Data is fundamental for any business and is available in a variety of formats. Let’s understand the role of these different formats along with their differences. 

What is Data and Big Data?

Data is essentially a distinct piece of information that is gathered and translated for a specific or general business purpose. Data is available in different forms such as bits and bytes and are stored in electronic memory, numbers, or texts on pieces of paper. 

Big data, on the other hand, refers to the data which are very large in size. For instance, the normal data size is MB which is the size of Word documents or Excel spreadsheets, or maximum GB which are the sizes of movies and codes. However, data in Petabytes, which is 10^15 in size is known as Big Data. 

Almost 90% of today’s data generated in the past 3 years are big data. Large organizations, telecom companies, e-commerce sites, and many more companies use big data. The three different formats of big data are structured, unstructured, and semi-structured data. Therefore, let’s understand the structured and unstructured data differences along with their examples.

What is Structured and Unstructured Data?

Structured Data

Structured data is the data that has been predefined and formatted to a set structured before it is placed in data storage, also referred to as schema-on-write. It is to the point, factual, and highly organized, making it easily understandable for machine languages. 

Structured data is qualitative in nature, therefore, it is related to the quantities which means it contains measurable numerical values like dates, numbers, and times. You can easily search and analyze structured data because of its predefined format. 

A relational database is one of the best examples of structured data. Here, data is formatted into defined fields such as rows and columns like that of Excel files and Google Docs spreadsheets.

The programming language SQL (structured query language) is used for the management of structured data. Developed by IBM in the 1970s, it is used to handle relational databases and warehouses. 

Benefits and Limitations of Structured Data

The benefits and potential drawbacks of structured data are as follows:

Benefits:

  • Machine Learning Algorithms Usage

The biggest advantage of structured data is that it can be easily used by machine learning (ML). This is because structured data is specific and organized, allowing for easy manipulation and querying of the data. 

  • Business Users Usage

Another great benefit of structured data is that it can be used by any average business user who understands this form of data. Even if the user does not possess an in-depth knowledge of various data types or relations of the data, they would be easily able to use it. This allows users to get the data they need by themselves. 

  • Increased Access to More Tools
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Structured data has been in use for a long time and therefore, offers more tools that are tried and tested. This gives data managers a wider range of options when it comes to using and analyzing structured data. 

Limitations:

  •  Limits Users

The main limitation of structured data is because of its inflexibility. Its fixed structure limits users on how the data can be used. This form of data is only meant to be used for specific purposes which limits its range and flexibility.

  • Limited Storage Options

Structured data are generally stored in data warehouses which are data storage systems with rigid schemas. If there is any change in the data requirement, it would update all the structured data to meet the new business needs. The outcome of this would lead to a huge expenditure of time and money. 

You can mitigate the cost by opting for a cloud-based data warehouse. It would allow greater scalability and eliminate the maintenance costs generated by having equipment on-premises. 

Structured Data Examples

Structured data is one of the common forms of data due to which it is present almost everywhere. They form the basis of inventory control systems and ATMs and can be human or machine-generated.

Some of the common examples of machine-generated structured data are airline reservation systems, and sales transactions while human-generated structured data includes spreadsheets. 

Unstructured Data

Unstructured data is the data stored in its native format. It is not processed until used, which is known as schema-on-read. The amount of this type of data is available more when compared to structured or semi-structured data. 

All types of unstructured file formats like log files, image and audio files, emails, social media posts, satellite imagery, presentations, and chats are unstructured data. 

Because of the nature of the unstructured data, it cannot be presented in a data model or schema. This makes it difficult to manage, analyze, or search for unstructured data. This form of data is essentially qualitative in nature and sometimes stored in non-relational databases or NoSQL.

Since unstructured data is not stored in relational databases, it is hard for humans as well as computers to interpret it, limiting its usage to experts like data scientists, and cannot be manipulated without specialized tools. 

Benefits and Limitations of Unstructured Data

The benefits and potential drawbacks of unstructured data are as follows:

Benefits:

  • Flexible Format

A significant advantage of unstructured data is that since it is available in native format, it allows the flexibility for it to be adapted as required. This increases its use cases and allows for a wider variety of file formats in the database as data can be stored in any format. Therefore, this allows a company with more data to draw from.

