Big Data Analytics is the process of examining large and varied datasets (big data) to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make data-driven decisions.
Types of Data Analytics:
There are four major types of data analytics used by organizations to derive insights:
1️⃣ Descriptive Analytics:
- Focuses on “What has happened?”
- Describes and summarizes the main features of collected data.
- It uses statistical tools to understand trends and patterns from historical data.
- Example: Sales reports, website traffic reports.
2️⃣ Diagnostic Analytics:
- Focuses on “Why did it happen?”
- Also called causal analysis — used to understand the cause and effect relationships.
- Helps identify the reasons for past outcomes.
- Example: Why a certain product saw a drop in sales.
3️⃣ Predictive Analytics:
- Focuses on “What is likely to happen?”
- Uses machine learning algorithms and historical data to forecast future outcomes.
- Example: Predicting customer churn, sales forecasting.
4️⃣ Prescriptive Analytics:
- Focuses on “What should be done?”
- Goes beyond prediction to recommend actions or strategies.
- Helps in decision-making and planning to reduce risks.
- Example: Suggesting inventory strategies based on demand forecasts.
Big Data Analytics Framework:
Big Data Analytics follows a 4-layer architecture, which enables structured processing of data.
1. Data Connection Layer:
- Handles data ingestion and connection from different sources (databases, APIs, IoT).
- Performs ETL operations — Extract, Transform, Load.
- Converts raw data into structured formats.
2. Data Management Layer:
- Manages and preprocesses data for analysis.
- Supports parallel query execution and efficient data access.
- Techniques:
- Data-in-place (no movement),
- Data warehouses,
- On-demand data fetching.
3. Data Analytics Layer:
- Performs core analytics like:
- Statistical tests,
- Machine learning algorithms,
- Model construction and validation.
- This is the intelligence layer where insights are generated.
4. Presentation Layer:
- Provides visualization and reporting tools like:
- Dashboards,
- Graphs, charts,
- Web/mobile interfaces.
- Helps present results clearly to decision-makers.
Big Data Processing Cycle:
The full cycle of Big Data processing includes:
- Data Collection
- Data Preprocessing
- Application of ML Algorithms
- Interpretation & Visualization of Results
This cycle is iterative and continues to evolve as new data arrives.