Business Intelligence Architecture

1. Business Intelligence (BI) Architecture

Business Intelligence Architecture defines the blueprint for how organizational data is collected, integrated, stored, analyzed, and distributed to support data-driven decisions.

Components with Examples

• ETL = Extract → Transform → Load.
• Data warehouse stores integrated, subject-oriented, non-volatile, time-variant data.

2. Business Intelligence Cycle

Phases and Examples

3. Binning (Discretization)

Binning converts continuous numeric values into discrete categorical bins to simplify data and reduce noise.

A. Equal Width Binning

Steps & Example:

Dataset: 10, 15, 18, 20, 31, 34, 41, 46, 51, 53, 54 Bins = 4 Formula: Bin Width=(max-min)/4= (54–10)/4=11

B. Equal Frequency Binning

Each bin has equal number of elements.

Dataset: 10, 15, 18, 20, 31, 34, 41, 46, 51, 53, 54, 60 Bins = 3 → 12/3 = 4 per bin

Purpose Example:

• If “Age” is continuous (e.g., 1–99), bin into “Child”, “Adult”, “Senior”.
• Helps in decision trees or visualization.

4. Data Smoothing

Removes noise and irregularities after binning to improve data quality.

Example Dataset

Marks: 4, 7, 13, 16, 20, 24, 27, 29, 31, 33, 38, 42 Use Equal-Frequency Binning (3 bins, 4 per bin):

A. Smoothing by Bin Mean

Replace all values with mean.

B. Smoothing by Bin Boundaries

Replace values with min or max (closest boundary).

• Mean = best for reducing outliers
• Boundary = best for retaining value limits

5. Dealing with Missing Values

Methods with Examples

Use predictive models (e.g., decision trees or k-means) when missing data is not random.

6. Data Normalization

A. Min-Max Normalization

Scales values to [0, 1].

B. Z-Score Normalization

Scales using mean (μ) and std. dev. (σ).

C. Decimal Scaling

• Min-Max: preserves relationships.
• Z-score: removes mean bias.
• Decimal: simplest computationally.

7. Chi-Square Test (χ²)

Statistical test to check whether difference between observed and expected frequencies is due to chance or actual relationship.

• Independence → relationship between variables
• Goodness-of-Fit → fit with expected distribution

8. Covariance

Measures direction of linear relationship between two variables.

Covariance only shows direction, not strength (for that use correlation).

9. Data Warehouse Schemas