In-Depth Notes on Business Analytics, Business Intelligence, and Knowledge Management

Business Analytics and Business Intelligence

Business Analytics (BA):
Extensive use of data and quantitative analysis.
Supports fact-based decision making within organizations.
Uses data to:
- Understand current business performance.
- Reveal new business patterns and relationships.
- Explain results and optimize operations.
- Forecast future business results.
Business Intelligence (BI):
Encompasses various applications, practices, and technologies.
Aims to extract, transform, integrate, visualize, analyze, interpret, and present data.
Goal is to make data understandable for effective decision making.

Detect Fraud:
Example: MetLife used analytical software for fraud detection in claims, resulting in a 16% increase in claims under investigation.
Improve Forecasting:
Example: Kroger reduced out-of-stock prescriptions by 1.5 million/year through improved inventory management, increasing sales by $80 million/year and reducing inventory costs by $120 million/year.
Increase Sales:
Example: DaimlerChrysler optimized pricing using a price-elasticity model, generating an additional $500 million in annual sales.
Optimize Operations:
Example: Chevron’s Petro system maximizes profit by advising on product mix based on constantly changing crude oil prices.
Reduce Costs:
Example: Coca-Cola Enterprises implemented a vehicle-routing optimization system, saving $45 million annually.

Spreadsheets:
Commonly used for data import, operations based on formulas, and creating reports/graphs (e.g., Excel).
Reporting and Querying Tools:
Help in gathering data for problem solving.
Enable users to format results independently.
Data Visualization Tools:
Present data in graphical formats, making trends and relationships easier to see.
- Word Cloud: Summarizes frequent terms in user feedback, providing a visual overview.
- Conversion Funnel: Visualizes steps a consumer takes to purchase, highlighting points of confusion.
Online Analytical Processing (OLAP):
Analyzes multidimensional data, allowing trend analysis and identifying opportunities.
Drill-Down Analysis:
Examines detailed data from high-level summaries, helping to understand underlying issues.
Linear Regression:
Predicts dependent variable values based on relationships with independent variables; does not imply causation.
Data Mining:
Explores large datasets for hidden patterns predicting future trends; includes techniques like Association Analysis and Neural Computing.
Dashboards:
Displays metrics (KPIs) for monitoring organizational goals, offering real-time access to information for decision-making.
Self-Service Analytics:
Empowers users to analyze data independently, improving decision-making speed and accuracy.

Definition:
Involves practices to increase awareness, foster collaboration, and facilitate knowledge sharing.
Aims to enable systematic knowledge creation, sharing, and application.
Systems and Key Components:
Explicit Knowledge: Documented information (e.g., procedures, research).
Tacit Knowledge: Intangible skills and perspectives gained from experience.
Communities of Practice (CoP): Groups sharing knowledge and strategies in specific areas of interest.
Organizational Network Analysis (ONA): Measures information flow and knowledge gaps; identifies experts for collaboration.
Web 2.0 Technologies: Enhances collaboration with tools like blogs and wikis to support knowledge management.
Business Rules Management Systems (BRMS): Enables business users to manage decision logic without IT bottlenecks.
Enterprise Search Software: Searches internal data sources for relevant information based on user queries.
Electronic Discovery (E-Discovery): Process of locating and securing electronic data for legal purposes, governed by federal rules.

Leary, T. (2021). Computing essentials. McGraw-Hill.
Polle, T. (2019). Fundamentals of information systems. Springer.
Stair, R., & Reynolds, G. (2018). Fundamentals of information systems. Cengage Learning.