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All data is not created equal. Some data is structured, but most of it is unstructured. Structured and unstructured data is sourced, collected and scaled in different ways, and each one resides in a different type of database. In this article, we’ll take a deep dive into both types so that you can get the most out of your data. Structured data — typically categorized as quantitative data — is highly organized and easily decipherable by machine learning algorithms. Developed by IBM in 1974, structured query language (SQL) is the programming language used to manage structured data. By using a relational (SQL) database, business users can quickly input, search and manipulate structured data. Pros and cons of structured dataExamples of structured data include dates, names, addresses, credit card numbers, etc. Their benefits are tied to ease of use and access, while liabilities revolve around data inflexibility: Pros
Cons
Structured data tools
Use cases for structured data
What is unstructured data?Unstructured data, typically categorized as qualitative data, cannot be processed and analyzed via conventional data tools and methods. Since unstructured data does not have a predefined data model, it is best managed in non-relational (NoSQL) databases. Another way to manage unstructured data is to use data lakes to preserve it in raw form. The importance of unstructured data is rapidly increasing. Recent projections indicate that unstructured data is over 80% of all enterprise data, while 95% of businesses prioritize unstructured data management. Pros and cons of unstructured dataExamples of unstructured data include text, mobile activity, social media posts, Internet of Things (IoT) sensor data, etc. Their benefits involve advantages in format, speed and storage, while liabilities revolve around expertise and available resources: Pros
Cons
Unstructured data tools
Use cases for unstructured data
What are the key differences between structured and unstructured data?While structured (quantitative) data gives a “birds-eye view” of customers, unstructured (qualitative) data provides a deeper understanding of customer behavior and intent. Let’s explore some of the key areas of difference and their implications:
What is semi-structured data?Semi-structured data (e.g., JSON, CSV, XML) is the “bridge” between structured and unstructured data. It does not have a predefined data model and is more complex than structured data, yet easier to store than unstructured data. Semi-structured data uses “metadata” (e.g., tags and semantic markers) to identify specific data characteristics and scale data into records and preset fields. Metadata ultimately enables semi-structured data to be better cataloged, searched and analyzed than unstructured data.
The future of dataRecent developments in artificial intelligence (AI) and machine learning (ML) are driving the future wave of data, which is enhancing business intelligence and advancing industrial innovation. In particular, the data formats and models covered in this article are helping business users to do the following:
Furthermore, smart and efficient usage of data formats and models can help you with the following:
Structured and unstructured data and IBMWhether you are a seasoned data expert or a novice business owner, being able to handle all forms of data is conducive to your success. By leveraging structured, semi-structured and unstructured data options, you can perform optimal data management that will ultimately benefit your mission. To better understand data storage options for whatever kind of data best serves you, check out IBM Cloud Databases. |