Data can be defined as the information converted into a very economical form for translation or processing. Data, including video, images, sounds, and text, is represented as binary values that mean 0 or 1. Using these two numbers, patterns are generated to store different types of data. The smallest unit of data in a computer system is a bit, and a single value is represented using a bit. A byte is eight binary digits long.
Data can be defined as information converted into binary digital form in today's computers and transmission media. With the increase in the number of computer users, the amount of data generated also increased significantly within the last decade. So a new term is coined for such a huge volume of data that is generating at a rapid speed. It is called big data. It is not only the volume of the data that has increased over time.
Along with the volume, the variety of the data getting generated is increasing rapidly. So it becomes very important to classify the types of data that is getting generated. In this era of the internet, a vast amount of data is generated. This data can be text, images, videos, documents, pdf files, videos, log files, and many more.
Now, let us classify this vast amount of data in broadly following categories. These categories are:
Structured data differentiates from semi-structured data. It is information that has been specifically designed to be easily searchable, and it is quantitative and highly organized. It is typically stored in relational databases (RDBMS) and is frequently written in structured query language (SQL), a standard language developed by IBM in the 1970s for communicating with databases.
Humans or machines can enter structured data, but it should adhere to a rigid framework with predefined organizational properties. Consider a hotel database that can be searched by guest name, phone number, room number, and other criteria. Or Excel files with data neatly organized into rows and columns.
Semi-structured data is a type of structured data that does not hold to the tabular structure of data models associated with relational databases or other types of data tables but still includes tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. As a result, it is also referred to as a self-describing structure.
In semi-structured data, entities of the same class may have different characteristics despite being grouped next to each other, and the order of the attributes is unimportant.
Since the rise of the internet, semi-structured data have become more common, as full-text documents and databases are no longer the only types of data. Various applications require a medium for information exchange, and Semi-structured data is common in object-oriented databases.
For example, Emails are semi-structured by Sender, Recipient, Subject, Date, and so on, or are automatically classified into folders such as Inbox, Spam, Promotions, and so on, using machine learning.
Semi-structured data is a hybrid of Pictures and videos. For example, it may comprise Meta tags relating to the location, date, or person who took them, but the information is contained within them has no structure. Consider social media platforms such as Facebook, which organizes information by Users, Friends, Groups, Marketplace, and so on, but the comments and text within these classifications are unorganized.
Semi-structured data is easier to analyze than structured data because it has a marginally greater level of organization. Still, it must also be broken down with machine learning tools before it can be analyzed without human intervention. It also includes quantitative data, which, like entirely unstructured data, can also provide much more useful insights.
There is also unstructured data, which is typically open text, images, videos, and other media with no predetermined organization or design. Consider online reviews, documents, and other sources of qualitative data on opinions and feelings. This data is more difficult to analyze, but it can be structured to extract insights using machine learning techniques, though it must first be structured so that machines can analyze it.
Examples of Semi-Structured Data
Semi-structured data comes in a multitude of formats, each with its own set of applications. Some are hardly structured at all, while others have a quite sophisticated hierarchical structure.
The three main languages used to interact or transfer data from a web server to a client are CSV, XML, and JSON (i.e., computer, Smartphone, etc.).
CSV stands for "comma separated values", and data are expressed as Lucy, Jessica, and Anthony. It can be expressed in the same way as Excel files but with only one column.
Email is arguably the most common type of semi-structured data since we all have it on a routine basis. Email messages comprise structured data such as name, email address, recipient, date, time, and so on, and are also organized into folders such as Inbox, Sent, Trash, and so on.
Even if most email software packages allow you to find by keyword or other text, the data inside every email is unstructured. Emails can provide a plethora of data mining opportunities for enterprises to analyze customer feedback, ensure customer service is operational, and assist in creating marketing materials.
HTML or "Hyper Text Markup Language" is a hierarchical language, and it is similar to XML but unlike XML. HTML is used to create websites and visualize information. The commentaries used to display text and images on a computer screen provide the semi-structure of HTML, but the text and images themselves are unstructured.
Web pages are designed to be easily accessible with tabs like Home, About Us, Blog, Contact, and so on, or links to other pages within the text, to help users find the information they need. Of course, this is all authored in HTML, but we don't see that on the computer monitor. And the text and data on each of these pages are unstructured.
Non-relational databases are commonly referred to as NoSQL ("not only structured query language" or "non SQL") databases, with the most common types being document, key-value, wide-column, and graph. They are versatile data storage devices because they can store both structured and unstructured data. And are ideal for semi-structured data because they scale easily. A single added layer of structure (subject, value, data type, etc.) can make unstructured data easier to search and process.
Electronic Data Interchange (EDI)
EDI is the electronic computer-to-computer transmission of business documents such as purchase orders, invoices, and inventory documents previously transmitted on paper. Because EDI uses many standard formats, including ANSI, EDIFACT, TRADACOMS, and ebXML, businesses must use the same format when communicating via EDI. EDI enables much faster and less expensive document transmission. Although each format is intended to be easily processed and understood by machines, the data contained within each transmission is unstructured.
Analyze Semi-Structured Data
Interacting with semi-structured data is less difficult than dealing with unstructured data, but it still creates challenges. Text analysis designs can now instantaneously break down and analyze semi-structured and unstructured text data for powerful insights, thanks to AI-guided machine learning technology.
Advantages & Disadvantages of Semi-Structured Data
Semi-structured data has the following advantages and disadvantages, such as:
- Semi-structured data is indeed not limited to a single architecture. So, for instance, a NoSQL database could even hold any format of data and can be easily scaled to store massive amounts of data. The downside is that this makes analyzing the data much more difficult. It must be manually processed (which takes hundreds of human hours) or first structured into a format that computers can comprehend.
- Semi-structured data is far more storable and mobile than entirely unstructured data, but the storage cost is typically much higher than structured data.
- Semi-structured data is versatile in that it allows you to start changing the schema. Still, the schema and data are frequently too tightly linked, so when conducting queries, you primarily have to know already what data you're looking for.