What is structured data?

Structured data is data that has been predefined and formatted to a set structure before being placed in data storage, which is often referred to as schema-on-write. Structured data is generally tabular data that is represented by columns and rows in a database.

• Databases that hold tables in this form are called relational databases.
• The mathematical term “relation” specifies a formed set of data held as a table.
• In structured data, all rows in a table have the same set of columns.
• SQL (Structured Query Language) programming language used for structured data.

Pros of structured data

There are three key benefits of structured data: 

• Easily used by machine learning algorithms: The largest benefit of structured data is how easily it can be used by machine learning. The specific and organized nature of structured data allows for easy manipulation and querying of that data. 
• Easily used by business users: Another benefit of structured data is that it can be used by an average business user with an understanding of the topic to which the data relates. There is no need to have an in-depth understanding of various different types of data or the relationships of that data. It opens up self-service data access to the business user.
• Increased access to more tools: Structured data also has the benefit of having been in use for far longer, as historically it was the only option. This means that there are more tools that have been tried and tested in using and analyzing structured data. Data managers have more product choices when using structured data.

Cons of structured data

The cons of structured data are centered in a lack of data flexibility. Here are some potential drawbacks to structured data’s use:  

• A predefined purpose limits use: While on-write-schema data definition is a large benefit to structured data,  it is also true that data with a predefined structure can only be used for its intended purpose. This limits its flexibility and use cases.  
• Limited storage options: Structured data is generally stored in data warehouses. Data warehouses are data storage systems with rigid schemas. Any change in requirements means updating all of that structured data to meet the new needs; this results in massive expenditure of resources and time. Some of the cost can be mitigated by using a cloud-based data warehouse, as this allows for greater scalability and eliminates the maintenance expenses generated by having equipment on-premises.

What is unstructured data?

Unstructured data is data stored in its native format and not processed until it is used, which is known as schema-on-read. 

• Unstructured data is information that either does not organize in a pre-defined manner or does not have a predefined data model.
• Unstructured information is a set of text-heavy but may contain data such as numbers, dates, and facts as well.
• Videos, audio, and binary data files might not have a specific structure. They’re assigned as unstructured data.

Pros of unstructured data

As there are pros and cons of structured data, unstructured data also has strengths and weaknesses for specific business needs. Some of its benefits include:

• Freedom of the native format:  Because unstructured data is stored in its native format, the data is not defined until it is needed. This leads to a larger pool of use cases, because the purpose of the data is adaptable. It allows us  to prepare and analyze only the data needed. 

The native format also allows for a wider variety of file formats in the database, because the data that can be stored is not restricted by a specific format. That means the company has more data to draw from.

• Faster accumulation rates: Another benefit of unstructured data is in data accumulation rates. There is no need to predefine the data, which means it can be collected quickly and easily. 
• Data lake storage: Unstructured data is often stored in cloud data lakes, which allow for massive storage. Cloud data lakes also allow for pay-as-you-use storage pricing, which helps cut costs and allows for easy scalability.

Cons of unstructured data

There are also cons to using unstructured data. It requires specific expertise and specialized tools in order to be used to its fullest potential.

• Requires data science expertise: The largest drawback to unstructured data is that data science expertise is required to prepare and analyze the data. A standard business user cannot use unstructured data as it is, due to its undefined/non-formatted nature. Using unstructured data requires understanding the topic or area of the data, but also of understanding how the data can be related to make it useful.
• Specialized tools: In addition to the required expertise, unstructured data requires specialized tools to manipulate. Standards  are intended for use with structured data, which leaves a data manager with limited choices in products for unstructured data, some of which are still in their infancy.

Structured vs Unstructured data

References:

https://lawtomated.com/structured-data-vs-unstructured-data-what-are-they-and-why-care/