Structured Data and Unstructured Data

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Structured Data, Unstructured Data, and Semi-structured data

What is structured data?

Structured data is essentially data that conforms to a data model, has a well-defined structure, and follows a consistent order. It should also be easily accessible and/or usable by people or computer programs.

Structured data is generally stored in well-defined schemas like databases. It tends to be tabular and has rows and columns that define its attributes in a very clear manner.

You will generally find Structured Query Language (SQL) being used to manage the structured data that is stored in databases.

The sources of unstructured data include:

  • SQL Databases
  • Spreadsheets like Excel
  • OLTP Systems
  • Online forms
  • Sensors like GPS or RFID tags
  • Network and Web server logs
  • Medical devices


What are the characteristics of structured data?

The characteristics of structured data include:

  • The data conforms to a data model and has an easily identifiable structure
  • The data is stored in rows and columns 
  • The data is well organized, which means that the definition, format, and the meaning of the data is known.
  • The data lives in fixed fields inside a record or a file.
  • Similar entities get grouped together to form relations or classes.
  • Entities that belong to the same group will have the same attributes.
  • The data is easy to access and query. Because of this, it is easy for other programs to make use of the data.
  • The data elements are addressable, which means that analyzing and processing them becomes a rather efficient task.


What are the advantages of structured data?

  • The well-defined structure of this kind of data aids in easy storage and access of data.
  • It is possible for data to be indexed based on text string and attributes. This makes search operations easier and hassle-free.
  • The process of data mining becomes easier - it becomes rather easy to extract knowledge from data.
  • Operations like updating and deleting tend to be rather easy because of how well structured the data is.
  • Business Intelligence operations like data warehousing can be undertaken with great ease.
  • It is easily scalable in case there are increments in the data.
  • It is easy to ensure security to the data.


What is considered unstructured data?

Unstructured data refers to data that does not happen to be arranged in accordance with a pre-set data model or schema, because of which it cannot be stored in a traditional relational database or RDBMS. A couple of commonly seen types of unstructured data are text and multimedia. A lot of business documents are unstructured, just like email messages, videos, photos, webpages, and audio files.

Around 80-90% of the data that organizations generate and collect tends to be unstructured… and these volumes are growing at a pace exponentially faster than the rate of growth for structured databases.

These unstructured databases hold a treasure of information that could be used to inform and guide business decisions. But, historically, unstructured data has been proven to be rather hard to analyze. With the help of artificial intelligence and machine learning, there are new software systems emerging that have the ability to search through vast amounts of unstructured data to uncover useful and actionable business intelligence.

There are several ways in which unstructured data could be stored. You could store this type of data in applications, NoSQL (non-relational) databases, data lakes, and data warehouses, etc.

What is semi-structured data?

Semi-structured data is data that does not reside in a relational database or any other data table, but still possesses some organizational properties, like semantic tags, that make it easier to analyze.

If you want a good example of semi-structured data, just look at HTML code - it does not restrict the amount of information you want to collect in a document, but it still enforces hierarchy through the use of semantic elements.

Some examples of semi-structured data include emails, CSV, XML, and JSON documents, NoSQL databases, HTML, electronic data interchange (EDI), and RDF.



What's the difference between structured, semi-structured, and unstructured data?

As explained above, structured data is data that is organized to enable easy searching. Unstructured data encompasses most of the other types of data and exists in formats like audio, video, and social media postings. It is not easy for traditional tools to search and analyze unstructured data. Semi-structured data is data that isn’t stored in relational databases, but it has some organizing properties that make it easier to parse and analyze. Semi-structured data has internal tags and markings that enable grouping and hierarchies.

You shouldn’t really look at it as a conflict regarding which data type is better than the other. You just have to pick one of these data types of the basis of the types of applications that you are interested in. You can use relational databases to work with structured data, and most other kinds of systems can handle unstructured data.

How is unstructured data used?

It is possible to carry out simple content searches on textual unstructured data. The traditional analytics tools are optimized for working with highly structured relational data, so they aren’t too useful for unstructured sources like rich media, customer interactions, and social media data.

Big Data and unstructured data generally tend to go together. IDC estimated that 90% of extremely large datasets that are generated happen to be unstructured. 

New tools have now emerged to analyze unstructured data. These platforms use artificial intelligence and machine learning to function at near real-time speed and educate themselves based on the patterns and insights they uncover. They are being used on extremely large datasets for applications that have never been possible before. Some of these applications include:

  • Analyzing communications for the purpose of regulatory compliance
  • Tracking & analyzing customer social media conversations and interactions
  • Gaining reliable insights into customer behavior and preferences


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