What is big data analytics?

Big data analytics examines large and varied data sets, known as "big data." By doing this, you can uncover hidden patterns, unknown correlations, market trends, and customer preferences. These insights help you make informed business decisions.

Big data typically comes from many sources. Think of surveys, social media, websites, and transaction records. This variety means you need specialized tools and techniques to manage and analyze the data. You can't use traditional data processing methods here. The sheer volume and complexity of big data require advanced processing capabilities. This is where distributed systems and cloud-based solutions come in handy.

Common techniques in big data analytics

What techniques are used?

• Association rule learning: Finds relationships or patterns in large datasets. Often used in market basket analysis to discover items frequently bought together. Learn more about association rule learning.

• Classification tree analysis: Uses predictive modeling to partition data into subsets. Assigns class labels to each subset based on input features. More details on classification tree analysis.

• Machine learning: Algorithms learn patterns from data. Used for making predictions and decisions automatically. Discover more about machine learning.

• Clustering: Groups data points based on similarity. Commonly applied in customer segmentation and anomaly detection. Explore clustering techniques.

• Regression analysis: Models the relationship between dependent and independent variables. Useful for predicting numerical values like sales based on advertising spend. Understand more about regression analysis.

Examples of big data analytics applications

Real-world applications

• Healthcare: Analyzes patient records to predict disease trends. Personalizes treatments based on medical images and data.

• Ecommerce: Uses clickstream data for real-time recommendations. Adjusts prices dynamically based on purchase histories.

• Banking: Monitors transactions in real-time to detect unusual patterns. Reduces fraud by identifying suspicious activities immediately.