Throughout the world, social media platforms generate data at all times. This has resulted in a proliferation of data, whether images, texts, audio, videos, or geo-locations.
Nowadays, businesses always face a similar challenge: they want to classify the data they collect to make it easier to analyze and use.
So, suppose you’re a business that wants to collect and analyze comments on social media. In that case, how should a common data source like social media comments be categorized? Keep scrolling for the details!
Table of contents
How Should A Common Data Source Like Social Media Comments Be Categorized?
By analyzing real-time sources of consumer data like social media chats, you’re better placed to judge whether certain products are making the impact you intended.
There are two main types of data available: structured & unstructured. These can be generated from multiple sources in virtually any genre, including an online website or a physical store.
In other words, social media produces both structured and unstructured data. Still, attempts to standardize social media assessment frequently rely on structured data.
The content of a social media post is unstructured data, but information about followers, groups, friends, or networks is structured data. Twitter data, for example, has enormous potential for revealing details about trends and news, as well as other important news and events.
On the other hand, we may extract information from interfaces with the social media platform’s APIs to use social media data. However, companies often face a series of issues called the “4 V”. These include velocity, variety, volume control, and veracity.
Velocity: The speed at which data is generated and the requirement for real-time analysis.
Variety: The vast array of types of data available.
Volume: The computer power and storage space needed.
Veracity: The requirement to validate all data quality.
What Is The Difference Between Structured & Unstructured Data?
Structured and unstructured data should not be confused with two sides of the same coin but rather a yin-and-yang relationship. The difference is determined by the amount of data knowledge required and whether on-write or on-read schema exists.
| Unstructured data | Structured data | |
| Who | Requires data science expertise | Self-service access |
| What | Many varied types conglomerated | Only select data types |
| Where | Commonly stored in data lakes | Commonly stored in data warehouses |
| When | Schema-on-read | Schema-on-write |
| How | Native format | Predefined format |
Structured data (also known as “hard” or “tabular” data) is typically found in spreadsheets that we’re all familiar with. There isn’t much room for interpretation because everything is clearly defined.
Unstructured data (also known as “soft” or “freeform” data), on the other hand, has a lot more gray area than structured data. These more free forms allow more flexibility and have anything from ease-of-use to security implications.
In addition, there are typically two storage places that keep data. Data warehouses store structured data, whereas an unstructured storage place is called a data lake. Structured data requires less storage space than unstructured data, but both are available in the cloud.
The last distinction may have the most influence on further activities. For example, the standard business user can utilize structured data, while unstructured data takes data science knowledge to acquire correct business insight.
Final Thoughts
In summary, that’s how should a common data source like social media comments be categorized. Again, both structured & unstructured data come from social media. Nevertheless, attempts to standardize social media assessment frequently rely on structured data.
