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What Is Big Data?
The rapid increase in the use of the internet and cloud-based systems over the years clearly reveals the growing volume of data generated for brands operating in digital environments. By continuing to read, you can find answers to the questions we have detailed for you, such as what big data is, how it is used, what big data is used for, and what real-world use cases look like. Here is the answer to the question “What is big data?”!
“Big data” is defined as the entirety of structured, semi-structured, and unstructured data that cannot be effectively processed using conventional databases and traditional software techniques due to its scale. Therefore, when answering the question what does big data mean, it is necessary to consider not only the volume of data, but also how this data is collected, processed, analyzed, and transformed into meaningful outputs.
While you are here, you may also find our article titled What Is Data Mining? The Power of Cloud-Based Analytics interesting.
The primary objective in the big data ecosystem is to derive insights that contribute to decision-making processes by processing high-volume data from different sources with the right tools and infrastructures. Especially with the acceleration of digitalization, big data has become a key element in creating competitive advantage for businesses. At this point, scalable systems and cloud-based infrastructures make it possible to manage big data effectively. In the list below, you can see the components of big data.
Big Data Components
- Volume: Big data refers to massive amounts of data that exceed the limits of traditional systems.
- Velocity: In the big data domain, data is generated continuously and at high speed, and is expected to be processed with low latency.
- Variety: It includes different data types such as structured, semi-structured, and unstructured data.
- Veracity: The accuracy of big data significantly affects data reliability and analysis outcomes.
- Value: When analyzing big data, it is essential to derive meaningful and actionable insights from the data.
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Big Data Storage Methods
Big data storage methods vary according to different needs. These methods include databases, data warehouses, distributed file systems, public and private cloud systems, on-premises servers, and hybrid cloud systems. You can see the details of each below.
- Databases: Used for the fast storage and querying of structured data.
- Data Warehouses: Systems where data from different sources is centrally collected for analysis and reporting purposes.
- Distributed File Systems: Storage infrastructures that enable scalable storage of large volumes of files and raw data.
- Public Cloud Systems: Provide flexible and scalable storage solutions accessible over the internet.
- Private Cloud Systems: Cloud infrastructures configured specifically for an organization, offering high security and control.
- Hybrid Cloud Systems: Flexible storage models that combine public and private cloud structures.
- On-Premises Servers: A traditional storage method where data is stored on physical servers within the organization.
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Types of Big Data
Structured Big Data
Structured big data refers to data stored in a predefined schema or tabular format that can be easily queried. It typically consists of rows and columns and can be processed seamlessly by traditional databases. Thanks to its orderly structure, this type of data provides high speed and accuracy in analysis and reporting processes. Examples of structured big data include customer name and surname information, order records, product price lists, inventory data, banking transaction data, or employee details stored in corporate databases.
Unstructured Big Data
Unstructured big data refers to raw and disorganized data that does not have a specific format or fixed data model. Such data cannot be processed directly by traditional databases and generally requires specialized analytics tools. It represents the largest share of data by volume in the big data ecosystem. Social media posts, email content, images and video files, audio recordings, PDF documents, and customer reviews are examples of unstructured big data.
Semi-Structured Big Data
Semi-structured data is a type of data that partially combines the characteristics of structured and unstructured data. In other words, while it is not completely unorganized, it also does not follow a traditional tabular structure. This type of data contains tags, keys, or metadata that allow it to be processed in a partially structured manner. In short, semi-structured big data offers a balance between flexibility and analyzability. JSON and XML files, API outputs, web server logs, data generated by IoT devices, and email header information are among the examples of semi-structured big data.
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What Is Big Data Used For?
So, what can be done with big data? One of the most important benefits of big data for companies is that it enables predictive analytics based on historical and real-time data. This allows brands to analyze customer behavior, increase sales, and ultimately generate more revenue. By continuing to read, you can find the most common answers to the question how is big data used.
- Analyzes customer behavior to reveal purchasing habits and preferences.
- Provides forecasts about future demand, sales, and trends through predictive analytics.
- Helps design marketing strategies in a more targeted and personalized manner.
- Enables cost reduction and increased efficiency by analyzing operational processes.
- Supports early detection of fraud, system failures, and security vulnerabilities through risk analysis.
- Helps organizations make more accurate decisions in product and service development by interpreting user feedback.
- Enables fast and data-driven decision-making processes through real-time data analytics.
5.
Big Data Examples from Global Brands
Here are some big data examples from global brands!
Google
When it comes to using big data, Google is undoubtedly one of the first brands that comes to mind. Google analyzes billions of search queries to improve search engine results. The same data is also used in advertising targeting and user experience optimization processes.
Netflix
Netflix analyzes users’ viewing history, pause durations, and content interactions to develop personalized recommendation systems. At the same time, this data is actively used to decide which types of content should be produced.
Amazon
As one of the giants of e-commerce, Amazon analyzes search history, purchasing behavior, and cart data to offer product recommendations. Thanks to big data analytics, inventory management, pricing, and logistics processes become much more efficient.
Spotify
Spotify analyzes users’ listening habits, favorite genres, and playlists to deliver personalized music recommendations. Its annual “Wrapped” summaries stand out as a successful example of big data analytics.
Uber
Uber analyzes real-time location, traffic density, and demand data to optimize driver–passenger matching. These analyses also form the basis of the company’s dynamic pricing system.
