Structured Data Vs Unstructured Data: Which One is Better for Your Business?
Data is the driving force of any organization. It can be of any shape and size, but it is generally classified into Structured Data and Unstructured Data. According to research, unstructured data constitutes over 80 per cent of enterprise data. Structured data is of much less amount for business use. Let us understand both these types in details, so that we can harness them effectively.
Structured Vs Unstructured Data: Definition
Structured data can be defined as the formatted data that fit a pre-defined structure before loading in data storage. On the other hand, Unstructured data is data that does not follow this rigid structure. This type of data is unprocessed and stored in its raw format without any pre-set rules.
Structured data is usually stored in relational databases, where the data consists of fields. These fields are distinctive such as home addresses, credit card numbers, and transactions. Due to the presence of these fields, you can easily search and segment records.
On the other hand, unstructured data are available in various formats like images, social media posts, emails and IM messages. JSON and Excel are also considered into the unstructured data category due to the lack of pre-defined structure. Unstructured data are mainly found in applications, NoSQL databases, data lakes and data warehouses.
In simple words, structured data is absolute, but unstructured data is objective. Structured data generally contain numbers or objective facts like a mail id, whereas unstructured data will be made up of objective text messages.
Structured Vs Unstructured Data: Pros & Cons
These two types of data have their advantages and disadvantages across your architecture and business. Here’s a breakdown of everything you need to consider:
Structured data: Pros
- Easy to understand – The absolute nature of structured data enables universal users having any skill level to understand and analyze different relationships of the data.
- Transferable – Most of the data tools available in the market only have structured data, making it easier for you to analyze.
- Easy for data programs – Various programs or machine learning algorithms easily crawl structured data. So, it promotes simplified data querying and manipulation.
- Less Storage – Organizations store data in structures to save the required storage space.
Structured data: Cons
- Inflexible Storage – Structured data is generally stored in relational databases or data warehouse, both have very rigid structures. If you need to edit your records, you will have to update all of the structured data.
- Limited Uses – Structured data with pre-set rules can only be used for its intended goal, limiting several business use-cases.
Unstructured data: Pros
- Indefinite Use – As unstructured data do not contain any predefined rules, it can be used for more than one intended purpose.
- Flexible formatting – You can store Unstructured data in a variety of formats.
- Cheaper Storage Cost – Modern data demands with the digital revolution’s onset makes the storage for this unstructured data easier and less expensive.
- Unlimited insights – An enterprise has more unstructured data than a structured one. So, even though it is difficult to analyze Unstructured Data, this data can result in more insights that could amplify your business competitiveness.
Unstructured data: Cons
- Difficult to Analyze – It is only possible for an experienced team of data scientists and data analysts to harness unstructured data. Most of the businesses will not be able to access their raw format or draw value from it.
- Data tools required – Most data tools, like Excel, are not designed to handle unstructured data. As a result, your business will have to opt for a specific data management tool to analyze the data.
Both Structured and Unstructured Data have their advantages and drawbacks. But it is a common observation that structured data is easier to handle and analyze. Unstructured data being more in quantity is difficult as it comprises a ton of competitive insights.
If your organization wants to handle and draw value from each data type, it needs proper guidance from seasoned Data Analysts.