What is big data analytics?

Big data analytics definition

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

Big data is a term applied to data sets whose size or type is beyond the adaptability of traditional relational databases to capture, manage and development the data with low latency. Big data has one or more of the following characteristics: high volume, high velocity or high variety. Artificial intelligence (AI), mobile, social and the Internet of Things (IoT) are driving data complexity through new forms and sources of data. For example, 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 at a very huge scale.

Analysis of big data allows analysts, researchers and business users to make better and faster decisions using data that was previously inaccessible or unusable. Businesses can apply advanced analytics techniques such as text analytics, machine training , predictive analytics, data mining, statistics and natural language processing to gain new insights from previously untapped data sources independently or together with existing enterprise data.

History and evolution of big data analytics

The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can accept analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term “big data,” businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends.

The new benefits that big data analytics brings to the table, however, are speed and ability. Whereas a few years ago a business would have gathered information , run analytics and unearthed information that could be used for future decisions, now that business can identify insights for immediate decisions. The adaptability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before.

Why is big data analytics important?

Big data analytics helps organizations harness their data and apply it to identify new opportunities. That, in turn, leads to smarter business moves, more Effective operations, higher profits and happier customers. In his report Big Data in Big Companies, IIA Director of Research Tom Davenport interviewed more than 50 businesses to understand how they used big data. He found they got value in the following ways:

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

2.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 do decisions based on what they’ve learned.

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