Structured Vs. Unstructured Data in Machine Learning

Data is the backbone of technological progress and business growth. Considering the huge volume of data companies generate daily, conventional tools aren’t sufficient to process or leverage data analytics to extract meaningful insights.

As it happens, analyzing and understanding data is a prerequisite for data processing. This is particularly important because data comes in two different forms: structured and unstructured. Each data type is accumulated, processed, sorted, and analyzed to derive valuable information and improve overall decision-making. Both structured and unstructured data are stored in different databases.

In this article, we’ll explore the two major data types and take a look at the advantages and limitations of each to draw a structured data vs unstructured data comparison. 

What is Structured Data?

Structured data is well-organized, easy to quantify, well defined, simple to search and analyze with software in data analytics. Structured data is usually located in a specific field within files or records. It is easy to place structured data into a standard pattern of set rows, tables, and columns.

A good example of handling structured data is accessing the hotel database where all the relevant details of the inmates, like name, contact number, address, etc., can be accessed with ease. Such types of data are structured.

Structured data is encased in RDBMS (relational databases). Any information stored in the database can be updated by person or machines and accessed with ease by algorithms or manual search. Structured Query Language (SQL) is the standard tool used to handle structured data, be it locating, adding & deleting, or updating.

Let us now take a look at the pros and cons of structured data.

Pros of Structured Data

1. Easy applicability to machine learning algorithms

 The well-organized and quantitative nature of structured data makes it very easy for them to update, modify, and search for data.

2. Easy to use for business people

 Anyone with basic knowledge of data and its related applications can use structured data. Structured data facilitates the self-service mode of data access to the user. So, it is not necessary to have in-depth knowledge of data types and their relationships.

3. More tool options

 As structured data has been in use for a long time, most tools have been tested for their efficiency in data analysis. Data managers have a lot of tools to choose from when tackling structured data.

4. Seamless integrations

 Simple and streamlined programs like Excel can be used to store and organize structured data. Furthermore, several other analytical tools can be linked to Excel for further data analysis as required.

5. Suitability

Structured data is highly suitable for basic organization and quantitative analysis.

Cons of Structured Data

1. Limited use

Structured data lacks versatility. It can be used only with a set vision and cannot deviate from that as it has a predefined structure.

2. Restricted data storage

 Structured data is stored in data warehouses with a rigid data storage method. Any change in data storage will require a complete update of existing data to accommodate additional expensive and time-consuming requirements. 

3. Not suitable for detailed analysis

Structured data can offer limited insight as it works on pre-set parameters. It does not provide the details of how and why the data analytics is carried out.

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What is Unstructured Data?

Unstructured data refers to information that is not organized and cannot be accommodated in a set or defined framework. It can be stored only in its original form until put to use. This feature is known as schema on read. 

The majority of the data we come across is unstructured. Nearly 80% of the enterprise data is unstructured; this percentage appears to be constantly growing. Unstructured data comes in various formats like emails, posts on social media platforms, chats, presentations, images, satellite feeds, and data from IoT sensors.

Naturally, companies that invest time and money in deciphering unstructured data get access to vital and valuable business intelligence to increase their profits. It can also help them connect to their customers more efficiently and in a personalized fashion, thereby contributing to increased profits.

Unstructured data is rather tricky to decipher; extracting valuable insights from unstructured data requires cutting-edge tools and complex algorithms by skilled data professionals who can leverage top-class programming skills and data analytics. 

However, the results are highly rewarding as the crucial qualitative insights (customer feedback, decision-making) help businesses streamline customer queries and improve organizational efficiency.

Advantages of Unstructured Data

1. Liberty to stay in the natural form

As unstructured data is accumulated in its original form (native form), it is not defined until used. This results in a larger reserve pool as the unstructured data can adapt to any data requirement. It also facilitates data analysts and data scientists to process and analyze only the required information. 

2. Easy and faster data gathering

Unstructured data has an impressive accumulation rate. As it does not require pre-set parameters, it can be gathered easily and quickly.

3. Massive data storage

Cloud data lakes store unstructured data due to their impressive storage capacity. Cloud data lakes charge on a pay-for-what-you-use basis and are highly cost-effective, flexible, and scalable.

Disadvantages of Unstructured Data

1. Need for data science expertise

As we mentioned before, you require data science expertise to leverage unstructured data for useful processing and analysis. So, a regular business person or user can not possibly extract any meaningful information from unstructured data in its crude native form. Processing unstructured data requires the knowledge of the topic related to the data and the knowledge of linking the data to make it resourceful. Even more disadvantageous is that there is a shortage of data science professionals despite the continually growing demand across industries.

2. Limited choice of tools

 Unstructured data requires specialized tools for manipulation besides data science expertise. Standard data analytics tools are useful and compatible with structured data, and data engineers only have a limited choice of tools to analyze unstructured data. However, new tools and technologies are being developed in the market as we speak.

Structured Data vs Unstructured Data: A Comparison

Structured Data

Unstructured data

Structured data can be quantified and represented in numbers, dates, strings, and values.

Unstructured data is qualitative and is represented in chats, videos, audio satellite feeds, and so on.

Structured data is stored in relational databases in rows and columns.

In cloud data lakes, unstructured data is stored in its native forms (audio, images, chats, or video).

It is estimated that about 20% of the data available is in a structured form.

It is estimated that 80% of the available data is unstructured.

They can be seen in closed surveys like scores of NPS, CSAT marks, and web analysis.

They can be seen in customer queries, feedback, social media posts, emails, reviews, etc.

They are stored in a data warehouse.

They are stored in non-relational databases like NoSQL, applications, data warehouses, and data lakes.

They display the trends to show what is happening.

They display patterns and trends explaining in detail why a particular thing is happening.

Demands less storage capacity

Demands more storage capacity

They can be analyzed with simple tools like Excel.

They can be analyzed only with specialized AI tools.

Structured data have a defined data model.

Unstructured data do not have a defined data model as they do not require any manipulation until used.

Common business users without the knowledge of data analytics can use structured data as they give self-service access.

Handling and analyzing requires data science expertise, and only data engineers can handle unstructured data.

They are known as schema on write as they have a predefined format.

They are known as schema on reading as they are in their native format.

Structured data have their sources in GPS sensors, online applications, web server logs, etc.

Unstructured data have their source in email messages, chats, voice messages, PDF files, etc.

Customer relationship management, online booking, and accounting departments use structured data.

Data mining, predictive analysis, and chatbots use unstructured data.

Semi-Structured Data

The third category of data features both structured and unstructured data, known as semi-structured data. Semi-structured data does not fit into any pre-set parameters or organized structures in a relational database resembling unstructured data. Yet, they have markers or metadata that carry processed, analyzed, and structured information just like structured data.

The best example of semi-structured data is the pictures in smartphones. Every image or photo in a smartphone has unstructured data and structured details like time, location, and other related information. Semi-structured data can be seen in the form of JSON, CSV, and XML file formats.

Wrapping Up

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