The World Economic Forum predicted that by 2025, 463 exabytes of data will be generated each day globally – which is unbelievably shocking! But this influx of data comes with a challenge: not all data is created equal. Some are structured and most of it is unstructured. Structured and unstructured data are two broad categories of collectible data. Both types of data come with their own characteristics, challenges, and opportunities. Therefore, mastering how to handle both types of data is essential for business success. Failing to process both structured and unstructured data could leave businesses behind.
Now the question arises: why should business care about the differences between structured and unstructured data?
The answer lies in the significant impact on how businesses store, process, analyze, and use their data. Structured data is easy to organize, query, and manipulate, but it may not capture the full richness and complexity of your data. Unstructured data is more diverse, dynamic, and expressive, but it may be difficult to access, understand, and integrate. By knowing the strengths and weaknesses of both types of data, you can choose the best methods and tools to handle your data and achieve your goals.
Read on to thoroughly understand the detailed differences between structured and unstructured data and their role in data analysis, decision-making, and business growth.
Exploring the data formats: Structured vs unstructured data
As mentioned earlier, data is not uniform, it comes in various forms and structures. Each data format is differently sourced, collected, scaled, and stored differently to ensure optimal processing and retrieval. Let’s categorize this data into two types: structured and unstructured.
What is structured data?
Structured data is found everywhere and is generated by both humans and machines. It is quantitative and comes in the form of numbers and values. Structured data is organized in a predefined format, typically in rows and columns, making it easily searchable and analyzable. Structured data is easy to store, query, and analyze using relational or structured query languages (SQL).
What is unstructured data?
Unlike structured data, unstructured data is quite a hassle to categorize or search. Unstructured data lacks a predefined data model or organization and is stored in its native format in no-relational (NoSQL) databases. Moreover, unstructured data that does not have a predefined schema, format, or structure. Some of the most common examples of unstructured data include texts, images, audios, videos, emails, documents and PDFs, or social media posts.
Since the amount of unstructured data keeps growing and now accounts for a whopping 80-90% of all organization data. This means organizations with unstructured data require advanced tools and techniques, such as natural language processing (NLP), computer vision, or machine learning techniques to manage, analyze, and extract valuable insights from this vast data for business intelligence.
A common example of the difference between structured and unstructured data is the difference between a customer survey and a customer review. A customer survey is a structured data source, as it has a fixed set of questions and answers, and can be easily stored, queried, and analyzed using a database or spreadsheet. A customer review is an unstructured data source, as it has a free-form text, and may also include images, videos, ratings, or emotions, and may require natural language processing or machine learning techniques to extract meaningful information. Also, integrating a video content management system helps manage and analyze unstructured video data effectively.
| Just a heads up: What is semi-structured data? Semi-structured data is another data format that doesn’t fit neatly into traditional rows and columns like structured data, but it still contains organizational properties that make it easier to analyze than completely unstructured data. |
Read more: Choosing the right BI and analytics tools for your data.
Key terms to know for managing and storing different data types
To provide a comprehensive understanding of how structured and unstructured data are managed and used, it’s essential to understand the key concepts such as data lake, data warehouse, and Lakehouse to navigate the complex data landscape. Understanding these terms will not only clarify the discussion but also highlight the different approaches in handling various types of data. Let’s delve into these key terms to build a solid foundation for our exploration.
Data lake
A data lake is a centralized repository that stores raw data of any type, structure, or format, without imposing any schema or transformation on the data. Data lakes allow you to store and access all your data in one place, without losing any information or flexibility.
Data warehouse
A data warehouse is a specialized repository that stores structured or semi-structured data and undergoes transformation, cleaning, and organization for analysis and reporting purposes. Data warehouses allow you to perform fast and complex queries and analytics on your data, using predefined schemas and dimensions.
Lakehouse
A Lakehouse is a hybrid approach that combines the best features of data lakes and data warehouses, by enabling both schema-on-read and schema-on-write capabilities. Lakehouse allows you to store and access both structured and unstructured data, while also providing reliable and efficient data quality, governance, and performance.
Further readings: Data Lake vs Data warehouse: 6 key differences you need to know
