Structured Data vs. Unstructured Data

If you’re looking to analyze, store, or secure data, you’ll first need to determine whether it is structured or unstructured data. Each kind of data comes with its own challenges, but if properly utilized, it can provide valuable insights for your company. 

What is Structured Data?  

Structured data, or quantitative data, is data that is easily defined and fits into organized fields. It usually consists of hard numbers or other variables that can be classified and sorted. 

Because structured data is easily defined and organized, it is easily understood by machine learning models. It requires less storage space than unstructured data and is usually stored in a data warehouse.  

What is Unstructured Data?  

Unstructured data, or qualitative data, has no predefined structure and can’t be processed in the same way as structured data. Unstructured data cannot fit into predefined fields and is hard to extract insights from in large volumes. Unstructured data comes in all different file types, from word documents to images.  

Because unstructured data is usually stored in its native format, it makes unstructured data management a bit more unruly. This native format makes it hard to analyze, data mine, search, and store. As unstructured data does not have a predefined data model, it is often stored in data lakes to preserve its raw form.   
 

Structured vs Unstructured Data Examples 

Examples of structured data include names, addresses, ZIP codes, Social Security numbers, and credit card numbers. While this data is easier to organize, it typically consists of sensitive data that needs to be secured.  

Examples of unstructured data include email, word documents, PDFs, social media posts, presentations, chats, videos, audio, and image files. While these data types are easy to store in their raw form, they can be hard to store securely due to their unstructured nature.  

Differences between Structured and Unstructured Data 

Analyzing & Storing Structured Data

When stored correctly, structured data is easy for machine learning algorithms to read and analyze. Structured data should be stored in a relational database which will format structured data to be navigated with SQL (Structured Query Language). Once properly stored, this data is easy to input, sort, manipulate, and analyze.  

Analyzing & Storing Unstructured Data  

Unstructured data is usually stored in its original state as it can be very hard to manipulate to extract insights. Unstructured data will likely be stored as filed in a data lake, instead of deconstructed like structured data. Analyzing unstructured data usually requires advanced analytical tools or an analyst with extensive technical expertise. This data can be valuable, but expensive for many companies to access, in contrast with the ease of analysis for structured data.   

Securing Structured Data  

Since structured data is often sensitive data (like credit card information) it’s important to find a way to secure it when not in use. Tokenization is the best solution for sensitive structured data. The tokenization process replaces data with a token that can retain important information, like the last four digits of a card number. The original data is then stored away from malicious actors.

Because the tokens don’t hold all the sensitive information, they’re rendered useless if stolen since the real information is stored securely. This is preferable to encryption, which scrambles the information until it is accessed with a key. Keys, while useful, can also potentially also be stolen in a data breach making encryption a less secure option. 

Securing Unstructured Data 

While tokenization works well to secure structured data while retaining identifiable portions, encryption may work better to secure unstructured data. Since unstructured data doesn’t fit into predefined fields or needs specific portions to remain unscrambled, encryption should be able to secure these files.  

Pros and Cons of Structured vs Unstructured Data 

Pros of Structured Data  

• Structured data is easy to analyze with machine learning or AI algorithms   
• More tools are available for structured data analysis  
• Because of its simplicity, it’s less expensive to analyze structured data  
• Structured data can also be analyzed by any individual with a basic understanding of the topic the data relates to  

Cons of Structured Data  

• Structured data is hard to restructure once organized into a predefined structure  
• Structured data has limited storage options since most structured data storage has strict schemas, which can be hard to reorganize or scale  

Pros of Unstructured Data  

• Unstructured data uses its native format, which means the data can be used in multiple different cases without any reformatting  
• There is a wider number of file formats able to be stored with unstructured data  
• Data can be collected and stored quickly and easily  
• Storage in data lakes is easily scalable  

Cons of Unstructured Data  

• Data science experts are needed to analyze large amounts of unstructured data  
• It’s hard to manipulate unstructured data to analyze en masse without specialized tools  
• Because of the specialized tools and talents needed to analyze unstructured data, it can often be more expensive to analyze unstructured data  

What is Semi-Structured Data?  

If there’s a type of data that you’re having a hard time categorizing, it’s possible you’re dealing with semi-structured data. Semi-structured data shares many characteristics of unstructured data. It is typically more complex than structured data and faces many of the same storage challenges as unstructured data. However, it shares similarities with structured data as it is easier to search and employs tagging systems that can sort different elements.  

Semi structured data examples include CSV, JSON, and XML files.  

Structured data is solidly defined, organizable, and can be easily analyzed by machine learning models. Semi-Structured data resembles structured data but lives in files like CSVs. Unstructured data is easy to store and often exists in its native format, although it is hard to analyze efficiently. Whatever data you’re dealing with, it will be easier to analyze, store, and secure if you understand what category it falls into.   

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