{"id":25553614,"date":"2023-02-14T15:01:37","date_gmt":"2023-02-14T09:31:37","guid":{"rendered":"https:\/\/entri.app\/blog\/?p=25553614"},"modified":"2023-05-11T16:28:40","modified_gmt":"2023-05-11T10:58:40","slug":"big-data-analytics","status":"publish","type":"post","link":"https:\/\/entri.app\/blog\/big-data-analytics\/","title":{"rendered":"Big Data Analytics &#8211; Importance, Applications and Examples"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_79_2 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69e3ac4e1ce95\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69e3ac4e1ce95\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/entri.app\/blog\/big-data-analytics\/#What_is_Big_Data_Analytics\" >What is Big Data Analytics?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/entri.app\/blog\/big-data-analytics\/#How_Big_Data_Analytics_Works\" >How Big Data Analytics Works<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/entri.app\/blog\/big-data-analytics\/#Benefits_of_using_Big_Data_Analytics\" >Benefits of using Big Data Analytics<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/entri.app\/blog\/big-data-analytics\/#Big_Data_Analytics_Tools\" >Big Data Analytics Tools<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/entri.app\/blog\/big-data-analytics\/#The_Challenges_of_Big_Data\" >The Challenges of Big Data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/entri.app\/blog\/big-data-analytics\/#Applications_of_Big_Data_Analytics\" >Applications of Big Data Analytics<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/entri.app\/blog\/big-data-analytics\/#Big_Data_Analytics_Examples\" >Big Data Analytics Examples<\/a><\/li><\/ul><\/nav><\/div>\n<p>Big data is currently one of the most crucial subjects of conversation among company executives and industry titans. We live in a digitally-driven era, therefore every business is pursuing big data to extract important insights from vast quantities of raw data. As a result, we will discover in this article what big data analytics is, why it is so crucial, and what its many aspects are.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"What_is_Big_Data_Analytics\"><\/span><strong>What is Big Data Analytics?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Big data analytics is the process of spotting patterns, trends, and correlations in vast quantities of unprocessed data in order to support data-driven decision-making. These procedures employ well-known statistical analysis methods, such as clustering and regression, to larger datasets with the aid of more recent instruments. Since the early 2000s, when advancements in software and technology allowed businesses to manage substantial volumes of unstructured data, the term &#8220;big data&#8221; has been popular.<\/p>\n<p><strong><div class=\"lead-gen-block\"><a href=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2023\/05\/1_merged-3_compressed.pdf\" data-url=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2023\/05\/1_merged-3_compressed.pdf\" class=\"lead-pdf-download\" data-id=\"25556853\"><\/strong><\/p>\n<p style=\"text-align: center;\"><button class=\"btn btn-default\">Free SQL Tutorial for Beginners &#8211; Download PDF<\/button><\/p>\n<p><strong><\/a><\/div><\/strong><\/p>\n<h4><strong>Here is The List Of The Top 10 Industries Using Big Data Applications:<\/strong><\/h4>\n<ol>\n<li aria-level=\"1\">Banking and Securities<\/li>\n<li aria-level=\"1\">Communications, Media and Entertainment<\/li>\n<li aria-level=\"1\">Healthcare Providers<\/li>\n<li aria-level=\"1\">Education<\/li>\n<li aria-level=\"1\">Manufacturing and Natural Resources<\/li>\n<li aria-level=\"1\">Government<\/li>\n<li aria-level=\"1\">Insurance<\/li>\n<li aria-level=\"1\">Retail and Wholesale trade<\/li>\n<li aria-level=\"1\">Transportation<\/li>\n<li aria-level=\"1\">Energy and Utilities<\/li>\n<\/ol>\n<p style=\"text-align: center;\"><strong><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\" target=\"_blank\" rel=\"noopener\">Enroll for Data Science and Machine Learning Course Now!<\/a><\/strong><\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_Big_Data_Analytics_Works\"><\/span><strong>How Big Data Analytics Works<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Big data analytics refers to collecting, processing, cleaning, and analyzing large datasets to help organizations operationalize their big data.<\/p>\n<h3><strong>1. Collect Data<\/strong><\/h3>\n<p>Every business has a distinct approach to data collecting. Organizations may now collect structured and unstructured data from a number of sources, including cloud storage, mobile apps, in-store IoT sensors, and more, thanks to modern technology. Data warehouses will be used to store some of the data so that business intelligence tools and solutions may quickly access it. A data lake can be used to hold raw or unstructured data that is too complicated or diverse to be kept in a warehouse.<\/p>\n<h3><strong>2. Process Data<\/strong><\/h3>\n<p>For analytical queries to yield correct answers, data must be appropriately organized once it has been gathered and stored, especially if the data is big and unstructured. Data processing is becoming more difficult for corporations as the amount of data available increases dramatically. Batch processing, which examines big data chunks over time, is one processing choice.<\/p>\n<p>When there is a longer gap between data collection and analysis, batch processing is advantageous. Small batches of data are examined all at once using stream processing, which reduces the time between data collection and analysis to enable speedier decision-making. Stream processing is more costly and complicated.