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Remarkable Differences Between Structured and Unstructured Data

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Remarkable Differences Between Structured and Unstructured Data

There are some notable differences between structured and unstructured data. First, structured data is easier to store, digest, and integrate. Unstructured data is more difficult to manage and digest. It requires more technical expertise. For this reason, people who work with unstructured data are often called data scientists or engineers. In larger organizations, there are teams dedicated to managing some data divided into structured and unstructured data.

Structured Data

Unstructured data is stored in its native form without a defined structure. Consequently, it is more flexible and adaptable. For example, it can be stored in different file formats and accumulated faster. However, there are significant differences between the two types of data.

The first difference is the source of data. When structured data comes from databases, it is usually in a structured format. Unstructured data is generally categorical and comes from sources other than databases. It can be used for various business purposes, including determining the effectiveness of marketing campaigns, uncovering potential buying trends, and monitoring compliance with corporate policies. This is why businesses must understand the difference between structured and unstructured data. They need to learn how to use these data types and understand how they relate to one another. They also need to use the right tools to manage these data types.

Compared to unstructured data, structured data is easier for computers to process and consume. It is also more organized and can be used by machine learning algorithms. Moreover, structured data is much more usable by average business users. They do not have to have advanced technical knowledge to understand it. This opens the door to self-service data access for business users.

Unstructured Data

Unstructured data is any file that is not structured and is stored in its native format. For example, unstructured data may include images, videos, songs, documents, etc. These files can be analyzed by advanced analytics software, but they still need to be structured first. In addition, unstructured data tends to accumulate at much faster rates.

Structured data can be processed by sophisticated analytics software, but most of these tools are in early development. In addition, structured data is often in a predefined format, such as text or numbers, unlike unstructured data. This means that data mining techniques can be more complicated, and developing best practices can be more difficult.

In contrast, unstructured data is more difficult to search and analyze. While structured data is typically stored in relational databases, unstructured data is stored in media files or NoSQL databases. In addition, unstructured data is difficult to process because it doesn’t have a predefined data model. However, unstructured data can be processed using natural language or text mining. These two types of data are useful in various applications.

Structured data is easy to store and access. This data type is often created from surveys asking respondents to rate different products or services. Survey data is easy to store in a database and allows for statistical analysis.

Semi-Structured Data

One of the most important features of big data is the ability to integrate unstructured data with structured data. Unstructured data is typically 80% of the Internet and requires a different approach than structured data. One such approach is schema integration. However, because unstructured sources do not typically contain a structured schema, they are often characterized only by keywords. When this is the case, the unstructured data will often need to be processed and analyzed using a unified approach.

Structured data can be easily manipulated and analyzed, while unstructured data is more difficult to process and search. Both types are valuable to any enterprise, so handling them as efficiently as possible is essential to reduce errors and improve productivity. Here are some examples of data types.

Traditional databases can only handle structured data and are not designed to handle unstructured data. As a result, companies usually outsource unstructured data processing to a secondary system. This process requires more copies of data, which eats up storage space. Ultimately, this approach can be expensive.

Unstructured data consists of any file type that is not structured. It can include images, videos, songs, and documents. On the other hand, structured data is often stored in a relational database. Unlike unstructured data, semi-structured data does not follow a predefined data model but still includes identifiable markers that help search it. One example of semi-structured data is smartphone photos containing both structured and unstructured data.

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