from smart devices
Industry Examples of Unstructured Data
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Retail
- Cross-selling promotions
- Targeted location-based marketing
- Sentiment analysis of customer reviews
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Manufacturing and Logistics
- Real-time supply chain information
- Process analysis using sensors
- Logistics optimization
- Predictive maintenance
- Supply chain optimization
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Description
This quiz covers the fundamental concepts of data types including structured, unstructured, and semi-structured data. Additionally, it explores the principles of data analytics and the characteristics of Big Data, focusing on its three V's. Test your understanding of these essential topics in data science.
