Looking Into The Differences Between Structured And Unstructured Data

Data plays a significant role in business operations worldwide. Several types of data are used by people every day, which aids them in daily business activities, planning, and decision-making. Data comes in different types and forms. Some are structured, such as the information you can see in worksheets. But the bigger share of data is unstructured. Both are sourced, collected, and scaled differently and have different database types for storage.

Key differences

Simply put, structured data gives an overview of customers, while unstructured data provides a deeper understanding of consumer intent and behavior. In terms of usage, structured data is applied to machine learning, whereas unstructured data is used for text mining and natural language processing. 

Structured data

Structured or quantitative data is exceptionally organized, and machine learning algorithms can easily recognize it. It uses a structured query language or SQL programming language for its management. Users find it easy to input, manipulate, or search for structured data with a SQL database. Examples of this type of data include credit card numbers, addresses, names, and dates. They are easy to access and use, but they are not flexible. 

There is limited use for structured data because of its predefined structure. As a result, the data can only be used for a specific purpose, detrimental to the data’s usability and flexibility. Likewise, the storage options for structured data are limited to data warehouses that have rigid schemas. Therefore, when there are changes in the data requirement, which calls for an update of every structured data, it eventually results in more investment in resources and time. 

Use cases 

Structured data is used for customer relationship management and SEO. Likewise, it can be used for online bookings through a WordPress booking system, such as ticket and hotel reservations. Accounting departments and accounting firms also use structured data to process and record financial dealings.

Unstructured data

Also called qualitative data, unstructured data cannot be processed and analyzed by conventional methods and tools. Moreover, it does not have a predefined data model; thus, it needs a different management program. You can check out https://dataloop.ai/platform/data-management/ to determine why its management requires a different platform.

It can indeed be challenging to handle unstructured data because it comes in many forms, including text, social media posts, mobile activity, sensor data, IoT, documents, etc. But, on the other hand, unstructured data is easy and quick to collect, and data lakes can be used for its storage. 

Despite the amount and easy availability of unstructured data, it needs expert data analysts for its preparation and analysis. Moreover, other users cannot access the data because they may not be familiar with the specific data topics and how they should be utilized. 

Use cases

Unstructured data is commonly used for data mining because it can be an excellent source of information on consumer behavior, purchasing patterns, and product preferences, which can help businesses serve their customer base better. In addition, unstructured data can also be used for predictive data analytics to give companies a head start in strategizing and adjusting to projected shifts in the market. 

Structured and unstructured data both have significance and value to enterprises to ensure that they can provide their consumers with an enhanced experience. The key to handling the two types is to ensure optimal data management that fits your business goals. 

Also Read: Data Warehousing: A Critical Path to Customer Success in the Cloud