topic: KM-01-KT07 Big Data

Learning Outcome

  • KT0701: Volume, Velocity, Variety, and Veracity
  • KT0702: Sources
  • KT0703: Applications
  • KT0704: Benefits

KT0701: Volume, Velocity, Variety, and Veracity (The Four Vs of Big Data)

Volume

  • Definition: The sheer amount of data that needs processing.
  • Characteristics: High volumes of low-density, unstructured data (e.g., Twitter feeds, web page clickstreams, sensor data).
  • Examples: Tens of terabytes to hundreds of petabytes of data for different organizations.

Velocity

  • Definition: The speed at which data is generated and needs to be processed.
  • Characteristics: Real-time or near real-time data that requires fast processing and action.
  • Examples: Smart products, web logs, sensor data.

Variety

  • Definition: The different types of data (structured, semi-structured, unstructured).
  • Characteristics: Traditional data was structured and fit neatly into relational databases. Big data includes unstructured and semi-structured types (e.g., text, audio, video) that require additional processing.
  • Examples: Social media posts, videos, sensor data, web logs.

Veracity

  • Definition: The trustworthiness and quality of data.
  • Characteristics: Big data is often messy, inconsistent, and incomplete, making it challenging to assess its accuracy.
  • Examples: Ensuring accurate analysis despite data discrepancies.

KT0702: Sources of Big Data

1. Social Data

  • Examples: Likes, tweets, comments, video uploads, and other social media interactions.
  • Use: Provides insights into consumer behavior, sentiment analysis, and market trends.

2. Machine Data

  • Examples: Data from industrial equipment, sensors, medical devices, smart meters, and the Internet of Things (IoT).
  • Use: High-velocity, high-volume data from various sources like machines and devices, especially important for industries like manufacturing, healthcare, and transportation.

3. Transactional Data

  • Examples: Data generated from transactions such as invoices, payment records, and delivery receipts.
  • Use: Provides insight into business activities, customer behavior, and supply chain operations.

Unlocking Real Value

  • Combining social, machine, and transactional data helps generate actionable insights that drive business decisions. CloudMoyo assists companies with analytics strategies to unlock this value.

KT0703: Applications of Big Data

1. Integration

  • Definition: Bringing together data from different sources and ensuring it’s formatted for analysis.
  • Challenges: Traditional integration tools (ETL) are often not enough for the scale and complexity of big data.

2. Management

  • Definition: Storing and managing big data using cloud or on-premises solutions.
  • Examples: Cloud storage for scalability, on-demand processing capabilities.

3. Analysis

  • Definition: Analyzing the data to uncover patterns, correlations, and predictions using tools like machine learning and AI.
  • Best Practices: Ensure business goals drive the use of big data and employ proper governance and standards.

KT0704: Benefits of Big Data

  1. Cost Reduction: Big data tools can help identify inefficiencies and reduce operational costs.

  2. Improved Efficiency: Saves time and resources by optimizing operations and reducing travel with digital tools.

  3. Better Pricing: Improves financial visibility and decision-making for optimal pricing strategies.

  4. Competitive Advantage: Small businesses can leverage big data tools to compete effectively with larger firms.

  5. Local Market Insights: Big data allows businesses to focus on and analyze local customer preferences for targeted strategies.

  6. Increased Sales and Loyalty: Personalizing products and services using customer data to drive sales.

  7. Improved Hiring: Using data to optimize recruitment by matching candidates with job requirements.

RAW CONTENT URL