A3.4.3 Role of Online Analytical Processing (OLAP) and Data Mining for Business Intelligence Notes

Online Analytical Processing (OLAP)

1. Online Analytical Processing (OLAP) is a powerful tool for analyzing data stored in data warehouses.
2. It enables businesses to make informed decisions by providing multidimensional views of data.

How OLAP Works

1. Data Organization: Data is structured into OLAP cubes, which are multidimensional arrays that allow for complex queries.
2. Pre-Processing: Data is cleaned, transformed, and organized into cubes before analysis.
3. Multidimensional Analysis: Users can explore data from different perspectives, such as time, location, and product categories.

OLAP Operations

1. Roll-Up: Summarizes data by aggregating it along a hierarchy (e.g., daily sales to monthly sales).
2. Drill-Down: Allows users to explore detailed data by breaking down aggregates (e.g., monthly sales to daily sales).
3. Slice: Extracts a single dimension from the cube (e.g., sales data for a specific region).
4. Dice: Selects a sub-cube by specifying multiple dimensions (e.g., sales data for a specific product in a specific region).
5. Pivot: Rotates the cube to view data from different perspectives.

Data Mining

1. Data mining involves extracting meaningful patterns and insights from large datasets.
2. Unlike OLAP, which uses pre-processed data, data mining works directly with raw data.

Key Data Mining Techniques

1. Classification
   1. Definition: Assigns items to one of several predefined categories based on their attributes.
   2. How it works: A model is trained on labelled data and then used to classify new data.
   3. Example: An email spam filter classifies emails as either "spam" or "not spam" based on the content, sender, or subject line.
2. Clustering
   1. Definition: Groups data items into clusters of similar items, without any predefined categories.
   2. How it works: The algorithm identifies natural groupings within the data.
   3. Example: A marketing team uses clustering to group customers into segments based on spending habits and interests, even though the segments were not defined in advance.
3. Regression
   1. Definition: Predicts continuous numerical values based on relationships between input variables.
   2. How it works: Finds a mathematical model (like a line or curve) that fits the data.
   3. Example: A real estate company uses regression to predict house prices based on features like size, location, and number of bedrooms.
4. Association Rule Discovery
   1. Definition: Identifies relationships or patterns between items that frequently occur together.
   2. How it works: Discovers rules such as “If A happens, B is likely to happen”.
   3. Example: In supermarket data:
      "If a customer buys bread and milk, they are likely to buy butter."
5. Sequential Pattern Discovery
   1. Definition: Finds patterns in time-ordered data or sequences of events.
   2. How it works: Identifies the order in which events typically occur.
   3. Example: An online store finds that customers who buy a console often return later to buy games, then accessories.