Differentiating Structured From Unstructured Data

 

 

The modern world revolves around data. As a matter of fact, this includes even business entities. Business entities throughout the calendar handle large chunks of data. The data handling must be in an organized manner or in an unorganized manner so that it can be well arranged. There are two types of data, i.e. Structured and Unstructured Data. In this article, let’s dive into their differences and why the classification is necessary..

 

Structured Data

Structured data complies with a data model, has a clearly defined structure, follows a consistent order, and is simple for a person or computer program to access and utilize. Typically, structured data is kept in databases or other places with clear schemas. Typically, it is tabular with well-defined headings for columns and rows in each of its properties. To manage structured data kept in databases, SQL (Structured Query language) is frequently utilized.

 

Characteristics of Structured Data

i) Data is structured clearly and complies with a data model.

ii) Rows and columns are the primary data storage formats.

iii) Data is well-organized so that its Definition, Format, and Meaning are all well understood.

iv) Within a record or file, data is stored in fixed fields.

v) Classes or relations are formed by grouping together similar things.

vi) The properties of entities in the same group are the same.

vii) Data is easily accessed and queried, making it accessible to other programmes.

viii) Addressable data pieces allow for quick analysis and processing.

 

Pros of Structured Data

i) Data can be indexed based on text strings as well as attributes since structured data has a well-defined structure that makes it easy to store and access data. This makes conducting searches simple.

ii) Data mining is simple, making it simple to extract knowledge from data.

iii) Operations like updating and deleting are simple since the data is well-structured.

iv) Operations involving business intelligence, such as data warehousing, are simple to carry out.

v) Easily scalable in the event of an increase in data.

vi) Data security is best ensured.

 

Cons of Structured Data

i) Use is constrained by a specific goal: Structured data has several advantages, including the ability to define data on-write, but it is also true that data with a preset structure can only be utilized for that purpose. This limits the use cases and flexibility of the system.

ii) Limited storage possibilities: Data warehouses are often where structured data is kept. Data warehouses are structured data storage solutions. Any change in requirements necessitates updating all of that structured data to fit the new criteria, which consumes a significant amount of time and resources. Utilizing a cloud-based data warehouse can reduce costs in part because it enables better scalability and eliminates the need for on-site equipment upkeep.

 

 

Some of the sources of Structured data are SQL, OLTP Systems, Excel sheets, and so on.

 

Unstructured Data

We’ve looked into what Structured Data is and its parameters. We’ll now dive into the concept of Unstructured Data, and its parameters.

Unstructured data is any data that does not adhere to a data model and has no obvious organization, making it difficult for computer programmes to use. Unstructured data is not well suited for a common relational database since it is not organized in a predefined way or does not have a predefined data model.

 

Characteristics of Unstructured Data

i) Data is unstructured and does not follow a data model.

ii) Rows and columns, as used in databases, cannot be used to store data.

iii) Data does not adhere to any rules or semantics.

iv) Data does not follow a specific format or order.

v) Data lacks a well-defined structure.

vi) The lack of a recognizable structure makes it difficult for computer programs to use.

 

Pros of Unstructured Data

i) It supports information that is not properly formatted or ordered.

ii) There is no fixed schema that restricts the data.

iii) Due to the lack of a schema, it is flexible.

iv) Data is scalable and portable.

v) It can manage the diversity of sources with ease.

vi) There are lots of business intelligence and analytics applications for this type of data.

 

Cons of Unstructured Data

i) Due to a lack of schema and organization, it is challenging to store and handle unstructured data.

ii) Due to the data's ambiguous structure and lack of pre-defined properties, indexing is challenging and error-prone. Search results are therefore not particularly accurate.

Data security is a challenging issue.

 

Some of the sources of Unstructured Data include pictures, web pages, videos, etc.

 

Conclusion

In this article, we have brought out the differences between Structured and Unstructured Data. Structured Query Language is used to extract data, and acts as a powerful tool for the same. Structured Query Language is a powerful backend tool. One has to be strong in SQL, to fetch a Back-end Developer role. Back-End developers are in huge demand by top product-based organizations nowadays. There are many institutes in our country that help candidates in upskilling themselves with Back-end skills. At SkillSlash, candidates are provided 1:1 mentorship. Skillslash also has in store, exclusive courses like Data Science Course In Delhi, Data science course in Nagpur and Data science course in Dubai to ensure aspirants of each domain have a great learning journey and a secure future in these fields. To find out how you can make a career in the IT and tech field with Skillslash, contact the student support team to know more about the course and institute.