Types of Digital Data | What is a Digital Data? Definition, Features and More | data science

Types of Digital Data:

Digital data, in information theory and information system, is the discrete, discontinuous representation of information or works. Numbers and letters are commonly used representations.

1.  Structured data:

• Structured data is data whose elements are addressable for effective analysis. It has been organized into a formatted repository that is typically a database.
• It concerns all data which can be stored in database SQL in a table with rows and columns. They have relational keys and can easily be mapped into pre-designed fields.
• Today, those data are most processed in the development and simplest ways to manage information.
• Example: Relational data
• Structured data depends on the existence of a data model - a model of how data can be stored, Processed and accessed.
• Because of a data model, each field is discrete and can be accesses separately or jointly along with data form other fields.
• This makes structured data extremely powerful:  it is possible to quickly aggregate data form various locations in the database.

 
Unstructured data:

• Unstructured Data is a data that is not organized in a predefined manner or does not have a pre-defined data model, thus it is not a good fit for a mainstream relational database.
• So for Unstructured data, there are alternative platform for storing and managing, it is increasingly prevalent in It system and is use by organizations in a variety of business intelligence and analytics applications.
• Example: Word, PDF, Text, Media logs.
• The ability to analyses unstructured data is especially relevant in the context of Big Data, since a large part of data in organizations is unstructured. Think about pictures, videos or PDF documents.
• The ability to extract values form unstructured data is one of main drivers behind the quick growth of Big Data.

 

Semi-structured Data:

• Semi-structured Data is information that does not reside in a relational database but that have some organizational properties that make it easier to analyze. With some process, you can store them in the relation database but, Semi-structured exist to ease space.
• Example: XML data.
• The reason that this third category exists is because semi-structured data is considerably easier to analyses than unstructured data. Many Big Data solutions and tools have the ability to read and process either JSON or XML. This reduces the complexity to analyses structured data, compared to unstructured data.

 
Difference between Structured, Semi-structured, Unstructured data.

Structured DATA:

• It is based on Relational database table
• Matured transaction and various concurrency technique
• Versioning over tuple, row, tables
• It is schema dependent and less flexible
• It is very difficult to scale DB schema
• Very robust
• Structured query allow complex joining

 

Semi-structured DATA:

• It is based on XML/RDF
• Transaction is adapted from DBMS not matured.
• Versioning over tuples or graph is possible
• It is more flexible then structured data but less then flexible than unstructured data
• It’s scaling is simpler than structured data
• New technology, not very spread
• Queries over anonymous nodes are possible

 

Unstructured DATA:

• It is based on character and binary data
• No transaction management and no concurrency
• Versioned as whole
• It very flexible and there is absence of schema
• It is very scalable
• Only textural query are possible