Datasets are exploding at an ever-accelerating rate, so collecting and analyzing data to maximum effect is crucial. Companies and businesses focus a lot on data collection in order to make sure they can get valuable insights out of it. Understanding information structure could be a key to opening its value.
The term structured data refers to any type of data where formatting, numbers, and layout are fixed fields in a file or record. Excel tables are a prime example of this, but they are not the only examples of structured information.
Most questionnaires and application forms are fixed forms, but these forms can also be distributed in a variety of ways, including by email, social media, and other forms of communication.
The most attractive feature of structured data is that it is easy to understand in machine language and can be searched and manipulated in many different ways. Those who work with relational databases can enter, search and manipulate structured data relatively quickly. Examples of structured data include questionnaires, surveys, social media and other forms of communication, and social networks.
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Unstructured data is classified as qualitative data, which means that it cannot be processed or analyzed with conventional tools and methods. It is difficult to deconstruct it because it does not have a predefined model, so the data must be stored in its original format. The data model is a combination of data types such as text, image, video, audio, and other rich media.
The vast majority of data generated today is unstructured and accounts for 80% or more of all business data. A typical example of unstructured data is the data from the US Department of Energy’s Office of Management and Budget (OMB).
This means that companies that do not take unstructured data into account miss out on a lot of valuable business intelligence. Because of this disorganized structure, it is very cumbersome and even impossible for machines and computers to understand all this. Great strides have been made in machine learning to teach machines how to understand and extract data from unstructured documents.
Data with a certain degree of organization is semi-structured, however, this may vary. This is the third category that falls somewhere between the other two, and it is achieved by using types, tags, or other defined properties that are introduced into the hierarchy system within a file or file.
A smartphone photo is a good example of semi-structured data with a certain degree of organization. A photo taken on a smartphone contains time and place, marked by a series of tags such as date, time, and other identifiable (and structured) information.
Equipped with AI and machine learning, Gleematic ensures that important information is extracted even in the most complex data structures. Semi-structured data formats include JSON, CSV, and XML file types.
DeCouto, C. (2021, January 25). Understanding Structured and Unstructured Data. Sisense. https://www.sisense.com/blog/understanding-structured-and-unstructured-data/
Written by Elsa Ajarwati