IBM - Big data analytics

About big data analytics

Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes. Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale.

Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions.

Want to learn more?What is big data analytics?

Deepak Rangarao, IBM Client Technical Specialist, takes us through a demo showing Big Semantic Search Data Analytics in action in the Telco industry.

Watch the video(00:24:13)


Smarter security intelligence

Leverage big data analytics to improve enterprise security

Get the brief


TDWI best practices Report: big data analytics

Get an understanding of the many new tools and techniques that have emerged for analytics with big data in recent years

Get the report


Big data analytics

Unlike other blogs and books that cover the basics of big data analytics and technological underpinnings, this book brings a practitioners view to the subject

Get the ebook