Traditional Data
WHEN
At the beginning of your analysis
WHY
Data-driven decisions require well-organized and relevant raw data stored in a digital format
WHAT
Data Collection & Preprocessing:
- Class labeling (categorical vs numerical)
- Data cleansing
- Dealing with missing values
- Balancing & shuffling datasets
- Data normalization and standardization
- Feature selection and engineering
Big Data
WHEN
After the data has been gathered & organized
WHY
Use data to create reports and dashboards to gain business insights
WHAT
Data Collection & Processing:
- Handling various data types (number, text, images, video, audio)
- Data cleansing at scale
- Dealing with missing values in large datasets
- Distributed computing (e.g., Hadoop, Spark)
- Real-time data processing
- Data lake and data warehouse management
Business Intelligence
WHEN
After data has been processed and is ready for analysis
WHY
Extract actionable insights from data to support decision-making
WHAT
Analyze the Data:
- Extract info and present it in the form of:
- Metrics & KPIs
- Interactive dashboards
- Automated reports
- Data visualization techniques
- OLAP (Online Analytical Processing)
- Ad-hoc querying and reporting
- Trend analysis and forecasting
Traditional Methods
WHEN
For historical analysis and understanding past patterns
WHY
Assess potential future scenarios using proven statistical methods
WHAT
Statistical Analysis:
- Regression Analysis
- Linear regression
- Multiple regression
- Polynomial regression
- Logistic Regression
- Time Series Analysis
- ARIMA models
- Exponential smoothing
- Factor Analysis
- Cluster Analysis
- Hypothesis Testing
Machine Learning
WHEN
For complex pattern recognition and predictive modeling
WHY
Utilize artificial intelligence to predict behavior in unprecedented ways
WHAT
Advanced Analytics:
- Supervised Learning:
- Support Vector Machines (SVMs)
- Neural Networks & Deep Learning
- Random Forests
- Gradient Boosting Machines
- Unsupervised Learning:
- K-means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- t-SNE for high-dimensional data visualization
- Reinforcement Learning
- Natural Language Processing (NLP)
- Computer Vision
- Anomaly Detection
- Ensemble Methods