Companies use data that is present in two main formats: Structured Data and Unstructured Data. Both formats are very different when it comes to storage and access. Structured data as the name suggests is more organized and easier to access, whereas unstructured data is spread across various systems, present in various formats. This results in complexities while accessing data and converting it.
Data protection deals with the protection of sensitive data of a company and avoiding it getting corrupted or rendered unusable. It also allows restoring the data in case something unfortunate happens. The data getting compromised or lost is one of the factors that companies try to avoid and are always anxious about it. Since the features, factors, and structures of both the variety of data are different, Data protection also applies differently to both variants and requires additional steps to protect them.
This article tries to give a guide on the differences between Structured vs Unstructured Data and also provides steps to protect data.
What is Structured Data?
Structured data is properly arranged and easily understandable data that can be accessed efficiently using a structured query language (SQL). SQL is the programming language that is used to access and modify structured data efficiently. A relational database is an example of structured data that business users can quickly input, search and manipulate structured data.
Efficiently used by ML algorithms:
The querying of ML data is made efficient by the specific and organized structure of the data.
Made For business users:
Structured data doesn’t require a deep understanding of different data types and their capabilities. By understanding the basics of the subject matter related to data, users can easily access and interpret the data.
More accessible Tools:
Structured data precedes unstructured data, so you can use more tools to use and analyze structured data.
Limited use cases:
Data that is organized and structured predominantly, can only be used for the specific tasks it was intended for which limits its flexibility.
Specific Storage Required:
Structured data can be stored only in systems with rigid schemas such as Databases and Data warehouses. Therefore in case of changes that need to be made it necessitates an update of all the structured data present in the system, which requires efforts, resources, and time.
What is Unstructured Data?
Unstructured data is data that is not organized and is not present in fixed formats. It cannot be accessed, modified, or for even that matter retrieved using conventional data tools and methods. Since unstructured data doesn’t have a fixed structure and do not follow particular formatting, it can be stored efficiently only in non-relational (NoSQL) databases. Data lakes are another option that can be used to store unstructured data without losing its raw nature.
Recent times have seen an increase in understanding the importance of Unstructured Data. Studies show that over 80% of enterprise data is unstructured and now firms are moving towards Unstructured Data Management.
When the Unstructured data is stored in its raw form, it remains unchanged until required. Since the raw data is adaptable to various formats, it increases the data pool and allows data scientists to analyze and prepare the data based on their requirements.
Faster Acquisition of Data:
Since unstructured data doesn’t require to be present in a predefined format hence can be collected quickly.
Data Lake Storage:
Since Data Lakes are used to store unstructured data, itAllows for massive storage and pay-as-you-use pricing, which cuts costs and eases scalability.
Since the data is raw and unformatted, proper data science expertise is required for the preparation of unstructured data. This causes inexperienced people to not be able to understand the concepts and utilize the data to its full potential.
since the unstructured data cannot be modified by conventional methods it requires specialized tools. This reduces the choices for data managers.
What are the key Differences Between Structured VS Unstructured Data?
While structured data gives a comprehensive guide on customer data, unstructured data provides a deeper understanding of customer behavior and intent. Some key differences between Structured vs Unstructured Data.
1. Structured vs Unstructured Data: Sources
Structured data comes from GPS sensors, online forms, network logs, and web server logs. Unstructured data sources include email messages, word processing documents, PDF files, and more.
2. Structured vs Unstructured Data: Forms
Structured data consists of numbers and values, and unstructured data consists of sensors, text files, audio and video files, and so on.
Structured vs Unstructured Data: Models
Structured data has a predefined data model that is formatted into the specified data structure before it is placed in the data store (for example, schema on write). Unstructured data, on the other hand, is stored in native format and is only processed when used (eg .schema-on-read).
. Structured vs Unstructured Data: Storage
Structured data is stored in tabular formats that require less storage space (such as Excel spreadsheets and SQL databases). It can be stored in a data warehouse, making it highly extensible. Unstructured data, on the other hand, is stored as a media file or NoSQL database that occupies more space. Scaling is difficult because it can be stored in the data lake.
. Structured vs Unstructured: Uses
structured data is used in machine learning (ML) to drive its algorithms. Unstructured data is used in natural language processing (NLP) and text mining.
What is Data Transformation?
Data transformation is the process of converting Unstructured Data to Structured Data. Data Transformation can be done using manual methods or even automated ones. This is done mostly by data scientists so as to convert the raw data that is present in undefined structure to a format of their choice to be able to apply useful algorithms to them. Data Transformation helps in Reducing the clutter. Since Data protection works more efficiently on Structured data since it is organized, Converting the raw and unstructured data to a structured format helps in achieving efficiency.
