Comparison of Structured and Unstructured Data

What Are Structured Data? 

Structured datasets or "structured data" are web data in their most "pure" form. This means there are no duplicate files or data points and nothing is corrupted. Structured datasets have already been transformed or collected in an identical format (e.g., JSON, CSV, HTML, or Microsoft Excel). This means such information can be easily stored in databases and data lakes and analyzed by systems and algorithms to extract valuable insights.

Key Benefits of Structured Data 

Many companies prefer to use structured data for the following reasons:

Reason One: Requires fewer resources to collect and use 
When companies need to collect and use data, they prefer structured data because it requires significantly less time, technical expertise, and energy. Structured data contains:

• No duplicate/incomplete data
• No corrupted files
• No improperly formatted or mislabeled datasets.

Practically, this means companies can focus their efforts on core business development rather than data collection.

Reason Two: Fast querying and analysis 
Continuing from the first reason, since structured data does not require additional processing, the time from "collection to actionable insight" is reduced. This means companies using structured data can offer their clients not only information but also a time advantage over competitors.

Main Disadvantages of Structured Data 

Here are some challenges companies may face when using structured data: 

Reason One: Limited maneuverability and flexibility
Like many things in life, one of the main advantages of structured data (namely its formatting) is also its "Achilles' heel." To illustrate, imagine a company collecting stock movement data in Microsoft Excel format for its analysts. But when this data is fed into a stock prediction algorithm, it requires JSON format. This creates a lack of flexibility, which sometimes hinders fast and simultaneous progress. 

Reason Two: Limited storage capabilities 
Data storage can sometimes be tricky, especially with data warehouses. This is because they typically have a "fixed schema," and changes in requirements may force companies to spend time and effort ensuring data and storage compatibility. 

What Are Unstructured Data

Unstructured data can be thought of as diamonds in the rough or crude oil. Unstructured data may contain information in various formats, have records repeated throughout the dataset, and/or contain corrupted files. Such data must go through a timely "cleaning"/"formatting" process before it can be stored, analyzed, and passed on to teams or algorithms.

Key Benefits of Unstructured Data 

Some companies prefer unstructured data for the following reasons: 

Reason One: Faster to start data collection 
Unstructured data collection tasks can be set up and launched much faster because there are fewer technical parameters to adhere to. 

Reason Two: Versatility of formats 
Since unstructured data can come in various formats, it can be defined as needed, offering increased flexibility and usability.

Main Disadvantages of Unstructured Data 

The drawbacks of using unstructured data include:

Reason One: Custom systems 
Companies that need to structure unstructured data must pay for or develop specialized tools in-house. This requires significant budget and time investment. 

Reason Two: Human resources 
In addition to specialized tools, structuring data requires experts in data science, IT, and DevOps. This can be a whole team of specialists dedicated to collecting, cleaning, and structuring data before the company even begins analysis.

Key Differences: Structured vs. Unstructured Data 

Guides on web scraping will tell you that the key differences between these two data archetypes are primarily in how the data is packaged and who can use it. Here are some key differences:

1. Structured datasets have a single format, while unstructured data has multiple formats.
2. Structured data is typically stored in data warehouses, while unstructured data is stored in data lakes. 
3. Structured data can be used by almost anyone, even without technical training, while unstructured data requires data cleaning/processing specialists before it can be widely used. 

Examples of Unstructured Data

Good examples of unstructured data include open web data collected from social media, reviews/star ratings from e-commerce sites, and discussions on internet forums. 

These often come in HTML or plain text, which is difficult for machines to process. This is because algorithms or data models must classify the information before analyzing it. To do this, they need fields, labels, or properties that plain text files rarely have. 

That is why data researchers often have to find patterns using methods like natural language processing (NLP) or manually label metadata for further processing. 

Examples of Structured Data 

Structured data is much more "straightforward" and can come in various shapes and sizes. Examples include:

• Geolocation data
• Corporate event dates
• Business names 
• Inventory information (trading volume, stock price changes, etc.). 

As you can see, this data is easily classified by machine learning (ML) methods, especially when a logical numerical scheme is present.

What Are Semi-Structured Data

Semi-structured data is a hybrid between "structured" and "unstructured" data. For example, the dataset in question may contain duplicate data points on one hand. On the other hand, it may contain certain metadata (e.g., "file last modified date") that can help systems organize the information. 

Examples of semi-structured data include:

• CSV, XML, and JSON documents
• NoSQL databases
• Electronic Data Interchange (EDI)

If we consider an XML document for an e-commerce brand, it may contain:

• Plain text explaining how the business operates 
• Inventory information 
• Transactional data 

In this example, the plain text part would be considered "unstructured," while the inventory and transactional data would be "structured." 

How to Collect Structured/Unstructured Data 

There is a wide range of options for obtaining target data points, whether aimed at structured or unstructured information. Companies with a dedicated data team can use, for example, Selenium & Puppeteer.  Companies can also choose to purchase proxies for scraping or simply buy a proxy server.

Specialists who go the Selenium/Puppeteer route will need to define target data and URLs, write custom code for data extraction, and then format the data before it can be properly analyzed. 

Companies wanting to offload the burden of data collection and structuring to a third party can choose one of two options:

Option One: Automated data collection 
Companies use a Web Scraper IDE to automatically scrape, map, synthesize, process, and structure unstructured target data.

For an automated tool like the Web Scraper IDE, the process looks like this:

1. Select the target website.
2. Choose the desired collection frequency and data format.
3. Receive data at your chosen destination (webhook, email, Amazon S3, Google Cloud, Microsoft Azure, SFTP, or API).

Option Two: Ready-made datasets 
Datasets are becoming increasingly popular as a tool. This is because businesses no longer want to be involved in the data collection process. They prefer to be a "customer" — much like receiving electricity but not having to generate it themselves. Datasets can be ordered within minutes in the format required by the end user, on demand.