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.
6. Anomaly Detection
   1. Definition: Identifies outliers, data points that do not follow expected patterns.
   2. How it works: Flags anything that looks suspicious or unusual compared to the norm.
   3. Example: A bank uses anomaly detection to identify fraudulent transactions, e.g. a sudden large withdrawal from an overseas location.

Applications of Data Mining

1. Marketing
   1. Data mining helps companies understand their target market and how they respond to media campaigns.
   2. Allows marketers to identify which adverts work, and which are ignored.
   3. Enables personalised advertising based on customer behaviour.
2. Sales
   1. Used to analyse sales trends across different products and locations.
   2. Helps answer questions like:
      1. What’s selling well?
      2. Where are products underperforming?
   3. Allows companies to optimise stock distribution by sending the right products to the right places.
3. Fraud Detection
   1. Data mining can map links between data points to identify normal behaviour.
   2. The absence of expected links may indicate suspicious or fraudulent activity.
   3. Helps organisations flag anomalies for further investigation.
4. Human Resources (HR)
   1. HR departments use data mining to analyse employee records, such as:
      1. Training history
      2. Performance data
      3. Exit reasons
   2. Helps design better onboarding packages and improve staff retention.
5. Customer Service
   1. Used to identify frequent problems and their common solutions.
   2. Improves customer experience by populating:
      1. Help centre FAQs
      2. Chatbots with relevant answers
   3. Leads to faster issue resolution and improved company reputation.

The Synergy of OLAP and Data Mining

1. Complementary Roles: OLAP provides structured analysis, while data mining uncovers hidden patterns.
2. Enhanced Decision-Making: Combining both tools allows businesses to gain deeper insights and make more informed decisions.
3. Efficiency: OLAP's pre-processed data enables faster querying, while data mining's raw data analysis uncovers new trends.

Ethical Considerations

1. Privacy: Ensure that data mining practices comply with privacy regulations and ethical standards.
2. Bias: Be aware of potential biases in data and algorithms that can lead to unfair or inaccurate conclusions.
3. Transparency: Clearly communicate how data is used and the insights derived from it.