Big data analytics helps businesses and organizations make better decisions by revealing information that would have otherwise been hidden.
Meaningful insights about the trends, correlations and patterns that exist within big data can be difficult to extract without vast computing power. But the techniques and technologies used in big data analytics make it possible to learn more from large data sets. This includes data of any source, size and structure.
The predictive models and statistical algorithms of data visualization with big data are more advanced than basic business intelligence queries. Answers are nearly instant compared to traditional business intelligence methods.
Big data is only getting bigger with the growth of artificial intelligence, social media and the Internet of Things with a myriad of sensors and devices. Data is measured in the “3Vs” of variety, volume and velocity. There’s more of it than ever before — often in real time. This torrential flood of data is meaningless and unusable if it can’t be interrogated. But the big data analytics model uses machine learning to examine text, statistics and language to find previously unknowable insights. All data sources can be mined for predictions and value.
Business applications range from customer personalization to fraud detection using big data analytics. They also lead to more efficient operations. Computing power and the ability to automate are essential for big data and business analytics. The advent of cloud computing has made this possible.
