Upcoming Trends of Big Data Analytics for Effective Data Management in Future

Upcoming Trends of Big Data Analytics for Effective Data Management in Future

Check out the upcoming trends of big data analytics to drive meaningful insights

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.

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. 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 is generated in real-time and at a very large 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 use 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.

Latest trends of big data analytics

Natural language processing (NLP) of big data analytics allows users to perform queries in natural human language, with either written or voice input

Using artificial intelligence and machine learning, augmented analytics gives users a programmed way to find the most important insights

Blockchain can enhance predictive big data analytics because it verifies data validity, preventing false information from being included in analysis

Edge computing brings computation to a network's edge and reduces the amount of long-distance connection that has to happen between a customer and a server, which is making it the latest trend in big data analytics.

Hybrid cloud provides excellent flexibility and more data deployment options by moving the processes between private and public clouds

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