What is Big Data?

Since the past, people have recorded events and information by writing. In this way, they transferred what they knew and the cultural developments of the period to the next generations. However, as technology grows and develops day by day and the use of artificial intelligence increases, the information obtained from old-fashioned books has left its place for digitally accessible information. With this increase in digital accessibility, a huge amount of knowledge has been created. In fact, we all take an active role in the formation of this accumulation, even unknowingly, even simply by using the internet.

What is Big Data?

Big Data is data that includes diversity, has an ever-increasing volume, and can be accessed quickly. In short, they are large and complex datasets from new data sources. These data are classified as a result of the analysis and transformed into a more understandable form.

When defining Big Data, it can be considered from two different aspects. According to some, it is constantly growing and developing, and according to others, it is difficult to understand and irregular.

Why is Big Data Important?

Ultimately, the ultimate goal of companies is to increase their revenues and profits. To achieve these goals, they need to reach new customers constantly. The easiest and fastest way to reach new customers is to create a customer profile suitable for them and access the data of the people with this profile. Big Data is a huge ocean that contains this data. In this ocean, companies can convert them into customers by using the data of the people who are suitable for them, and they can succeed in moving ahead of their competitors in the competitive market.

Apart from that, companies can reduce their costs, improve their products, understand customers better and make better decisions thanks to Big Data.

History of Big Data

Big Data consists of complex and gigantic data that is impossible to process with old-fashioned methods. The possibility of storing this data in Big Data and accessing this information has been possible for a long time. But the popularity of Big Data dates back to the early 2000s. Industry analyst Doug Laney expressed the definition of Big Data as 3V in 3 components in the early 2000s:

  • Velocity: With the development of technology day by day, the speed of the data obtained is increasing. These data, which are obtained quickly, need to be processed and stored quickly.
  • Volume: With the rapid increase in data production day by day, the expenditures for storing this data are also increasing. Therefore, accurate processing and storage of high-volume data have gained importance.
  • Variety: The data produced does not come from a single source and in a single way. It can come from different sources such as computers, phones, and tablets, and this data can be produced in different formats such as video, audio, e-mail, and photos.

Challenges of Big Data

In today's world, the importance of Big Data usage has increased in a competitive environment. However, certain difficulties began to emerge:

  • Lack of Knowledge: Due to the rapid development of data processing tools, the number of experts who can keep up with this speed is not enough.
  • Incorrect Understanding of Big Data: Errors may occur due to too much data and insufficient knowledge of employees.
  • Confusion in Data Tool Selection: When choosing the data tool to use, companies prefer tools that are cost-effective and easy to use. In a wrong choice, they both lose financially and waste time and effort.
  • Securing Data: One of the biggest challenges of Big Data is ensuring the security of this data. Data that is not adequately secured can be captured by cyber hackers, and as a result, great damage may occur to the company.

How Does Big Data Work?

Big data works by collecting large amounts of data from a variety of sources and then using sophisticated analytics to identify patterns and insights. Data sources can include web logs, social media posts, transactional databases, machine data, sensor data, and more. Once this data is collected, it can be analyzed to uncover patterns, trends, and correlations. This analysis can help organizations make better decisions, improve customer experiences, and optimize operations.

  • Integrate:

Big data requires revolutionary new technologies and approaches to effectively manage and analyze massive data sets at the terabyte and petabyte levels. Traditional data integration processes like ETL are insufficient for managing big data. Consequently, modern strategies and technologies are necessary to deal with the complexity of big data.

  • Manage:

Storage solutions for big data can be in the cloud, on premises, or both. Depending on the current data location, you can choose the best storage solution for your needs. The cloud is becoming more popular due to its ability to instantly spin up resources as needed. Additionally, you can store your data in the desired form and bring your processing requirements to the data sets with ease.

  • Analyze

Your investment in big data yields dividends when you use it to gain valuable insights. Visualize your data to uncover patterns and trends. Dig deeper to uncover new knowledge. Facilitate collaboration by sharing your findings. Leverage machine learning and AI to create powerful data models. Put your data to work and see what you can achieve!

How Big Data Increases Efficiency?

Big data has the potential to increase productivity by providing insights into how businesses operate, allowing them to make informed decisions quickly and accurately. By collecting and analyzing large volumes of data, businesses can identify trends and patterns, uncover operational inefficiencies and better understand customer behavior. This information can then be used to make changes to processes, products and services, resulting in greater efficiency. For example, businesses can use big data to identify bottlenecks in their supply chains and make adjustments to reduce delays or increase efficiency. Big data also helps businesses detect and prevent cyberattacks, keeping businesses safe.

Examples of Big Data

Since the volume of Big Data is very large, it is not possible to limit the usage areas. However, the main areas of use are areas such as health, communication, social media, marketing, and banking.

  • Health: It provides the possibility of early diagnosis of some diseases. It reduces treatment costs and increases the quality of service.
  • Communication and Social Media: With the development of technology, people can now communicate with each other from any device, anytime, anywhere. Millions of posts are shared on social media in a day.
  • Marketing: With Big Data, marketers know what will attract the attention of consumers more. As a result, they prepare advertisements in line with their interest.
  • Banking: Thanks to Big Data, users' account movements are monitored and theft is prevented.

Advantages of Big Data

Better Decision Making

Big data helps businesses and organizations make better decisions by providing more accurate and reliable information. Businesses and organizations analyze large amounts of data and identify trends that are not possible with smaller datasets. This enables them to identify opportunities and make better decisions.

Cost Savings

Big data helps reduce costs by eliminating the need for manual data collection and analysis. Automated processes are used to collect and analyze large amounts of data much more efficiently than traditional methods.

Improved Customer Experience

It enables businesses to better understand their customers and provide them with a more personalized experience. Thus, it becomes possible to determine customer preferences by collecting and analyzing customer data and to provide them with special products and services that meet their needs.


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