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The major fields where big data is being used are as follows. 1. Big data analytics technology is the one that helps retailers to fulfil the demands, equipped with infinite quantities of data from client loyalty programs. And it majorly includes applying various data mining algorithms on a certain dataset. Big Data could be 1) Structured, 2) Unstructured, 3) Semi-structured Big Data Analytics examines large and different types of data in order to uncover the hidden patterns, insights, and correlations. 0 votes . A picture, a voice recording, a tweet they all can be different but express ideas and thoughts based on human understanding. Big data analytics is used to discover hidden patterns, market trends and consumer preferences, for the benefit of organizational decision making. To identify if there is a prevailing type of data analytics, lets turn to different surveys on the topic for the period 2016-2019. Basically, Big Data Analytics is helping large companies facilitate their growth and development. It can be unstructured and it can include so many different types of data from XML to video to SMS. Big Data definition : Big Data is defined as data that is huge in size. Its components and connectors include Spark streaming, Machine learning, and IoT. Big data analytics Technologies and Tools. 1 and 2. ElasticSearch. Big data has found many applications in various fields today. High Volume, velocity and variety are the key features of big data. Big data platform: It comes with a user-based subscription license. E. 1, 2, 3 and 4. With unstructured data, on the other hand, there are no rules. Big data analysis helps in understanding and targeting customers. Data points with different densities; Data points with round shapes; Data points with non-convex shapes; Options: A. For the 2016 Global Data and Analytics Survey: Big Decisions, more than 2,000 executives were asked to choose a category that described their companys decision-making process best. Variety describes one of the biggest challenges of big data. Today, though, the growing volume of data and the advanced analytics technologies available mean you can get much deeper data insights more quickly. These are the classic predictive analytics problems where you want to unearth trends or push the boundaries of scientific knowledge by mining mind-boggling amount of data. Data types involved in Big Data analytics are many: structured, unstructured, geographic, real-time media, natural language, time series, event, network and linked. One of the goals of big data is to use technology to take this unstructured data and make sense of it. Bigdata is a term used to describe a collection of data that is huge in size and yet growing exponentially with time. Data analytics is just a part of this big data analytics. A brief description of each type is given below. To better comprehend big data, the fields of data science and analytics have gone from largely being relegated to academia, to instead becoming integral elements of Business Intelligence and big data analytics tools. Data analytics is also used to detect and prevent fraud to improve efficiency and reduce risk for financial institutions. It provides Web, email, and phone support. Big data analytics tools can bring this data together with the historical information to determine what the probability of an event were to happen based on past experiences. Velocity is the speed in which data is process and becomes accessible. Their main benefits are faster query performance, better maintenance, and scalability. Informational features: In contrast to traditional data that may change at any moment (e.g., bank accounts, quantity of goods in a warehouse), big data represents a log of records where each describes some event (e.g., a purchase in a store, a web page view, a sensor value at a given moment, a comment on a social network). Real-time big data platform: It comes under a user-based subscription license. Big data analytics is not a single process instead is a collection of many processes that are related to business and they may be related to data scientists, business management, and production teams too. It is necessary here to distinguish between human-generated data and device-generated data since human data is often less trustworthy, noisy and unclean. Benefits or advantages of Big Data. The insights that big data and modern technologies make possible are more accurate and more detailed. This is also an open-source, distributed NoSQL database system. The data could be from a client dataset, a third party, or some kind of static/dimensional data (such as geo coordinates, postal code, and so on).While designing the solution, the input data can be segmented into business-process-related data, business-solution-related data, or data for technical process building. Data quality: the quality of data needs to be good and arranged to proceed with big data analytics. Variety. Big data and analytics software allows them to look through incredible amounts of information and feel confident when figuring out how to deal with things in their respective industries. And in a market with a barrage of global competition, manufacturers like USG know the importance of producing high-quality products at an affordable price. Big Data Analytics takes this a step further, as the technology can access a variety of both structured and unstructured datasets (such as user behaviour or images). 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