<\/p>\n<h3><strong>3. Clean Data<\/strong><\/h3>\n<p>Data big or small requires scrubbing to improve data quality and get stronger results; all data must be formatted correctly, and any duplicative or irrelevant\u00a0data must be eliminated or accounted for. Dirty data can obscure and mislead, creating flawed insights.<\/p>\n<h3><strong>4.<\/strong> <strong>Analyze Data<\/strong><\/h3>\n<p>Getting big data into a usable state takes time. Once it\u2019s ready, advanced analytics processes can turn big data into big insights. Some of these big data analysis methods include:<\/p>\n<ul>\n<li><strong>Data mining<\/strong>\u00a0sorts through large datasets to identify patterns and relationships by identifying anomalies and creating data clusters.<\/li>\n<li><strong>Predictive analytics<\/strong>\u00a0uses an organization\u2019s historical data to make predictions about the future, identifying upcoming risks and opportunities.<\/li>\n<li><strong>Deep learning<\/strong>\u00a0imitates human learning patterns by using artificial intelligence and machine learning to layer algorithms and find patterns in the most complex and abstract data.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Benefits_of_using_Big_Data_Analytics\"><\/span><strong>Benefits of using Big Data Analytics<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>The following are some of the benefits of using big data analytics:<\/strong><\/p>\n<ul>\n<li>Analysis of large volumes of data from disparate sources in a variety of forms and kinds in a timely manner<\/li>\n<li>Quickly making well-informed judgments for successful strategizing to enhance the supply chain, logistics, and other tactical decision-making sectors<\/li>\n<li>Savings due to the increased efficiency and optimization of business processes<\/li>\n<li>More informed risk management techniques based on large data sample sizes<\/li>\n<li>Greater knowledge of consumer behavior, demands, and sentiment can result in better product development data and strategic management processes<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><strong><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\" target=\"_blank\" rel=\"noopener\">Enroll for Data Science and Machine Learning Course Now!<\/a><\/strong><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Big_Data_Analytics_Tools\"><\/span><strong>Big Data Analytics Tools<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>In this section, we will go through the various aspects of the big data analytics domain:<\/strong><\/p>\n<ul>\n<li><strong>Apache Spark<\/strong><strong>:<\/strong>\u00a0Spark is a framework for real-time data analytics, which is a part of the Hadoop ecosystem.<\/li>\n<li><strong>Python:<\/strong>\u00a0Python is one of the most versatile programming languages that is rapidly being deployed for various applications including machine learning.<\/li>\n<li><strong>SAS:<\/strong>\u00a0SAS is an advanced analytical tool that is used for working with large volumes of data and deriving valuable insights from it.<\/li>\n<li><strong>Hadoop:<\/strong>\u00a0Hadoop is the most popular big data framework that is deployed by a wide range of organizations from around the world for making sense of big data.<\/li>\n<li><strong>SQL:<\/strong>\u00a0SQL is used for working with relational database management systems.<\/li>\n<li><strong>Tableau:<\/strong>\u00a0Tableau is the most popular business intelligence tool that is deployed for the purpose of data visualization and business analytics.<\/li>\n<li><strong>Splunk:<\/strong>\u00a0Splunk\u00a0is the tool of choice for parsing machine-generated data and deriving valuable business insights out of it.<\/li>\n<li><strong>R:<\/strong>\u00a0R is the no. 1 programming language that is being used by data scientists for statistical computing and graphical applications alike.<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><strong><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\" target=\"_blank\" rel=\"noopener\">Enroll for Data Science and Machine Learning Course Now!<\/a><\/strong><\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_Challenges_of_Big_Data\"><\/span><strong>The Challenges of Big Data<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Big data offers enormous advantages, but it also presents enormous difficulties, including new privacy and security worries, user accessibility for business users, and selecting the best solutions for your company&#8217;s requirements. Organizations must deal with the following issues in order to benefit from incoming data:<\/p>\n<ul>\n<li><strong>Making big data accessible.<\/strong>\u00a0Collecting and processing data becomes more difficult as the amount of data grows. Organizations must make data easy and convenient for data owners of all skill levels to use.<\/li>\n<li><strong>Maintaining quality data.<\/strong>\u00a0With so much data to maintain, organizations are spending more time than ever before scrubbing for duplicates, errors, absences, conflicts, and inconsistencies.<\/li>\n<li><strong>Keeping data secure.<\/strong>\u00a0As the amount of data grows, so do\u00a0privacy and security concerns. Organizations will need to strive for compliance and put tight data processes in place before they take advantage of big data.<\/li>\n<li><strong>Finding the right tools and platforms.