What Is Semi-structured Data?

Data comes in three main forms: structured, unstructured, and semi-structured. Structured data follows a rigid format, where the data is systematically laid out in predefined rows and columns. Medical history, flight data, and markup within HTML files are all examples of structured data. Commonly stored in relational database systems, Microsoft Excel, or Google Sheets, you’ll find that structured data is typically easy to manage and scale.

Unstructured data, on the other hand, is qualitative and lacks a defined format. Examples of unstructured data you might recognize include surveillance footage, images, survey responses, and call recordings. This type of data, typically stored in non-relational database systems or data lakes, can be challenging to organize and analyze. 

Semi-structured data falls between structured and unstructured data; it doesn’t adhere to a tabular format like structured data, yet it is more organized than unstructured data. Read on to further explore semi-structured data, including its various types, applications, benefits, drawbacks, and more. 

Types of semi-structured data

Semi-structured data includes metadata, like tags, to define attributes and organize data into preset fields. This improves its cataloging, access, and analysis over unstructured data. The following are common examples of semi-structured data:

HyperText Markup Language (HTML) 

HTML is a popular markup language for designing web pages. HTML qualifies as semi-structured as it uses tags to structure text, images, and video, among other multimedia elements. 

Example: <h2>This is a heading</h2>

Here, <h2> is the opening HTML tag, while </h2> is the closing tag. True to its name, the h2 tag styles the text between the opening and closing tags as a level-two heading, which you can use for subheadings on websites.

Likewise, HTML provides a variety of tags that allow you to format different types of content on a web page. These include tags for styling text in italics, creating line breaks, displaying bullet points, and more. 

Extensible Markup Language (XML)

While HTML controls the appearance of data, XML tags provide the definition of the data. XML’s main function is to store data in a manner that facilitates easy reading and sharing between applications.

Example: Much like defining field names for a data structure, you can use custom XML tags that fit your application. For instance, if you’re organizing information for a music album, you could use tags like <album>, <name>, and <artist>. An XML document for a specific album might look like this:

<album>

<name> Thriller </name>

<artist> Michael Jackson </artist>

</album>

JavaScript Object Notation (JSON)

JSON, built on JavaScript, facilitates data exchange between a server and a client. Note that JSON doesn’t use tags to label data, unlike HTML and XML. JSON represents data in two main formats:

• Object: A set of key-value/name-value pairs enclosed in braces ({}). Each pair begins with a name, a colon, and a value. All pairs are separated by commas.

• Array: An ordered list of values enclosed in brackets ([]) with array items separated by commas.

Example: The JSON below defines an array of objects called “books.” Each object represents a book, with two key-value pairs: “BookID,” which stores the title of the book, and “Author,” which stores the author’s name.

var books = [

 {“BookID”: “harry potter and the philosopher’s stone”, “Author”: “JK Rowling”},

 {“BookID”: “to kill a mockingbird”, “Author”: “Harper Lee”}

];

Who uses semi-structured data? 

Businesses, regardless of their size, make use of and benefit from semi-structured data, two examples being marketing companies and restaurants. Specifically, semi-structured data is managed and maintained by:

• Programmers

• Software developers

• Web developers

• Analysts

Practical applications of semi-structured data

Because of the flexibility of semi-structured data, you can use it for a variety of purposes such as discovering customer preferences, learning how they behave, and recognizing the different trends developing in the market. These attributes make it suitable for a wide range of use cases across industries. 

E-commerce 

To track product performance, e-commerce firms often gather online reviews from customers. These reviews consist of unstructured text as well as structured data, like product ratings. Together, these elements create semi-structured data that offers brands insights into customer satisfaction.

Health care 

Health care systems combine structured data such as patient profiles and history with unstructured notes or written comments from health care providers. The resulting semi-structured data allows for streamlined patient records, improving patient diagnostics.

Internet of Things (IoT) 

From thermostats to smartwatches, IoT devices generate continuous data streams that mix structured metadata such as timestamps with unstructured sensor readings like temperature fluctuations or pulse rate. Ultimately, the ability to analyze and act on this continuous stream of IoT data enhances overall system reliability and user experience.

Pros and cons of using semi-structured data

As with all forms of data, semi-structured data has its strengths and limitations. Below, you’ll find a closer examination of both.

Benefits 

The perks of using semi-structured data are: 

• Data portability: With better analytical tools for semi-structured data compared to unstructured data, transferring it between network locations is simpler.

• Support of multiple data types: Semi-structured data allows for the inclusion of formats like emails and social media posts, providing businesses with a richer data set.

• Flexible data framework: With semi-structured data, you can easily update, remove, or add new elements without altering the entire data structure, a feature not available with structured data.

Drawbacks 

Notable downsides to using semi-structured data are: 

• Integration barriers: Merging semi-structured data with different data types can be challenging.

• Scaling concerns: The absence of a predefined schema can complicate indexing, partitioning, and scaling semi-structured data.

• Data analysis challenges: Semi-structured data, as opposed to structured data, contains elements that are difficult for computers to interpret, requiring organizations to develop methods or manually handle unstructured components.

Tips for getting started with semi-structured data

If you’re just starting out, the following tips, such as understanding data formats and learning how to query, can help you begin working with semi-structured data.

Gain foundational knowledge. 

Begin by familiarizing yourself with the semi-structured data formats commonly found in contemporary data sources, including document management systems. These include JSON, XML, HTML, comma-separated values (CSV), log files, and more. 

Learn how to query semi-structured data. 

After gaining a basic understanding of semi-structured data, it’s helpful to learn about databases or storage systems designed to query it. Apache Cassandra, for instance, provides strong querying capabilities for JSON and XML. You might also want to learn specialized query languages like XPath to enhance your ability to extract data effectively.

Apply for entry-level jobs in data analysis or web development. 

Given the widespread use of semi-structured data in data analysis and web development, pursuing entry-level roles such as junior data analyst, data analysis assistant, or junior web developer can help propel your career forward. As you gather more experience, you can attain the skills needed to transition to an advanced role. Finally, if you’re interested in web development, this is a growing job sector likely to expand by 8 percent from 2023 to 2033 [1]. 

Learn more about semi-structured data on Coursera 

The flexible design of semi-structured data allows it to accommodate information that doesn’t adhere to predefined schemas. As businesses continue to generate and rely on vast amounts of diverse data, professionals who can manage, process, and analyze semi-structured data will be in high demand.

If you’re considering a career in data analysis, the Introduction to Data Analytics course, available on Coursera, is an excellent place to start. Offered by IBM, this five-module course delves into important aspects of data analytics, including data structures, file formats, and sources of data.