Big Data is the biggest buzzword right now. With the amount of data generated every minute by consumers and businesses globally, Big Data analytics holds enormous significance.
What exactly is Big Data Analytics?
Big Data analytics is the process of extracting valuable insights from large amounts of data, such as hidden patterns, unknown relationships, market trends, and customer preferences. Big Data analytics offers numerous benefits, including improved decision-making and the prevention of fraudulent actions.
What is the significance of big data analytics?
Big Data analytics is powering everything we do online today—in every business.
Consider the music streaming service Spotify. Every day, the company’s almost 96 million users generate a massive amount of data. The cloud-based technology uses this information to automatically generate suggested music based on likes, shares, search history, and other factors. The methodologies, tools, and frameworks developed as a result of Big Data analytics enable this.
If you use Spotify, you’ve probably come across the top recommendation section, which is based on your preferences, past 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. This is exactly what Spotify does.
What really is Big Data?
Big Data refers to large amounts of data that cannot be stored, processed, or analyzed using typical technologies.
There are now millions of data sources that generate data at a quick pace. These data sources can be found all around the world. Social media platforms and networks are among the most important data sources.
As an example, Facebook creates more than 500 terabytes of data per day. This data contains images, videos, messages, and other items.
Data is also available in a variety of formats, including structured data, semi-structured data, and unstructured data. In a standard Excel sheet, for example, data is categorized as structured data—material 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 Analytics Benefits:
1. Risk Analysis:
Use Case: Banco de Oro, a Philippine banking firm, uses Big Data analytics to detect fraudulent activities and inconsistencies. It is used by the organization to narrow down a list of suspects or root causes of problems.
2. Product Innovation and Development:
Use Case: Rolls-Royce, one of the world’s major producers of jet engines for airlines and armed forces, uses Big Data analytics to determine how efficient engine designs are and where improvements may be made.
3. Quicker and Better Decision Making Within Organizations:
Starbucks employs Big Data analytics to make strategic decisions. For example, the corporation uses it to determine whether or not a specific area is acceptable for a new outlet. They will look at a variety of aspects, including population, demography, geographical accessibility, and more.
4. Improve Service Quality:
Case Study: Delta Air Lines analyses Big Data to improve customer experiences. They monitor tweets to learn about their clients’ travel experiences, such as delays. The airline recognizes bad tweets and takes the required steps to correct the situation. It helps the airline create positive customer relations by publicly addressing these issues and proposing remedies.
What Is Big Data Analytics and Why Does It Matter
Big Data is the biggest buzzword right now. With the amount of data generated every minute by consumers and businesses globally, Big Data analytics holds enormous significance.
What exactly is Big Data Analytics?
Big Data analytics is the process of extracting valuable insights from large amounts of data, such as hidden patterns, unknown relationships, market trends, and customer preferences. Big Data analytics offers numerous benefits, including improved decision-making and the prevention of fraudulent actions.
What is the significance of big data analytics?
Big Data analytics is powering everything we do online today—in every business.
Consider the music streaming service Spotify. Every day, the company’s almost 96 million users generate a massive amount of data. The cloud-based technology uses this information to automatically generate suggested music based on likes, shares, search history, and other factors. The methodologies, tools, and frameworks developed as a result of Big Data analytics enable this.
If you use Spotify, you’ve probably come across the top recommendation section, which is based on your preferences, past 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. This is exactly what Spotify does.
What really is Big Data?
Big Data refers to large amounts of data that cannot be stored, processed, or analyzed using typical technologies.
There are now millions of data sources that generate data at a quick pace. These data sources can be found all around the world. Social media platforms and networks are among the most important data sources.
As an example, Facebook creates more than 500 terabytes of data per day. This data contains images, videos, messages, and other items.
Data is also available in a variety of formats, including structured data, semi-structured data, and unstructured data. In a standard Excel sheet, for example, data is categorized as structured data—material 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 Analytics Benefits:
1. Risk Analysis:
Use Case: Banco de Oro, a Philippine banking firm, uses Big Data analytics to detect fraudulent activities and inconsistencies. It is used by the organization to narrow down a list of suspects or root causes of problems.
2. Product Innovation and Development:
Use Case: Rolls-Royce, one of the world’s major producers of jet engines for airlines and armed forces, uses Big Data analytics to determine how efficient engine designs are and where improvements may be made.
3. Quicker and Better Decision Making Within Organizations:
Starbucks employs Big Data analytics to make strategic decisions. For example, the corporation uses it to determine whether or not a specific area is acceptable for a new outlet. They will look at a variety of aspects, including population, demography, geographical accessibility, and more.
4. Improve Service Quality:
Case Study: Delta Air Lines analyses Big Data to improve customer experiences. They monitor tweets to learn about their clients’ travel experiences, such as delays. The airline recognizes bad tweets and takes the required steps to correct the situation. It helps the airline create positive customer relations by publicly addressing these issues and proposing remedies.
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