Enhancing Business Intelligence Using Big Data, Analytics, and Artificial Intelligence

Learning Objectives

• 6.1: Describe the need for business intelligence (BI), advanced analytics (AA), artificial intelligence (AI), and the foundational role of databases in business decision-making.
• 6.2: Explain core concepts of BI, AA, and AI.
• 6.3: Describe enhancement of decision-making through knowledge management and geographic information systems (GIS).

Enhancing Organizational Decision Making

• Business Intelligence (BI)

  • Tools and techniques for analyzing and visualizing past data.
  • Provides answers based on historical analysis.

• Advanced Analytics (AA)

  • Tools and techniques to:
  • Understand why events occurred.
  • Predict future outcomes.
  • Discover hidden patterns.

• Business Analytics: An umbrella term that encompasses both BI and AA.

• Applications of Analytics:

  • Political parties: Model social media influence on elections.
  • Government: Analyze public benefits program performance.
  • Defense: Manage logistics in overseas deployments.
  • Hospitals: Predict patient volume and resource needs.
  • Nonprofit organizations: Target donors in fundraising campaigns.

Importance of Business Intelligence and Advanced Analytics

• Data-Driven Organizations: Need accurate, integrated information.

  • Familiarity with data analysis tools is crucial for users.

• Responding to Threats and Opportunities: Enables swift organizational responses using analytics.

Understanding Big Data

• Businesses face challenges with Big Data characterized by:
  • High Volume: Unprecedented amounts of data.
  • High Variety: Structured and unstructured data types.
  • High Velocity: Rapid data processing is essential.

Continuous Planning Process

• Organizations must continuously monitor, analyze, and adapt their business processes based on data insights.

Databases as a Foundation for Analytics

• Databases are crucial in enabling:
  • E-commerce through centralized data storage (e.g., product catalogs, user information).
  • Processing millions of transactions efficiently.

Advantages of Databases

• Minimal data redundancy: Single data copies improve efficiency.
• Improved data consistency: Redundancy reduction enhances consistency.
• Increased security: Simplified enforcement of access restrictions.
• Enhanced data quality: Centralization helps optimize quality.
• Better data accessibility: Central systems facilitate easier access.
• Enforced standards: Standardizes data management rules.
• Increased application productivity: Lower maintenance costs and adaptation ease.

Effective Database Management

• Data Model: Diagram representing entities and their relationships.
• Data Dictionary: A repository of metadata explaining data attributes and rules.

Querying Data

• Ad Hoc Query: Created for unplanned information requests, often via GUI.
• Report Types:
  • Scheduled Reports: Predefined intervals (daily, weekly).
  • Key-Indicator Reports: Summaries of critical metrics.
  • Exception Reports: Highlight anomalies or outliers.
  • Drill-Down Reports: Provide detailed analyses.

Online Transaction Processing (OLTP)

• OLTP systems allow real-time transaction processing that supports business operations.
• The power of analytics comes from aggregating and analyzing data across systems (using OLAP).

Operational vs. Informational Systems

• Operational Systems: Focused on running business processes.
• Informational Systems: Support managerial decision-making through complex queries and analyses.

Data Warehouses and Data Lakes

• Data Warehouse: Integrates data from various sources into a single repository.
• Data Lake: A repository for storing raw structured, semi-structured, and unstructured data.

Data Marts

• A smaller, limited scope data warehouse focused on specific areas of an organization.