Structured and Unstructured Data: A Simple Guide for Data Security

Organizations generate more data than ever before while introducing a new world of data-related challenges. Now, businesses across all industries must have a strong security posture to prevent data from falling into the wrong hands. 

However, navigating the difference between structured and unstructured data in the context of security remains a challenge. One study found that between 80-90% of data is unstructured information, which means most companies have a wealth of information that may be unprotected or inadequately protected.

Structured data is relational and easier to understand, while unstructured data is non-relational and can struggle to accurately identify, classify, and protect. To further complicate the matter, semi-structured data and mainframe data must also be protected.

So, how can organizations gain better control of their entire data estate to protect it adequately? We’ll be diving into these two primary data types, how they vary, and how they relate to data security. 

What is Structured Data?

Structured data is highly organized and easily made sense of by machine learning algorithms and humans alike. Structured data emerged from the creation of structured query language (SQL), a programming language used for managing structured data.

As such, an easy way to determine structured vs. unstructured data is to think about if it can easily fit in a table. Users can quickly input, search, and change structured data with ease, which has made it the go-to data type for decades.

Pros of Structured Data

Structured data has been the cornerstone of computing for decades, which means it has plenty of benefits and utility that are firmly established, including:

• Most tools can access it: Structured data can be accessed and understood by a wide variety of tools, and they don’t need to use AI to make sense of it. The relational nature of structured data makes it easy for simple programmatic queries to find, analyze, and manipulate data as necessary.
• Easily learned and used by business users: Business users don’t need to gain an in-depth understanding of structured data to be able to put it to use. A basic understanding of the relevant topic and proficiency in the chosen software is all that’s necessary.
• Ready for use by machine learning and AI algorithms: Structured data is primed and ready for training in advanced AI and ML algorithms. There’s no need for time-consuming pre-processing, provided everything is accurate.
• Easy to discover and classify: A critical aspect of data security is data discovery and classification. Structured data is relatively straightforward since most modern tools can make sense of it and reading the data is easier as there is structure around it.

Cons of Structured Data

What are the drawbacks of structured data? While it has plenty of benefits, it’s not perfect, with drawbacks such as:

• Limited usage: This data type has a predefined structure and can only be used for its intended purpose. While it can be highly useful in some use cases, its flexibility and usability are limited.
• Minimal storage options: Structured data must be stored in rigid schemas, such as a data warehouse. As a result, any changes in data requirements mean updating storage for all structured data, which can often be more expensive.

What is Unstructured Data?

Unstructured data, which you can consider qualitative data, cannot be processed and analyzed with conventional data analysis methodologies. Structured data does not have a predefined data model, which makes it harder to work with traditional tools.

The importance and prevalence of unstructured data is changing rapidly. Discovering, classifying, and protecting unstructured data is necessary to prevent breaches and maintain compliance.

Pros of Unstructured Data

Unstructured data can be text, social media posts, and other mobile activity that can’t be easily represented in a table. There are several benefits to this type of data, including:

• Native format: Unstructured data is stored in its native format, which means it’s adaptable to being stored in different formats depending on specific requirements. Then, data scientists or analysis tools only need to prepare the specific data they need to use.
• Fast accumulation rates: Collecting unstructured data can be done easily since there’s no need to predefine the data structure. Data capture can be streamlined to handle customer survey responses, reviews on eCommerce sites, and any other source without strict structural requirements.
• Data lake storage: Unlike structured data, unstructured data can be stored in data lakes. This type of storage is easy to scale, changes only necessary data, and is secure. While unstructured data often requires pre-processing before it can be used, cost-effective storage can help offset these downsides.

Cons of Unstructured Data

We’ve touched on some of the drawbacks of unstructured data. So, let’s dive deeper into some of these drawbacks, such as:

• Typically requires expertise: Due to its non-formatted nature, learning how to work with unstructured data typically requires specialized training. While specifically preparing unstructured data can be a benefit in many use cases, it’s also a drawback for business users who aren’t familiar with unstructured data.
• Limited (but growing) amount of analysis tools: Analyzing unstructured data requires specialized tools capable of making sense of it. However, this drawback is getting a little easier to handle; leading-edge AI platforms are more able to handle unstructured data.

A Brief Look at Semi-Structured and Mainframe Data

We’re focusing on structured and unstructured data, but it’s important to note that there are other types of data that don’t fit in these two overarching categories — and they still need to be protected.

Semi-structured data is a bridge of sorts between structured and unstructured data. Semi-structured data uses metadata to identify specific data characteristics and often comes in JSON or CSV files. 

Semi-structured data can be used with both structured and unstructured data to introduce the additional metadata functionality to data storage.

Mainframe data presents an even greater challenge as its native format, VSAM, isn’t easily accessed by most tools and can often be left out of data security. Having security tools capable of accessing, discovering, and classifying VSAM data sets is mission-critical. Similarly, mainframe data is a treasure trove of insights that has generally held sensitive customer information for many years, which would be critical to data/AI projects.

Critical Data Security Challenges to Address

Data security isn’t optional — a single breach is costly and can cause permanent damage to your company’s reputation. Structured and unstructured data must be included in your data security posture management (DSPM) program; otherwise, your entire operating ability may be at risk. 

So, we’ll be breaking down key data security challenges and how you can address them to help protect the entire data estate, which are:

• Data classification: You need to know about all the data in your IT ecosystem. Classification follows data discovery and determines the specific data’s specific category. Categories can vary but often include public, internal, personal, and classified data. Data classification identifies the data, and its category determines how it should be protected.
• Access Control: Data that is too accessible is at a higher risk than tightly controlled access levels. Access control is often based on its specific category and can only be accessed by those who need it. Additionally, access should be logged, creating valuable audit trails that are critical for root cause analysis following an incident and filing compliance reports.
• Encryption: Encryption should be a standard practice to further protect data from bad actors even if they gain access to sensitive data. Encrypted data can only be viewed with the decryption key or a specific program, which means a bad actor can’t make sense of encrypted data without additional info. Data should be encrypted at rest, in transit, and when stored.
• Data Governance: Unstructured and structured data must be included in your overarching data governance policies. Data governance is concerned with data management, data protection, and compliance to ensure all data is properly identified and secured.

Team Up with 1touch for Effective Data Management

Structured and unstructured data both need to be discovered, classified, and protected to avoid a devastating data breach or cyber-attack. Alongside semi-structured and mainframe data, you can’t afford to leave any corner of your data estate in the dark.

Inventa by 1touch is an industry-leading data management tool that simplifies everything from discovery to classification. Our platform is ready to play a foundational role in your overarching security program. Inventa discovers new data throughout your IT ecosystem so every byte can be discovered and protected.

Looking for an effective data management and intelligence solution to protect your most valuable assets? Book a demo today to learn more about how Inventa can enhance your security and compliance initiatives.