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Big Data Analytics

Big Data Analytics

Big Data Analytics

Big Data is today’s biggest term, and with the quantity of data created every minute by consumers and organizations around the world, Big Data analytics has a lot of promise.

We’ll go through the following subjects in depth to give you a better grasp of what Big Data analytics is and how it works.

 

  1. What is Big Data Analytics?

Big Data analytics is a technique for uncovering hidden patterns, undiscovered relationships, market trends, and client preferences from large amounts of data. Big Data analytics has a number of advantages, including the ability to improve decision-making and avoid fraudulent actions.

  1. Why is big data analytics important?

Big Data analytics is powering everything we do online today, in every business.

Take, for example, the music streaming service Spotify. Every day, the company’s almost 96 million users create a massive quantity of data. The cloud-based platform uses this data to produce suggested music based on likes, shares, search history, and more, using a clever recommendation engine. The methodologies, tools, and frameworks developed as a result of Big Data analytics are what make this possible.

If you use Spotify, you’ve probably come across the top recommendation area, which is based on your preferences, previous listening history, and other factors. It is possible to use a recommendation engine that uses data filtering technologies to collect data and then filter it using algorithms. Spotify is a company that specializes in this type of service.

 

  1. What is Big Data?

Big Data refers to large amounts of data that can’t be stored, processed, or evaluated with conventional technologies.

There are millions of data sources nowadays that provide data at a quick rate. These data sources may be found all around the world. Social media platforms and networks are some of the most important data sources. Consider Facebook, which creates more than 500 terabytes of data every day. Pictures, videos, texts, and other types of information are included in this data.

Data comes in a variety of forms, including organized, semi-structured, and unstructured. For example, data in a typical Excel sheet is categorized as structured data—that is, data that has a specific format. Emails, on the other hand, are classified as semi-structured data, while photos and videos are classified as unstructured data. Big Data is the sum total of all of this information.

Big Data, on the other hand, is useless in its raw form. So, let’s have a look at Big Data Analytics.

Let’s take a look at four Big Data analytics advantages.

 

  1. Big Data Analytics’ Benefits and Advantages
  2. Risk Management 

Use Case: Banco de Oro, a Philippine financial firm, using Big Data analytics to detect fraud and anomalies. It is used by the organization to narrow down a list of suspects or underlying causes of issues.

  1. Innovations and Product Development

Use Case: Rolls-Royce, one of the world’s major producers of jet engines for airlines and military forces, using Big Data analytics to assess the efficiency of engine designs and determine whether any upgrades are required.

  1. Organizational Decision-Making that is Faster and Better

Starbucks, for example, use Big Data analytics to make strategic decisions. The firm, for example, utilizes it to decide if a given location is suitable for a new store. They’ll consider factors such as population, demographics, geographical accessibility, and more.

  1. Enhance the Customer Experience

Use Case: To improve customer experiences, Delta Air Lines employs Big Data analysis. They keep an eye on Twitter to find out about their clients’ journeys, delays, and other issues. The airline keeps track of negative tweets and takes proper steps to address the issue. By openly addressing these problems and suggesting solutions, it aids the airline in establishing excellent customer relations.

 

  1. The Big Data Analytics Lifecycle Phases

Let’s have a look at how Big Data analytics works:

  • Level 1 – The Big Data analytics lifecycle begins with the examination of a business case, which specifies the purpose and aim for the investigation.
  • Level 2 – Identification of data sources – This step identifies a wide range of data sources.
  • Level 3 – Data filtering – All of the previously detected data is filtered here to remove any corrupt data.
  • Level 4 – Data extraction – Incompatible data is extracted and then changed into a format that is compatible with the tool.
  • Level 5 – Data aggregation – At this step, data from several datasets with the same fields is combined.
  • Level 6 – Data analysis – Analytical and statistical methods are used to examine data in order to uncover meaningful information.
  • Level 7 – Data visualization – Big Data researchers may create graphic visualizations of their analysis using technologies like Tableau, Power BI, and QlikView.
  • Level 8 – Final analysis result – At this point in the Big Data analytics lifecycle, the final analysis results are made accessible to business stakeholders who will act on them.

 

  1. Big Data Analytics: Different Types

There four different kinds of Big Data analytics:

 

  1. Descriptive Analytics

This summarizes previous data in an easy-to-understand format. This helps with reporting like a company’s income, profit, and sales, among other things. It also helps with social media analytics tabulation.

Use Case: The Pharmaceutical Company examined historical data in order to improve facility use in its offices and laboratories. Dow was able to discover underused space using descriptive analytics. The corporation was able to save roughly $4 million per year as a result of the space consolidation.

 

  1. Diagnostic Analytics

This is done in order to figure out what created the issue in the first place. Drill-down, data mining, and data recovery are all instances of techniques. Diagnostic analytics are used by businesses because they give a detailed understanding of an issue.

Use Case: A report from an e-commerce business reveals that sales have decreased, despite customers adding items to their shopping carts. This might be due to a variety of factors, including a form that did not load successfully, a shipping price that is too expensive, or a lack of payment choices. This is where diagnostic analytics can help you figure out what’s going on.

 

  1. Predictive Analytics

This sort of analytics examines past and current data in order to create predictions about the future. Predictive analytics analyses current data and makes predictions about the future using data mining, artificial intelligence, and machine learning. It predicts customer and market trends, among other things.

Use Case: PayPal evaluates what safeguards they need to put in place to protect their customers from fraudulent transactions. The business employs predictive analytics to construct an algorithm that detects fraudulent activity based on all past payment data and user behavior data.

 

  1. Prescriptive Analytics

This form of analytics recommends a remedy to a specific issue. Both descriptive and predictive analytics are used in perspective analytics. The majority of the time, AI and machine learning are used.

Prescriptive analytics can be used to increase an airline’s profit margins. This sort of data is used to create an algorithm that adjusts flight tickets automatically depending on a variety of criteria such as client demand, weather, destination, holiday seasons, and oil prices.

 

  1. Big Data Analytics Tools

Here are a few of the most important big data analytics tools:

  • Hadoop– Data storage and analysis are made easier using this software.
  • MongoDB – used on regularly changing datasets
  • Talend – utilized for data management and integration
  • Cassandra – a database that can manage large amounts of data in a distributed fashion
  • Spark – used to handle and analyze enormous volumes of data in real time
  • STORM – a real-time computational system that is open-source
  • Kafka – a fault-tolerant storage infrastructure for distributed streaming

 

  1. Applications of Big Data in Industry

Industries that are actively using Big Data:

  • Ecommerce – E-commerce leverages Big Data analytics in a variety of ways, including predicting customer patterns and optimizing prices.
  • Marketing – Big Data analytics aids in the development of high-return-on-investment marketing initiatives that result in increased revenue.
  • Education – Based on market demands, it’s being used to create new and improve existing courses.
  • Healthcare – Big Data analytics is used to estimate how probable a patient is to develop health difficulties based on their medical history.
  • Media and entertainment – Used to determine the popularity of episodes, movies, music, and other items in order to provide consumers with a customized suggestion list.
  • Banking – Customer income and spending patterns can help anticipate whether or not they would choose various financial products such as loans and credit cards.
  • Telecommunications – Used to improve customer experience by forecasting network capacity.
  • Government – Big Data analytics aids governments in a variety of ways, including law enforcement.

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