Introduction
In recent years, new and advanced data analysis techniques and software have emerged to enable everyone to gain significant business insights not only from quantitative or structured data in spreadsheets and statistics but also from qualitative or unstructured and semi-structured data in websites, emails, and customer service interactions, and other sources.
With techniques such as topic analysis and opinion mining, qualitative data analysis allows us to go beyond what has happened and discover why it happened. Analyzing semi-structured data can be pretty simple if the proper processes are in place.
Semi-Structured Data
Semi-structured data is information that cannot be organized in relational databases or does not have a strict structural framework but does have some structural properties or a loose organizational framework. Semi-structured data is a text that has been organized by subject or topic or that fits into a hierarchical programming language. Still, the text within is open-ended and lacks structure.
Emails, for example, are semi-structured by Recipient, Sender, Date, Subject, and so on, or are automatically classified into folders such as Outbox, Inbox, Spam, Promotions, and so on using machine learning.
Semi-structured Data Characteristics
Here are some characteristics of semi-structured data.
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Semi-structured data is stored in the form of rows and columns as in databases.
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Because the structure of semi-structured data is not well defined, computer programs cannot easily use it.
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Semi-structured data is difficult to automate and manage because it lacks sufficient metadata.
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The size and type of the same attributes in a group may differ.
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Entities in the same group in semi-structured data may or may not have the same attributes or properties.
- Similar entities are taken together and organized in a hierarchy in semi-structured data.
Uses of Semi-structured data
Some uses of semi-structured data are given below:
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We can integrate data from various sources and exchange data between different systems using semi-structured data. Applications and systems must evolve, but this is impossible if we only work with structured data.
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Let's look at web forms. You may wish to modify forms and collect different information for different users. When using a traditional relational database, the database schema must be changed whenever a new field is required, and fields cannot be left empty. Semi-structured data allows you to capture any data in any structure without modifying the database schema or coding. Changing or removing data does not affect functionality or dependencies.
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When working with semi-structured data, you get a flexible representation that does not require configuration or code changes as the data evolves.
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Data from various sources with varying notation and meaning can be collected and used. Relationships are described as references and are fully integrated into parent objects (tree).
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Semi-structured data allows for the preservation and support of complex query types of data structure and storage and the preservation of relationships between objects and complex schema.
- Queries and reporting can now be performed across multiple systems and data types.
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