Your Bible to Structured Vs. Unstructured data

In this article we will discuss:

• What is structured data 
• What is unstructured data
• What is semi-structured data
• Key differences: Structured vs unstructured data 
• Demonstrating each with examples
• How to collect structured/unstructured data

What is structured data 

Structured Datasets or ‘structured data’ is web data in its ‘cleanest’ form. This means that there are no extra copies of files or data points and nothing is corrupted. Structured data sets have already been converted or collected in an identical format (e.g. JSON, CSV, HTML, or Microsoft Excel). What this means is that this information can be easily stored in databases and data lakes and analyzed by systems and algorithms for high-value insights.

Key structured data advantages 

Many companies prefer using structured data for the following reasons:

Reason One: Requires less resources to collect and use 

When companies look to collect and make use of data, they prefer the structured variety, as it requires significantly less time, technical experts, and energy. Structured data does not contain any:

• Duplicate/incomplete data points
• Corrupted files
• Incorrectly formatted or mislabeled Datasets

Practically, this means that businesses can focus their efforts on their core business development and not on data collection itself.

Reason Two: Quickly queried and analyzed 

Following up on reason one, since structured data does not require any further processing, the time from ‘collection to attaining an applicable insight’ is reduced. This means that companies utilizing structured data can provide their customers with not only an informational, but also a time-based advantage over competitors.

Key structured data disadvantages 

Here are some issues that companies may experience while using structured data: 

Reason One: Limited agility & flexibility

As with many things in life, one of structured data’s biggest advantages (i.e. being formatted) is also its achilles’ heel. To explain this, imagine a company that collects stock movement data in Microsoft Excel format for its analysts. But when feeding this data into their stock performance prediction algorithm, they need the data in JSON. This creates a lack of flexibility, which can at times take a toll on quick/simultaneous progress. 

Reason Two: Narrow storage options 

Storage can sometimes be complicated, especially when dealing with data warehouses. The reason is that these typically have ‘fixed schema’ and changes in requirements can cause businesses to waste time and manpower on aligning data/warehouse compatibility. 

What is unstructured data

Unstructured data can be conceptualized as diamonds in the rough or crude oil. Unstructured data may contain information in a variety of formats, have entries appearing repeatedly throughout a given Dataset, and /or contain files that are corrupt. This data needs to go through a timely ‘cleaning’/’formatting’ process before it can be saved, analyzed, and fed to teams or algorithms.

Key unstructured data advantages 

Certain companies may prefer unstructured data for the following reasons: 

Reason One: It’s quicker to start a collection job 

Unstructured data collection jobs can be set up, and run much quicker as there are fewer technical collection parameters to abide by. 

Reason Two: Format versatility 

Since unstructured data may come in a variety of formats, it can be defined on an as-needed basis, allowing for increased flexibility, and usability. 

Key unstructured data disadvantages 

The disadvantages of using unstructured data include:

Reason One: Tailored systems 

Companies that need to deal with structuring unstructured data will need to pay for or develop customized tools in-house. This is a huge budgetary and time-based constraint. 

Reason Two: Manpower 

In addition to specialized tools, structuring data requires data scientists, IT, and DevOps personnel. This can consist of an entire team of professionals dedicated to the collection, cleaning, and structuring of data before a company even reaches the analysis stage.

Key differences: Structured Vs Unstructured data 

Web scraping guides will teach you that the key differences between these two data archetypes pertain primarily to how this data is packaged, as well as who can make use of it. Here are some of the key differences:

1. Structured Datasets have one format, whereas unstructured data comes in a variety of formats.
2. Structured data is typically kept in data warehouses, whereas unstructured data is usually saved in data lakes. 
3. Structured data can be utilized by virtually anyone, even if they do not have a technical background. Whereas unstructured data requires data specialists to clean/process it before it possesses broader utility.

Demonstrating each with examples 

Unstructured data examples

A good example of unstructured data can be open source web data collected from social media sites, reviews/star ratings from eCommerce sites, and discussions from online forums. 

Very often, it comes in the form of HTML or plain text, which is difficult for machines to process. This is due to the fact that algorithms or data models need to categorize information before it can be analyzed. And in order to accomplish this, they need fields, labels, or properties that plain text files rarely have. 

It is for this reason that data scientists need to find patterns using techniques such as Natural Language Processing (NLP) or tag metadata manually for further processing. 

Structured data examples 

Structured data is much more ‘straightforward’ and can come in many shapes and sizes. Some good examples include:

• GEO-location data
• Dates of corporate events
• Names of businesses 
• Stock information (trading volume, security price changes, etc) 

You can see that these are items that can be easily categorized by Machine Learning (ML), especially if there is a logic-based numerical pattern to follow. 

What is semi-structured data

Semi-structured data is a hybrid between ‘structured’, and ‘unstructured’ data. For example, a Dataset in question may contain duplicate data points, on the one hand. And on the other hand, it may contain certain metadata (e.g., ‘the date the file was last modified’)  which can help systems to order the information in question. 

Semi-structured data examples may include:

• CSV, XML and JSON documents
• NoSQL databases
• Electronic data interchange (EDI)

If we focus on an XML document for an eCommerce brand,  for example, this may contain:

• Plain text explaining the business’s use case 
• Inventory information 
• Transactional data 

The plain text portion in this example would be considered ‘unstructured’ while the inventory data, and transactional data would be considered the ‘structured’ section. 

How to collect structured/unstructured data 

There is a wide range of options for businesses to obtain their target data points, whether they are aiming at structured or unstructured information. Businesses with a dedicated data team may choose to use Selenium & Puppeteer, for example.  Companies may also choose to buy proxies for scraping or simply opt to buy a proxy.

Professionals who go the Selenium/Puppeteer route would need to define their target data and URL, write customized code to perform the data extraction, and then format the data before it could be properly analyzed. 

Companies that want to shift the burden of data collection and structuring onto a third party can do so by choosing from either of two options:

Option One: Automated data collection 

Companies are using Web Scraper IDE in order to automatically clean, match, synthesize, process, and structure unstructured target data.

For an automated tool like Web Scraper IDE, the process is as follows:

1. Choose the target website.
2. Select your preferred collection frequency and data format.
3. Have the data delivered to your destination of choice (webhook, email, Amazon S3, Google Cloud, Microsoft Azure, SFTP, or API).

Option Two: Ready-to-Use Datasets 

Datasets are becoming an increasingly popular tool. The reason for this is that businesses no longer want to be involved with the data collection process. They would much rather prefer to be a ‘client’ – much in the same way that they are supplied with electricity, but have no interest in generating power themselves. Datasets can be ordered within a few minutes in whichever format is required by the end user, on an as-needed basis.