Business Intelligence Chapter 2

Business Intelligence, Analytics, and Data Science: A Managerial Perspective

• Chapter 2 focuses on descriptive analytics, covering data nature, statistical modeling, and visualization.
• Data is a collection of facts, often obtained from experiences, observations, or experiments.
• Data types include numbers, words, images, and more.
• Data is the foundational element for deriving information and knowledge.
• Data quality and integrity are crucial for analytics. Data integrity encompasses accuracy, completeness, consistency, and validity.
• Metrics for analytics-ready data include source reliability, content accuracy, accessibility, security/privacy, richness, consistency, currency/timeliness, validity, and granularity.
• Data is categorized into structured, unstructured, and semi-structured forms.
• Structured data is standardized, follows a format, and is easily accessed (e.g., names, dates, addresses, etc.).
• Unstructured data includes any combination of text, images, voice, and web content.
• Semi-structured data falls between structured and unstructured, with some organizational structure (e.g., XML, JSON, log files).

Data Categorization

• Categorical variables represent types or groups (e.g., race, sex, age group).
  • Nominal data are used for labeling without quantitative value (e.g., gender, color).
  • Ordinal data have an inherent order (e.g., Likert scale, educational level).
• Numerical variables represent measured values that can be logically ordered.
  • Interval data have order and difference between values (e.g., temperature).
  • Ratio data have order, difference, and a meaningful zero point (e.g., height, income).

Data Preprocessing

• Real-world data is often dirty, requiring preprocessing for analytics.
• Data preprocessing involves data consolidation, cleaning, transforming, and reduction.
  • Data reduction techniques include dimensional reduction and variable selection for variables and sampling/stratification for cases/samples.

Statistical Modeling

• Statistics is a set of mathematical techniques to characterize and interpret data.
  • Descriptive statistics describe data, while inferential statistics draw inferences about a population from sample data.
• Measures of central tendency include the arithmetic mean, median, and mode.

Dispersion

• Dispersion, or variability, measures the spread or variation in a given variable.
• Measures of dispersion include range (max - min), variance, standard deviation, and mean absolute deviation (MAD).

Data Visualization Techniques

• Data visualization uses visual representations to explore, make sense of, and communicate data.
• Visualizations can span from histograms to graphs, charts, illustrations, etc.
• Visual analytics combines information visualization with predictive analytics techniques.
• Performance dashboards are used for combining and visualising key information from multiple sources.

Regression Modeling

• Regression is a technique in statistics used to understand or model the relationship between variables.
• Regression can be used to build models that allow for prediction and analysis of data.
• It typically involves determining the explanatory (input) and response (output) variables.

Business Reporting

• Reports translate information into actionable decisions.
• Reports involve various functions, such as communication, maintaining departmental efficiency, providing analysis results, persuasion, and knowledge management for the organization.
• Business reports may vary in format, distribution, and source of information.
• Reports can utilize key performance indicators (KPIs), and can use various types of visualizations to support the presentation.