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How Important Is Big Data Analytics?
How Important Is Big Data Analytics?

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What is Big Data Analytics?

Big Data analytics is a technique to uncover important insights such as unobserved correlations, hidden patterns, market trends, and consumer preferences. Big Data analytics offers a number of benefits including the ability to use it to improve decision-making and stop fraud.

What is Big Data?

Big Data is an enormous collection of data sets that can't be handled, stored, or examined with conventional technologies.

 

Millions of data sources currently provide data at an incredibly fast rate. These data sources are accessible on a global scale. Social media networks and platforms are some of the biggest providers of data. As an example, consider Facebook, which daily produces more than 500 gigabytes of data. This information consists of images, movies, texts, and more.

 

Additionally, data is available in a variety of formats, including organized, semi-structured, and unstructured data. For instance, data in a typical Excel sheet is categorized as structured data since it has a specific format. On the other hand, emails are classified as semi-structured data, while your photos and videos are classified as unstructured data. Big Data is the totality of all this data.

Big data analytics: Why are they crucial?

Big Data analytics is the driving force behind everything we do online today across all sectors.

 

As an illustration, consider the music streaming service Spotify. The company's around 96 million users produce a vast amount of data each day. The cloud-based platform uses this data to automatically produce new music using a clever recommendation engine that considers likes, shares, search history, and other factors. The methods, instruments, and frameworks developed as a result of big data analytics are what make this possible.

History of Big Data Analytics:

Big Data analytics have a long history dating back to the early days of computing when businesses started storing and analyzing massive volumes of data on computers. Big Data analytics, however, did not truly take off until the late 1990s and early 2000s, when businesses increasingly resorted to computers to assist them in making sense of the vast amounts of data that were being generated by their operations.

 

Big Data analytics is now a crucial tool for businesses of all kinds operating in a variety of sectors. Organizations are now able to learn things about their customers, their markets, and the environment around them that were previously impossible by utilizing the power of big data.

 

Big Data Analytics's advantages and benefits:

Step 1: Risk Management:

Use Case: To spot irregularities and fraudulent activity, the Philippine banking firm Banco de Oro employs big data analytics. The organization uses it to reduce a list of potential suspects or the main reasons for issues.

Step 2: Product Innovation and Development:

Use Case: Rolls-Royce, one of the biggest jet engine producers for airlines and the armed services worldwide, employs big data analytics to assess how effective the engine designs are and whether any changes are necessary.

 

Step 3: Improved Decision-Making Speed Within Organizations:

 

Starbucks employs big data analytics to make important choices. For instance, the business uses it to determine whether a specific location would be appropriate for a new outlet or not. They will examine a range of variables, including population, demography, geographical accessibility, and more.

 

Step 4: Enhance the client experience:

Use Case: To enhance client experiences, Delta Air Lines uses big data analysis. They read tweets to learn about their clients' experiences, travels, delays, and other issues. The airline recognizes unfavorable tweets and takes action as needed to make things right. The airline improves customer relations by openly addressing these problems and providing remedies.

 

Types of big data analytics:

 

The four types of big data analytics are:

 

  1. Descriptive analytics: This presents historical data in an accessible format. This facilitates the creation of reports on a company's earnings, profits, sales, and other metrics. Additionally, it assists in compiling social media stats.

 

  1. Diagnostic analytics: This is done to comprehend the root cause of a problem. Drill-down, data mining, and data recovery are a few examples of techniques. Organizations utilize diagnostic analytics because they thoroughly understand a specific issue.

 

  1. Predictive analytics: This sort of analytics examines historical and current data to predict the future. Predictive analytics uses data mining, AI, and machine learning to examine current data and forecast the future. It functions by foreseeing consumer trends, market trends, and other trends.
  2. Prescriptive analytics: This kind of analytics suggests a solution to a specific issue. Both descriptive and predictive analytics can be used with perspective analytics. It frequently makes use of AI and machine learning.

 

Conclusion:

Big data analytics is the sometimes challenging process of examining enormous amounts of data to uncover information that may help firms make wise business decisions, such as hidden patterns, correlations, market trends, and consumer preferences.


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