Understanding and managing data is crucial for any business operating in today’s times. With modern data volumes growing by leaps and bounds, this task gets tougher. Statistics reveal that the total data creation and consumption is expected to triple between now and 2025. Another exciting piece of information? Around 80 percent of the data available is in an unstructured form. It means only 20 percent of the data available is structured. We will cover structured examples, along with unstructured ones.
Structured and unstructured data are the most common data groupings. The distinction between structured and unstructured data deeply influences businesses’ approach to their own data. It also impacts the utility value of data.
Efficient data identification and management are essential to staying relevant in a data-driven world.
Structured Vs. Unstructured Data
The most fundamental difference between structured and unstructured data is that.
- Structured data has a fixed field within a database, record, or file.
- Unstructured data is devoid of a specific field structure.
Businesses must know how to identify each kind of data, their nuances, and how to work with them.
Businesses must be able to understand the nuances of each data type. They also need to know how the storage and management process. They can use data specialists and leverage various tools and data strategies to manage multiple types of data efficiently and correctly.
Structured Data Definition and Examples
Structured data is the type of data that fits within a record or file and can be worked on directly. This is the type of data that business users work with directly.
Examples of structured data include data from Excel spreadsheets structured within the format of the table.
Structured data is easier to analyze, store, and process manually and automatically, hence preferred by businesses. The tools and languages used for working with structured data are simple and user-friendly. There are more examples of structured data below.
Unstructured Data Definition and Examples
Unstructured data does not fit within a consistent format or structure. They cannot be analyzed using conventional tools and methods. Also, unstructured data can take many forms.
- Video files
- Mp3 Audio files
- Social media posts
- Abstract or behavioral data
- Mobile data
Unstructured data can take many forms, so its management is a huge challenge. As it doesn’t follow any predefined model, data managers can’t organize it in a relational base.
Unstructured data converts into structured in most workflows through a process of structuring and transformation before finally ready for analysis.
Structured vs. Unstructured Data: The Differences
Some of the core differences between structured and unstructured data are in the following areas:
It is easy to store structured data at scale as it is designed into a format and hence takes less space. Storage of unstructured data poses a challenge as the storage function has to handle various formats.
They place structured data in relational databases. They can be quickly accessed from such databases. When scaled, they become data warehouses defined as repositories for high volumes of structured data. They are the final destination of the ETL or Extraction, Transformation, and Loading pipeline. The pipeline converts data into a structured form before its dispatched to the data warehouse.
Unstructured data generally stores in data lakes at scale. Data lakes are freeform repositories for storing data in the raw form. Data lakes use more raw storage space. However, they are more flexible than warehouses.
Structured data is easier to analyze using traditional tools and programs. Likewise, analyzing unstructured data is a more intensive process and involves the use of a specialized approach. Also, users almost always have to run the data through unique structuring tools and processes.
Can You Work With Unstructured Data?
Structured data is easy to analyze and manage via programs like Excel and Google sheets. Analysis of unstructured data is a more complicated process.
It is possible to work with unstructured data, although some form of structuring is desirable for the proper analysis of data. Technical specialists such as data engineers and data scientists can work directly with unstructured data because it involves knowledge of scalable data integration and integrity practices.
While some may process unstructured data for their own use, others may transform unstructured data for the benefit of non-technical users. Larger organizations have dedicated teams for managing unstructured data.
Tools for Analyzing Unstructured Data
As unstructured data continues to grow in volume and variety, the tools to handle the data are also undergoing major changes. Some of these tools are developed for specific data types. Technologies such as Machine learning and Artificial Intelligence -powered analytics have significantly enhanced the ability to work with unstructured data.
Businesses can extract better value from data when they can distinguish between unstructured and structured data, the repository needed for specific data types, the skillset required for better data management and analysis, and the tools needed to make that analysis effortless and quick. Businesses can also involve third-party data management and analysis experts to handle their unstructured and structured data efficiently. Now that you’ve seen examples of structured data and unstructured, you can decide which one your business needs.