With today’s technology, it’s possible to analyze your data and get answers from it almost immediately– an effort that’s slower and less efficient with more traditional business intelligence solutions. Furthermore, Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights.




Why is big data analytics important?

Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers.

New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. Davenport points out that with big data analytics, more companies are creating new products to meet customers’ needs.

Faster, better decision making. With the speed of Hadoop and in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on what they’ve learned.

Cost reduction. Big data technologies such as Hadoop and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.


What is Big data analytics

Big data analytics is the use of advanced analytic techniques against very large, diverse big data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.

Characteristics of big data include high volume, high velocity and high variety. Sources of data are becoming more complex than those for traditional data because they are being driven by artificial intelligence (AI), mobile devices, social media and the Internet of Things (IoT).

The amount of data in today’s world is staggering. But big data offers vast opportunities for businesses, whether used independently or with existing traditional data. Data scientists, analysts, researchers and business users can leverage these new data sources for advanced analytics that deliver deeper insights and to power innovative big data applications.