Structured vs. Unstructured Data: Key Differences

 Structured vs. Unstructured Data: Key Differences

Understanding, managing, and leveraging data is a crucial task for any modern business. The availability and volume of data have grown exponentially in recent years. This data poses a range of opportunities for savvy businesses. It also poses new challenges around data analytics and management for SMBs and enterprises alike. 

There are myriad types of data available. Structured and unstructured data are the two most common groupings of data. The distinction between structured and unstructured data heavily impacts how businesses approach their own data.

The difference between structured and unstructured data is simple. Structured data has a fixed field within a file, record or database. Unstructured data does not follow a particular field structure. This difference has implications for how businesses collect, store, and analyze their data.

Businesses and stakeholders should know how to identify and work with each kind of data. Businesses must be able to understand the nuances of each data type. They also need to know how to store and manage the data. There are also specialized skill sets are helpful or simply necessary to do so. There are also a range of tools available to help businesses throughout this process.

What Is Structured Data?
Structured data is data which fits within a fixed field, such as a table, within a record or file. Structured data is most data that business users work with directly. For instance, any data found in Excel spreadsheets has been structured within the table format. 

Business users work with structured data more frequently because it is easier to analyze. Users can store and process the data automatically or manually. The tools and languages for working with structured data are also easier. For instance, structured data is stored in relational databases (RDBMS). These databases allow business users to use a Structured Query Language (SQL) to get data. 

There are many advantages to working with structured data. However, data is rarely created in a prestructured format like a table. Most data starts in an unstructured format. 

What Is Unstructured Data?
Unstructured data is that which not fit within a consistent structure or format. It is usually categorized as qualitative data, such as natural language text. Users cannot analyze unstructured data with conventional data tools and methods. This poses a big problem– 80% or more of enterprise data is unstructured. 

Unstructured data can take many forms. Examples of unstructured data include:

Video files
Audio files (i.e. mp3)
Social media posts
abstract data, i.e. behavioral data
Mobile data
A big component of unstructured data’s challenge is the variety of forms it takes. It follows no predefined data model. This means that businesses can’t organize it in a relational database.

Unstructured data does not mean that the data cannot be structured. It just means that no one has transformed the data into a structured format yet. In most workflows, unstructured data goes through a structuring/transforming process before analysis.

Bonus: What Is Semistructured Data?
There are some data types that fall in between structured and unstructured data. This “semistructured” data is technically structured data. However, it does not fit into the formal structure of a relational database. Examples of semi-structured data formats include JSON, XML, and CSV filetypes. 

Semistructured data doesn’t have a specific tabular data model. It does include more tools to assist with analysis than unstructured data. Common semistructured tools include tags and semantic elements. Analysts and data scientists can use these indicators to format the data into a dataset.

Structured Data Unstructured Data
Definition Data That Fits Into a Table or Other Fixed Field Data That Does Not Fit Into a Fixed Field or Consistent Structure
Examples Quantitative Data, Categorized Data Images, Audio files, Natural language/text, Social Media Content, Digital Behavior Data
Storage Relational Database Management System (RDBMS), Data Warehouse NoSQL databases, Data lakes
Analysis Conventional Methods and Tools, Excel, Google Sheets, Artificial Intelligence Specialized tools, Natural language, Processing (NLP)Text Mining, Some manual analysis
Users Business Professionals, Data Analysts Data Scientists, Data Engineers
Structured vs. Unstructured Data: 4 Key Differences
There are some core definitional differences to highlight between structured and unstructured data. Structured data fits into rows and columns, making it easy to access in relational databases. In contrast, unstructured data does not have a predefined data model to follow. 

The clearest distinction between structured/unstructured data is quantitative and qualitative data, respectively. Quantitative data consists of numbers or countable values. This makes quantitative data easy to structure. Qualitative data is most other forms of data, such as open text. There is much more variety in the format and orientation of qualitative data. This makes it impossible to analyze via conventional methods. 

These differences impact how businesses store and analyze structured and unstructured data. 

1. Storing Structured and Unstructured Data
Structured data is easier to store at scale than unstructured data, in most cases. It takes up less storage space because it is prestructured to a particular format. In contrast, unstructured data requires the storage function to handle a wider variety of formats. 

As mentioned above, structured data resides in relational databases. At smaller scales, these databases can be accessible and even free in some cases. At scale, these databases become data warehouses. Data warehouses are high-volume, long term repositories for structured data. They are usually the endpoint for the Extraction, Transformation, and Loading (ETL) pipeline. This pipeline gets data into a structured format before sending it to the data warehouse. Cloud data warehouses have also become more accessible in recent years. 

Unstructured data is usually stored at scale in data lakes. Data lakes are a more freeform repository that stores data in its original format or with minor “cleaning.” Data lakes use more raw storage space, but have much more flexibility than warehouses.


Previous Post Next Post