A lot of unstructured & structured data is expanding rapidly in double digits. We can’t say how much of it is structured or unstructured, but we can say that it’s experiencing exponential growth. Unstructured data accounts for 80-90 percent of the world’s data, according to venturebeat.com. According to the most recent ITC data, the volume of unstructured data is expected to increase from 33 zettabytes in 2018 to 175 zettabytes, or 175 billion terabytes, by 2025.
Data is essentially “the plain facts and statistics gathered during the course of a business’s operations”. They can be used to measure/record a wide range of internal and external business activities. While the data itself may not be very informative, it serves as the foundation for all reporting and is therefore critical in business. Many BI Tools including Tableau, Power BI helps in this regard. This crucial part of BI comes in a wide range of formats, from strictly formed relational databases to your most recent Facebook post.
According to Krishna Subramanian, president and COO of Komprise, a provider of data management software,
“If you believe your data is growing at a rate of 30%, it is actually growing at a rate of 90-100 percent when all documents and records are taken into account. It’s also worth noting that cloud storage isn’t always less expensive. AWS, for example, now provides over 16 thresholds of unstructured file and element disk space. If you wouldn’t put your data in a proper place and monitor egress expenses, you may have to pay more than if you kept it on-premises because you’ll be charged every time”.
According to this Data Science Course tutorial all of that data, in all of its various formats, can be divided into two categories: structured data and unstructured data.
What is Structured Data?
Structured data is data that is stored in a fixed field within a file or record. A relational database is typically used to store structured data (RDBMS). It can be made up of numbers and text, and it can be sourced automatically or manually as long as it is within an RDBMS structure. It is dependent on the development of a data model that specifies what types of data to include and how to store and process them.
SQL is the programming language used for structured data (Structured Query Language). SQL is a relational database management system developed by IBM in the 1970s. Names, addresses, credit card numbers, geolocation, and other types of structured data are common examples.
What is unstructured data?
Unstructured data refers to all data that is not structured. Unstructured data may have a local, internal structure, but it is not structured in a predefined manner. There is no data structure; data is stored in its original format. Rich media, text, social media activity, surveillance imagery, and other types of unstructured data are common examples.
The amount of unstructured data is significantly greater than the amount of structured data. Unstructured data accounts for 80 percent or more of all enterprise data, and the percentage is increasing. This means that businesses that do not consider unstructured data are wasting a lot of valuable business intelligence.
What is relatively structured and unstructured data?
Semi-structured data is unstructured data that also has metadata that identifies specific characteristics. The metadata contains sufficient detail to make the data to be cataloged, searched, and analyzed more efficiently than strictly unstructured data. Consider semi-structured data to be a bridge between structured and unstructured data. A tab-delimited file containing customer data versus a database containing CRM tables is a good example of semi-structured data vs. structured data. On the other hand, semi-structured data has more hierarchy than unstructured data.
What is the major difference between structured and unstructured data?
Unstructured data is defined as information that is not mainly controlled via a technology standpoint, including such information that isn’t even stored in a database management system (RDBMS). While systematic or structured data can be analyzed in a data model as records (or transactions), such as tables in a SQL query chart. In a nutshell, there is no specified distinction between structured and unstructured data. Since each possesses tools that allow users to view information. Unstructured data simply appears to be more widely available than structured information.
Structured vs Unstructured Data: Understanding the key differences
Specified vs Non-specified Data
Structured data is data that has clearly defined types in a structure, whereas unstructured data is typically kept in its native format. Structured data is organized in rows and columns and can be modeled to pre-defined fields. Unstructured data, in contrast to structured data, which is organized and easy to access in relational databases, lacks a predefined data model. This means that structured data takes advantage of schema-on-write and unstructured data employs schema-on-read.
Data Types: Analytical vs Evaluative data
Structured data is frequently quantitative data, which means it is made up of hard figures or things that can be recorded. Analysis methods include regression (to predict relations among variables), classification (to estimate possibility), and data clustering (based on different attributes). Unstructured data, on the other hand, is frequently classified as qualitative data because it cannot be processed or analyzed using traditional tools and methods. In a business context, qualitative data can come from customer surveys, interviews, and social media interactions, for example. To extract insights from qualitative data, advanced analytics techniques are required such as data mining and data stacking.
Structured vs Unstructured Data: Data house Storage
Structured data is typically stored in data warehouses, whereas unstructured data is typically stored in data lakes. A data warehouse is the destination of the data after it has passed through an ETL channel. A data lake, on the other hand, is a virtually limitless source in which data is stored in its original version or after a basic “cleaning” process. Both can be used in the cloud. Structured data requires less storage space than unstructured data. Even a small image, for example, requires more space than many pages of text. Structured data is typically stored in a relational database (RDBMS), while Non-relational, or NoSQL, databases, on the other hand, are the best choice for unstructured data.
Analytical Ease
One of the most significant distinctions between structured and unstructured data is the ease with which it can be analyzed. Structured data is simple to search, for both humans and algorithms. Unstructured data, on the other hand, is inherently more difficult to search and requires processing before it can be understood. Such as;
- It is difficult to deconstruct because of the unavailability predefined data model and thus does not fit in relational databases.
- While there are numerous advanced analytics tools for structured data, most analytics tools for mining and organizing unstructured data are still in the early stages of development.
- Data mining is difficult due to the lack of a predefined structure, thus developing best practices for handling data sources such as rich media, blogs, social media data, and customer communication is a challenge.
- The average business user can use structured data, but unstructured data demands data science expertise to acquire accurate business intelligence.
Layout Reformulation vs. Diverse Templates
Text and numbers are the most commonly used structured data formats. Structured data has been pre-defined in a data structure. Unstructured data, on the other hand, come in many different shapes and sizes. It can include audio, video, and graphics, as well as email and sensor data. There is no data model for unstructured data; it is kept natively or in a data lake with no alteration required.
Structured data vs. Unstructured data: A quick glance
Structured data vs. unstructured data comes down to data types that can be used, the level of data expertise required to use it, and on-write versus on-read schema. Structured vs. unstructured data can be distinguished by considering the data’s who, what, when, where, and how.
| Structured Data | Unstructured Data | |
| Who | Self-service access | Requires data science expertise |
| What | Only select data types | Many varied types conglomerated |
| When | Schema-on-write | Schema-on-read |
| Where | Commonly stored in data warehouses | Commonly stored in data lakes |
| How | Predefined format | Native format |
Conclusion
What makes growing data difficult to understand or contemplate is that many agencies dislike deleting data. You never know when you’ll require it. And when you do, via structured data you will be able to easily find it. While the main objective of unstructured data is to assist business users in leveraging historical data, this demands data search, predictive analysis, and business intelligence. These are popular places where a lot of innovation is taking place.
Takethiscourse provided a detailed overview to help you understand the differences, so now it’s your moment to shine!