It is a known fact that data analysis has advanced beyond what was first anticipated, and this is because of the quick advancement of technology, the production of more significant and larger amounts of data, and the aggressive application of quantitative analysis across a range of disciplines.
This advancement has allowed the use of different data that can be taken from different sources. But amidst the vast data presented, some of it is structured, and some unstructured. While working on data, one might get confused about which data is structured or unstructured; a common confusion is whether natural language is structured data or unstructured data?
A company’s performance is frequently determined by its capacity to obtain the appropriate data, analyze it, and take action in response to those insights. But because of the increase in the volume of data available to businesses and the kind of data available, it is more important than ever to understand the differences.
Types of data
Before one jumps into working with the data, it is essential to find out what data types are and how they are used.
A specified schema produces structured data, often arranged in a table format. Imagine a table with discrete values in each cell. The table header row used to define the value, each column’s format, and the data’s overall structure are all represented by the schema. The schema likewise imposes the restrictions required to make the data consistent, computable, and minable.
In a counterintuitive way, natural language is structured data, as it has symbolized meaning collected in organizational structures (grammar) that can be predicted, and thus, searched, mined, and manipulated.
Unstructured data may be found in various places, including social media posts, emails, blogs, and web pages. According to recent forecasts, unstructured data makes up more than 80% of all corporate data, and 95% of firms give unstructured data management a high priority.
The majority of unstructured data in the corporate sector is found in content that is readily available and relevant to customers. However, unstructured data is still extremely difficult to deal with. It cannot be processed and analyzed using conventional techniques and tools. Non-relational databases, sometimes called NoSQL, are one method for managing unstructured data.
A third type that lies in between the other two is semi-structured data. It is a kind of structured data that does not adhere to a relational database’s formal structure. However, even if it may not precisely fit the definition of structured data, it nonetheless uses labeling systems or recognizable markers to separate various pieces and enable search. This type is also known as data with a self-describing structure.
Photos taken on a smartphone are a classic example of semi-structured data. Every smartphone photo includes:
- Structured and unstructured visual material and its time of collection.
- Other identifying (and structured) data.
Data has become a vital part of every organization regardless of its industry. As there are different types of data: structured data, unstructured data, and semi-structured data, companies need to work rigorously to collect, organize, analyze and represent this data. This is why many firms have entered this market to help organizations collect data and also deliver insightful graph databases to companies. These companies can work on different databases with the help of prevalent software, allowing them to use the acquired information to facilitate better decision-making.