Benefits of Data Transformation:
- High-Quality Data: All the imperfections, duplications, unordered format, and many more can be taken care of by transforming data. The quality of data is validated after data transformation and this is readily available for analysis.
- Better usability: Since the data in Unstructured data is present in large quantities and organizing them into format reduces the storage requirements. This also allows for faster analytics and access.
- Reduced effort: If the Data transformations were done using automated methods this helps in reducing the effort and will, in turn, reduce expenses. This will improve overall efficiency.
- Automated ingestion of data: Since the data is collected from various sources like webpages, search engines, raw data sources, etc. data transformation allows for storing them in the same location for faster access and also ensure data is consistent and readily available.
Data Protection – Three Steps:
Data protection is a three-step process. Each step has its own importance. Regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) outline expectations for handling personally identifiable information (PII). Compliance and data protection are the goals for both structured and unstructured data, but the tactics followed for both variations are quite different.
Step 1: you cannot protect the data that is unrecognizable.
personally identifiable information (PII) protection starts with the discovery of PII. for Structured Data, the PII discovery is more of a one-time task as the collection of structured data stored in the company’s database in an ordered format. For unstructured data, PII discovery is usually a continuous process. Therefore, discovery is a step that can’t be skipped.
Let us understand why PII for unstructured data is quite hard. Usually, an organization deals with about 10M files from all the departments from marketing to customer interaction and many more. Since there is a variety of data this task becomes one of the most difficult data security challenges.
PII for structured data is also difficult, Database designs and regulations are the main obstacles. Modern privacy regulation keeps privacy in mind and hence the sensitive content is usually scattered across various platforms. Sometimes the data is also duplicated to protect it from loss and PII becomes a much more difficult task.
Automated PII discovery is useful in helping the professional to determine that the PII found is the one they need to protect. Recent trends in Artificial Intelligence have shown some signs of promise in automating data-discovery tasks for both types of data.
Step 2: Assessing the data you found
Performing a complete assessment of who can access the PII is the first step to understanding the risks associated with it. The risk associated with both structured and unstructured data is different and even the methods to assess them are different. Some parameters that can help in evaluating PII access are mentioned below:
- Connecting a handful of accounts to the large-scale database supporting web apps and e-commerce helps in tracking the access.
- Usually, API connections extend access outside, and increasing the connections puts data at risk. Hence these connections need to be supervised.
- PII usually moves from structured to unstructured whenever reports of data from the database are generated. This is one of the most overlooked points of data exposure.
Managing and accessing unstructured data is far more difficult. If step 1 is properly followed then finding the document with PII is easier to find and determine. After the discovery the risk assessment is manageable. The following risk Indicators can be looked at after you find PII:
- Any data sharing that is inappropriate to the context through external means and personal emails.
- Sharing of unprotected and non-expiring links that raise a red flag.
- Any file that is present in the place it is not supposed to be.
- Any unclassified files that may not be in the jurisdiction of data loss prevention systems.
Recent innovations in AI helps in establishing access control for your end user’s files.
Step 3: Protect The assessed data
Since the process to be followed for Both Structured and unstructured data is quite different we will talk about them separately.
Structured Data Risk Mitigation:
- Modify the data in such a way that it is easier for further people to understand and perform PII in the future. Also, refactor the database so as to avoid any duplication and simplify all the data structures used so the PII is efficient.
- Perform Encryption on sensitive information so as to add a layer of security on top of access control.
- A major issue of PII is finding data that is not needed, data that is old and obsolete. Hence deleting the data that is redundant or not required is a good practice.
- Understand the concepts of API data access and then do proper implementation so that there is no loss of data due to poor API design.
Unstructured data Risk mitigation:
- Usage of access control that provides the least privileges to all business-critical data at file level since folder-level is not sufficient.
- A one-time audit of newly created files is not sufficient, instead continuous monitoring provides a safe environment as there are thousands of files created through the course of time.
- PII risk management can be combined with the complete security stack.
- The communication regarding the threats should be validated and only be communicated when there is a very high-risk factor involved. Continuous messaging will create alert fatigue and will defeat the purpose.
This blog introduced and explained in detail the types of data that companies use along with each type’s pros and cons. It stated the difficulties faced by using both types of data and to overcome some of them. Also, risk Management was discussed in detail and the difference in methodology, as well as difference in perceiving the data, was discussed.