<\/strong>\u00a0New technologies for processing and analyzing big data are developed all the time. Organizations must find the right technology to work within their established ecosystems and address their particular needs. Often, the right solution is also a flexible solution that can accommodate future infrastructure changes.<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><strong><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\" target=\"_blank\" rel=\"noopener\">Enroll for Data Science and Machine Learning Course Now!<\/a><\/strong><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Applications_of_Big_Data_Analytics\"><\/span><strong>Applications of Big Data Analytics<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>Customer Acquisition and Retention:<\/strong> Consumer data is quite useful for marketing trends that increase customer satisfaction through data-driven initiatives. Personalization technologies, like those employed by companies like Netflix, Amazon, and Spotify, for example, improve customer loyalty and the shopping experience for consumers.<\/li>\n<li><strong>Targeted Ads:<\/strong> To construct targeted ad campaigns for customers on a bigger scale and at the individual level, personalized data regarding interaction patterns, order histories, and product page viewing history may be quite helpful.<\/li>\n<li><strong>Product Development:<\/strong> It can produce insights on product viability, performance metrics, development choices, etc., and direct changes that benefit the customers.<\/li>\n<li><strong>Price Optimization:<\/strong> With the use of various data sources, merchants may build pricing models and utilize them to increase profits.<\/li>\n<li><strong>Supply Chain and Channel Analytics:\u00a0<\/strong>Predictive analytical models help with B2B supplier networks, preemptive replenishment, route optimizations, inventory management, and notification of potential delays in deliveries.<\/li>\n<li><strong>Risk Management:\u00a0<\/strong>It helps in the identification of new risks with the help of data patterns for the purpose of developing effective risk management strategies.<\/li>\n<li><strong>Improved Decision-making:<\/strong>\u00a0The insights that are extracted from the data can help enterprises make sound and quick decisions.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Big_Data_Analytics_Examples\"><\/span><strong>Big Data Analytics Examples<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>There are several sorts of Big Data analytics examples. On the basis of their enormous current and past data sets, some organizations utilize these Big Data Analytics Examples to produce various reports and dashboards. Big Data Analytics Examples is used to generate various reports among those some examples are given below:<\/p>\n<ol>\n<li>Live Tracking Report which is generally used by Transport Sectors such as Meru, Ola, Uber, and Mega to track the vehicles, customer\u2019s requests, payment management, emergency alert and to find the daily needs and revenues and so on.<\/li>\n<li>Sales Report and Future target and goal analysis which is mostly used by all sectors to analyze their sales, revenues, and needs of customers and also used to determine the future target and so on.<\/li>\n<li>Many reports based on live data mostly used to manage live data in many entertainment sites, share market, real-time Sensex data etc.<\/li>\n<li>Generate different types of alarms based on different activities like alarm generated by data Centre, various notifications Big Data Analytics Examples has been used here.<\/li>\n<li>Google Analytics report where we can get how many user\u2019s visit counts, from which location the user is from, from which device the site is accessing and so on.<\/li>\n<li>Many Health care Organization nowadays rapidly introduced Big Data predictive analytics to improve our daily life. It has been used to update many protocols of Healthcare Sectors and also used to improve the outcomes against entire populations.<\/li>\n<\/ol>\n<h4><\/h4>\n<div class=\"table-responsive wprt_style_display\">\n<div class=\"table-responsive wprt_style_display\">\n<div class=\"table-responsive wprt_style_display\">\n<div class=\"table-responsive wprt_style_display\">\n<table class=\"table\" dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\">\n<colgroup>\n<col width=\"437\" \/>\n<col width=\"387\" \/><\/colgroup>\n<tbody>\n<tr>\n<td style=\"text-align: left;\"><strong>Related Articles<\/strong><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Understanding Machine Learning Basics \u2013 A Simple Guide&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/understanding-machine-learning-basics-a-simple-guide\/\"><span style=\"color: #0000ff;\"><strong><a class=\"in-cell-link\" style=\"color: #0000ff;\" 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We live in a digitally-driven era, therefore every business is pursuing big data to extract important insights from vast quantities of raw data. As a result, we will discover in this article what big data analytics is, [&hellip;]<\/p>\n","protected":false},"author":113,"featured_media":25553620,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[802,1864,1841],"tags":[],"class_list":["post-25553614","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles","category-data-science-ml","category-entri-skilling"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Big Data Analytics - Importance, Applications, Examples<\/title>\n<meta name=\"description\" content=\"Big data analytics helps organizations harness their data and use it to identify opportunities. 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