Data increases exponentially with time

• What do data growth and complexity refer to?

  Expanding volume and variety of data
• What challenges do organizations face due to data growth and complexity?

  Storage, processing, accessibility, and security
• What are the characteristics of simple and complex data?

  • Simple Data:
  • Volume: Small
  • Variety: Homogeneous
  • Velocity: Slow
  • Complex Data:
  • Volume: Large
  • Variety: Heterogeneous
  • Velocity: Fast
• How does data volume grow over time?

  Exponentially
• What does exponential growth mean in data volume?

  Data doubles or more within short periods
• What are the key reasons for data volume growth?

  • Increased use of technology
  • Digital transformation
  • Advanced data collection methods
• Compare linear and exponential growth in data volume.

  • Linear Growth:
  • Constant rate
  • Example: Hiring 10 new employees each year
  • Exponential Growth:
  • Doubles or more quickly
  • Example: Increasing number of internet users
• What challenges arise from exponential data growth?

  Significant data management challenges
• What are the internal and external data sources?

  • Internal Sources:
  • Data generated within the organization (e.g., sales data)
  • External Sources:
  • Data obtained from outside (e.g., market research)
• What are the different types of data?

  • Structured Data: Organized in tables (e.g., SQL databases)
  • Semi-structured Data: Uses tags (e.g., JSON files)
  • Unstructured Data: No predefined format (e.g., emails)
• Why is it important to understand data sources and types?

  It helps manage data effectively and improve decision-making
• How does exponential data growth impact storage capacity?

  Traditional storage solutions become inadequate
• What are the characteristics of traditional and cloud storage?

  • Traditional Storage:
  • On-premises hardware
  • Control and security
  • Limited scalability
  • Cloud Storage:
  • Off-site infrastructure
  • Scalability and flexibility
  • Security concerns
• Why do organizations prefer cloud storage?

  For scalability and cost-effectiveness
• What are the key stages of data processing requirements?

  1. Data Acquisition
  2. Data Preparation
  3. Data Analysis
  4. Data Reporting
• What is required during data acquisition?

  Efficient ETL processes
• What is involved in data preparation?

  Cleaning, transforming, and structuring data
• What techniques are used in data analysis?

  Machine learning and statistics
• What is the purpose of data reporting?

  Presenting findings through dashboards and reports
• Compare traditional and modern data processing methods.

  • Traditional Methods:
  • Slow processing speed
  • Limited scalability
  • Inefficient resource utilization
  • Modern Techniques:
  • Fast processing speed
  • Highly scalable
  • Optimized resource utilization
• What factors affect data accessibility?

  • Data Location
  • Storage Format
  • Access Controls
• How do traditional access methods compare to modern techniques?

  Traditional methods lack scalability compared to modern techniques
• What are the features of data quality and integrity?

  • Data Quality:
  • Accuracy, completeness, consistency, relevance
  • Data Integrity:
  • Consistency, reliability, auditability, security
• What does good data quality mean?

  Data are accurate and useful
• What ensures data integrity?

  Data remains unaltered and reliable over time
• What are the criteria for excellent and poor data quality?

  • Excellent Data Quality:
  • 99.9% accuracy
  • No missing values
  • Consistent values across tables
  • Poor Data Quality:
  • Contains typos
  • Multiple missing fields
  • Contradictory entries
• What is necessary for maintaining high data quality and integrity?

  Regular audits, validations, and governance policies
• How can we relate data growth to toys?

  • Data growth is like toys growing in number.
  • Toys come from internal (own house) and external (friends) sources.
  • Different types of toys represent structured, semi-structured, and unstructured data.
  • Need for larger storage (toy boxes) relates to cloud storage.
  • Playing with toys parallels data processing stages.