Detailed Study Notes on Big Data & Business Intelligence

Overview of Big Data & Business Intelligence

Presentation Outline

  • Big Data

  • Business Intelligence

  • Why - Impacts

What is Big Data?

  • Definition: Big Data is the current buzzword in the business world, reshaping daily operations. It encompasses not only massive amounts of data but also the structure and processing of that data to provide tangible benefits to organizations.

Data Characteristics

  • Big Data Everywhere:

    • Data that is too large and complex for standard data tools to manage.

    • Examples:

    • Shares traded on US Stock Markets each day: 7 Billion transactions.

    • Data generated in one flight from New York to London: 10 Terabytes.

    • Tweets per day on Twitter: 400 Million.

    • Likes per day on Facebook: 3 Billion.

The 3 V's of Big Data

  1. Volume

    • Refers to the amount of data generated.

    • Data stored in kilobytes or terabytes via records, tables, or files.

  2. Variety

    • Denotes both structured and unstructured data types.

    • Examples involve online images, videos, human-generated texts, and machine-generated readings.

  3. Velocity

    • Indicates the speed of data generation and modification.

    • Data can come in streams, batches, or bits.

  • Statistics: 90% of the world’s data has been generated in the last two years.

Additional Characteristics

5 V's of Big Data

  1. Value

    • Access to big data is beneficial only if it can be converted into value for the organization.

  2. Veracity

    • Refers to the reliability and trustworthiness of data.

    • Essential for verifying and validating data accuracy.

Big Data with 8 V's

  1. Virality

    • Can the information be easily conveyed and shared?

  2. Viscosity

    • Does the information trigger action?

  3. Visualization

    • Can it be quickly understood, facilitating fast decision-making?

  4. Velocity

    • Do changes and opportunities arise swiftly in the information flow?

  5. Value

    • Is the information accessible when needed?

  6. Variety

    • Is the data well-balanced in representation?

Data Types

Structured Data

  • Definition: Data arranged in rows and columns.

  • Examples: Numerical values, dates, and strings.

  • Characteristics: Easier to manage and secure; estimated to represent about 20% of enterprise data (Gartner).

Unstructured Data

  • Definition: Data that cannot be neatly organized in rows and columns.

  • Examples: Images, audio, video, email, and word processing files.

  • Characteristics: Comprises about 80% of enterprise data; more demanding on storage and management.

Data Processing and Analytics

  • Data Analytics: Uses data, IT, statistical analysis, and models to allow managers a better grasp of their business operations for fact-based decision-making.

  • ### 4 Types of Data Analytics

    1. Descriptive Analytics

    • Question: What's happening in the business?

    • Outcome: Provides comprehensive, accurate, and real-time observation.

    1. Diagnostic Analytics

    • Question: Why is it happening?

    • Outcome: Isolates root causes and extraneous information.

    1. Predictive Analytics

    • Question: What's likely to occur?

    • Outcome: Uses historical patterns and algorithms to anticipate outcomes.

    1. Prescriptive Analytics

    • Question: What should I do?

    • Outcome: Involves recommended actions based on advanced analytical techniques.

Business Intelligence (BI)

  • Definition: While Big Data refers to large data sets, Business Intelligence is the process of performing analysis on that data. BI employs technology to extract valuable information from data, including normal and Big Data.

  • Components of BI:

    • Strategies and technologies for analyzing business data.

    • Merging enterprise information systems into a cohesive data warehouse for analytics.

Data Analytics Tools

Common Tools for BI

  • Tableau

  • Qlik Sense

  • Microsoft Power BI

  • Google Analytics

Common Tools for Big Data

  • Hadoop

  • Spark

  • Hive

  • Cassandra

  • Storm

Impacts of Big Data and BI

  1. Education

  2. Healthcare

  3. Agriculture

  4. Manufacturing

Data Management and Reporting Systems

  • Structure of Business Reporting Systems:

    • Focuses on collecting, processing, and delivering business information.

    • Enables decision-making at operational, tactical, and strategic levels.

Future Trends

  • Cloud Computing: Shifts towards online storage and collaboration models.

  • Increased reliance on analyzing large volumes of data in various formats can enhance business workflows.

Security and Ethical Considerations

  1. Ethics: Guiding principles for responsible technology use to prevent harm.

  2. Privacy: Controls regarding personal data usage and sharing.

  3. Security: Ensures confidentiality, integrity, and availability of information systems through various technical and administrative measures.

Conclusion

  • Understanding Big Data and Business Intelligence is crucial for effective decision-making and operational efficiency in modern businesses.