Big data analytics definition
Big data analytics is the use of advanced analytic techniques against very enormous , diverse data sets that include structured, semi-structured and unstructured data, from disparate sources, and in different 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 large scale.
Analysis of big data allows analysts, researchers and business users to do better and faster decisions using data that was previously inaccessible or unusable. Businesses can apply advanced analytics techniques such as text analytics, machine learning, 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 instantly understand that if they capture all the data that streams into their businesses, they can apply 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 efficiency. Whereas a few years ago a business would have gathered information , run analytics and unearthed instruction that could be used for future decisions, instantly that business can identify insights for immediate decisions. The ability 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 huge amounts of data – plus they can identify more Efficient ways of doing business.
2.Faster, better decision making. With the speed of Hadoop and in-memory analytics, combined with the efficiency to analyze new sources of data, businesses are able to analyze instruction immediately – and make 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 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.