The more data you have, the more chance you have of getting useful insights from it. However, the size of big data usually makes it impossible to use manual or even conventional computing methods.
Instead, big data analytics is based on:
- Data mining to sift through data to find patterns and relationships
- Statistical algorithms to build models and predict outcomes
- Machine learning to handle changing and new data, to adapt and enrich models
- Text analytics and natural language processing to analyze free form text and speech
Here are some examples of how big data analytics can be used to help organizations:
- Customer acquisition and retention.
- Targeted ads.
- Product development.
- Price optimization.
- Supply chain and channel analytics.
- Risk management.
- Improved decision-making.