{"id":5560,"date":"2022-05-07T15:03:00","date_gmt":"2022-05-07T08:03:00","guid":{"rendered":"https:\/\/bestarion.com\/us\/?p=5560"},"modified":"2025-07-24T18:40:14","modified_gmt":"2025-07-24T11:40:14","slug":"big-data-analytics-definition","status":"publish","type":"post","link":"https:\/\/bestarion.com\/us\/big-data-analytics-definition\/","title":{"rendered":"Big Data Analytics \u2013 Definition"},"content":{"rendered":"\r\n

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<\/span>What is big data analytics?<\/span><\/h2>\r\n\r\n\r\n\r\n

Big data analytics<\/strong> is the process of looking at large amounts of data to find helpful information, such as hidden patterns, correlations, market trends, and customer preferences. This information can help businesses make better decisions.<\/p>\r\n\r\n\r\n\r\n

Data analytics<\/a> technologies and techniques allow organizations to look at large data sets and learn new things about them. Business intelligence (BI) answers basic questions about how a business works and how well it is doing.<\/p>\r\n\r\n\r\n\r\n

Big data analytics is a type of advanced analytics. These complex applications use analytics systems to run things like predictive models, statistical algorithms, and “what-if” analyses.<\/p>\r\n

<\/span>Different Types of Big Data Analytics<\/span><\/h2>\r\n

There are four types of Big Data analytics:<\/p>\r\n

1. Descriptive Analytics<\/strong>
This summarizes past data into a form that people can easily read. This helps in creating reports, like a company\u2019s revenue, profit, sales, and so on. Also, it helps in the tabulation of social media metrics.<\/p>\r\n

Use Case: The Dow Chemical Company analyzed its past data to increase facility utilization across its office and lab space. Using descriptive analytics, Dow was able to identify underutilized space. This space consolidation helped the company save nearly US $4 million annually.<\/p>\r\n

2. Diagnostic Analytics<\/strong>
This is done to understand what caused a problem in the first place. Techniques like drill-down, data mining, and data recovery are all examples. Organizations use diagnostic analytics because they provide an in-depth insight into a particular problem.<\/p>\r\n

Use Case: An e-commerce company\u2019s report shows that their sales have gone down, although customers are adding products to their carts. This can be due to various reasons like the form didn\u2019t load correctly, the shipping fee is too high, or there are not enough payment options available. This is where you can use diagnostic analytics to find the reason.<\/p>\r\n

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3. Predictive Analytics<\/strong>
This type of analytics looks into the historical and present data to make predictions of the future. Predictive analytics uses data mining, AI, and machine learning to analyze current data and make predictions about the future. It works on predicting customer trends, market trends, and so on.<\/p>\r\n

Use Case: PayPal determines what kind of precautions they have to take to protect their clients against fraudulent transactions. Using predictive analytics, the company uses all the historical payment data and user behavior data and builds an algorithm that predicts fraudulent activities.<\/p>\r\n

4. Prescriptive Analytics<\/strong>
This type of analytics prescribes the solution to a particular problem. Perspective analytics works with both descriptive and predictive analytics. Most of the time, it relies on AI and machine learning.<\/p>\r\n

Use Case: Prescriptive analytics can be used to maximize an airline\u2019s profit. This type of analytics is used to build an algorithm that will automatically adjust the flight fares based on numerous factors, including customer demand, weather, destination, holiday seasons, and oil prices.<\/p>\r\n

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<\/span>Why is big data analytics important?<\/span><\/h2>\r\n\r\n\r\n\r\n

Organizations can use software and systems to analyze big data<\/a> to make decisions based on the data, leading to better business results. Some benefits could be better marketing, new ways to make money, better customer service, and more efficient operations. With a good plan, these advantages can give you an edge over your competitors.<\/p>\r\n

1. Risk Management<\/strong>
Use Case: Banco de Oro, a Phillippine banking company, uses Big Data analytics to identify fraudulent activities and discrepancies. The organization leverages it to narrow down a list of suspects or root causes of problems.<\/p>\r\n

2. Product Development and Innovations<\/strong>
Use Case: Rolls-Royce, one of the largest manufacturers of jet engines for airlines and armed forces across the globe, uses Big Data analytics to analyze how efficient the engine designs are and if there is any need for improvements.<\/p>\r\n

3. Quicker and Better Decision Making Within Organizations<\/strong>
Use Case: Starbucks uses Big Data analytics to make strategic decisions. For example, the company leverages it to decide if a particular location would be suitable for a new outlet or not. They will analyze several different factors, such as population, demographics, accessibility of the location, and more.<\/p>\r\n

4. Improve Customer Experience<\/strong>
Use Case: Delta Air Lines uses Big Data analysis to improve customer experiences. They monitor tweets to find out their customers\u2019 experience regarding their journeys, delays, and so on. The airline identifies negative tweets and does what\u2019s necessary to remedy the situation. By publicly addressing these issues and offering solutions, it helps the airline build good customer relations.<\/p>\r\n\r\n\r\n\r\n

<\/span>How does big data analytics work?<\/span><\/h2>\r\n\r\n\r\n\r\n

Data analysts, data scientists, predictive modelers, statisticians, and other analytics professionals collect, process, clean, and analyze growing amounts of structured transaction data and different types of data that traditional BI and analytics programs don’t use.<\/p>\r\n\r\n\r\n\r\n

Here’s a quick look at the four steps of the process for analyzing big data:<\/p>\r\n\r\n\r\n\r\n

Collect data<\/strong><\/h3>\r\n\r\n\r\n\r\n

Data professionals collect data from many different sources. It is often a mix of semistructured and unstructured data. Each company will use other data streams, but here are some common ones:<\/p>\r\n\r\n\r\n\r\n