Data comes in many forms and from many sources. It can be used in many ways to solve different problems. In this realm of data science, there is data everywhere, and humans are producing more and more data each day. The data is normally divided into two main categories, i.e., structured and unstructured data. These two types of data serve different purposes and roles in research. Their ways of implementation are also different from each other. To understand all these differences, it is important that you know what these terms entail. So, let’s start today’s discussion on structured and unstructured data with their definitions.
What is structured data in research?
Structured data is the type of information that is provided in an easy, standardised, and uncomplicated format. Due to its standard structure, this data is easily accessible in relational databases. The structured data is mostly quantitative and is managed in the form of rows and columns in a SQL database. SQL means Structured Query Language. This kind of data is less stressful as it does not require any processing. In some cases, it requires a little processing of the data to make it available for further analysis.
What is the role of structured data?
Structured and unstructured data serve different roles. As far as structured data is concerned, its role in research is far more than unstructured. It serves different purposes for a website or a company. Some of its roles are as follows:
- This data serves the role of increasing click-through rates of your website
- It enhances the search visibility of your company’s website
- Faster indexing of your site’s content is the next role that it serves
- More importantly, the structured data helps Google understand your data better and can help enhance your site’s visibility
What is unstructured data in research?
Unstructured data is the opposite of structured one. This kind of data refers to the kind of data that has no order and is not processed. The format of this data is not a predefined one. It means that you will find this data in many formats other than a standard one. For example, the data in the form of images, videos, audio etc., is all unstructured. Almost 80% of the data that companies collect using different means is unstructured. Unstructured data is also known as free text, which has no logical order or meaning.
What is the role of unstructured data?
Like structured data, unstructured data also has a role to play in research. Although they play different roles in research, structured and unstructured data can be helpful in many ways. Some of the primary roles that this data play in research are as follows:
- It helps the enterprises make better decisions for the company’s growth, only if it is properly leveraged
- As this is the data which is not supported by a structured schema, it is very flexible and portable
- Due to its flexible nature, it is quickly scannable and provides useful insights into the customer behaviour
Hence, these are some of the roles this data play in research. If you are still unable to understand this role, then immediately contact a PhD dissertation help. The employees of such a service will surely make it easy for you to grab.
How to implement structured data?
Structured data helps you organise information on your website and communicate more effectively and efficiently with search engines. But do you know how to implement structured and unstructured data? Normally, the implementation of structured data is what occurs. The unstructured data is first processed and converted to structured, and then it is implemented in research. It means if you know how to implement structured data, you also know the opposite. Hence, a brief description of the steps to implement structured data is as follows:
1. Use a tool to code the data
The first step in the implementation is to choose a tool to code the data. Normally, the Structured Data Markup Helper by Google is used for this purpose. This tool is free to use and helps you create the code for your structured data. Hence, use this tool and code the data.
2. Select the data type
The second step is about selecting the data type and adding the URL of the page that you want to structure based on your data. When you select the data type, the tool automatically collects the existing formatting of the data. Once you are done with this, click Start Tagging in the tool.
3. Assign the data tags
Using this tool, you will see that your website is on the right, and the structured data that you want to implement is on the left. Highlight different components of your web page and assign them data tags like author name and date published or revised. The tool will also suggest your different tags based on the type of web page.
4. Create the HTML code
When you have finished tagging, then click on the HTML button and start creating the code in HTML. You can find this button in the upper right-hand corner of the tool. Once you click on the button, the tool creates a snippet of code for your structured data.
5. Copy and add the HTML
After creating the code, move on to the next screen. On the next screen, you will see structured data markup on the right side of the screen. This powerful tool automatically creates the script or code as JSON-LD markup. So, copy the code and add it to your page where you want to add the structured data.
6. Test your data
One last step in the implementation of structured and unstructured data is the testing of the data. It means that you see whether your created code has produced desired results or not. For this purpose, you can use Schema.org’s Scheme Markup Validator.
Conclusion
Today’s world is filled with data. Some of the collected data is structured, while more of that is unstructured. Structured and unstructured data are the two categories of data. Both types of data have different roles and purposes in research, as discussed above. The six-step procedure to implement the structured data is also mentioned above. So, learn and use this data to enhance your site’s visibility.