  • Quick Accumulation 
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Unstructured data is not predefined. This makes the process of data collection faster and easier. 

  • Better Scalability and Pricing

This type of data is often stored in cloud data lakes which have massive storage capacity. This allows for pay-as-you-use storage pricing, helping you cut costs and improve with easy scalability.

Limitations:

  • Expertise Required

One of the biggest limitations of unstructured data is that it can be only used by experts with knowledge of how to use this data type. For instance, data scientists and engineers are the ones who can prepare and analyze unstructured data.

A standard business user would not be able to use unstructured data as it is because of its undefined format. If you want to use this form of data, you would need a proper understanding of the topic or area of the data and how the data can be related so that it’s useful. 

  • Specialized Tools Needed

Apart from professional expertise, you would require specialized tools to manipulate unstructured data. Unlike structured data which can be used with standardized tools, a data manager has limited choice in products to analyze this data type.

Unstructured Data Examples

Since unstructured data is qualitative in nature, therefore it is more characteristic and categorical. By using this form of data, you can uncover the potential of sales trends through social media or identify the effectiveness of a marketing campaign. 

Human-generated unstructured data are text files, emails, business applications, and mobile data, among others whereas machine-generated unstructured data are scientific data, senator data, digital surveillance, etc. 

Unstructured data can be used to detect patterns in chats or even track suspicious email trends, making it very useful to organizations in assisting them with policy compliance. 

Difference Between Structured and Unstructured Data

If you want to compare structured and unstructured data, you can do so by analyzing the type of data used for each, the level of expertise required to use the data, and the on-write versus on-read schema. 

Structured Data Unstructured Data
Nature It is quantitative, i.e., consists of hard numbers of things that can be counted It is qualitative and cannot be processed or analyzed using conventional tools
Format Has a predefined format Has different formats and comes in a variety of shapes and sizes
Technology Based on the relational database Based on the non-relational database 
Flexibility It is schema-dependent and, therefore is less flexible Schema is absent in unstructured data, making it more flexible
Robustness Very robust Less robust
Scalability It is hard to scale database schema It is more scalable
Performance Structured data performs well as it allows for structured queries, meaning that advanced combining of data can be done which makes it faster and more efficient Unstructured data has lower performance than structured data and semi-structured as it allows for only textual queries that lack organization, making it tough to search through quickly
Analysis Easy to search Quite difficult to search
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Therefore, structured data is more organized and follows a set pattern like numbers and texts in standardized formats like CSV or XML. It can be easily processed and stirred in databases with labels, making it easy to search and analyze using readily available tools. 

On the contrary, unstructured data is available in a variety of formats like DOC or MP3, thereby lacking a predefined model. It is not as straightforward and is stored in raw formats or NoSQL databases like data lakes. This makes it more complex where you have to break down, group, and extract patterns in order to understand the data.

If you consider the future of your data, structured data can be easily processed for relational databases and analytics, whereas unstructured data needs to be handled intricately before fitting into those structures. So, to use both types of big data, you need to bridge the gap so that you can make the most out of them and gain relevant insights to improve your business functioning. 

What is Semi-Structured Data?

Sem-structured data is the data that can be defined as the middle ground between structured and unstructured data. Although it is mostly similar to unstructured data, it includes metadata which provides distinctive characteristics.

Because of the extra information in the form of metadata, you can search, organize, and analyze it better than unstructured data.

For instance, a file containing customer details in tabs is semi-structured whereas a CRM database is completely structured. Semi-structured data is, therefore, more organized and has a hierarchy than unstructured data. It is more like a file with customer information compared to just comments on a social media post. 

Conclusion 

Thus, structured data offers organized and easily searchable information whereas unstructured data is less organized, however, allows for different data types and provides flexibility in handling different data formats. Both types of big data have their own sets of advantages and limitations. When used as a combination, these data types may offer comprehensive insights and solutions in today’s data-driven landscape.