Difference between Structured Data & Unstructured Data
Collecting and analyzing data plays a crucial role in the digital marketing world. So many businesses and enterprises emphasizing data collection that’s why it’s important to take a closer look at the forms that data come in. Basically, there are two types of data that businesses usually collect. They are structured and unstructured data and these two make up the sum of an organization’s data collection.
Therefore, both these types of data are essential in the modern digital enterprise; however, you must learn to manage them differently. This article will help you understand the difference between these two. So, keep reading to get the most out of both of them. Let’s tackle them one by one.
What is Structured Data?
As the word structure itself suggests the data which is highly organized and neatly formatted. It is a type of data which can be put into table and spreadsheets. This data is also referred to as quantitative data. Most businesses collect transactional data as structured data which includes financial information that meets compliance standards. The best example of structured data is Consumer data.
Some more examples included in structured data are credit card numbers, financial amounts, dates, phone numbers, addresses, product names, etc.
Typical Human-Generated Unstructured Data Includes:
- Text files: Word processing, spreadsheets, presentations, email, logs.
- Email: Email has some internal structure thanks to its metadata, and we sometimes refer to it as semi-structured. However, its message field is unstructured, and traditional analytics tools cannot parse it.
- Social Media: Data from Facebook, Twitter, LinkedIn.
- Website: YouTube, Instagram, photo sharing sites.
- Mobile data: Text messages, locations.
- Communications: Chat, IM, phone recordings, collaboration software.
- Media: MP3, digital photos, audio, and video files.
- Business applications: MS Office documents, productivity applications.
What is the Unstructured Data?
As the word indicates, unstructured data isn’t organized or properly formatted. It is a significant challenge to collect, process, and analyze unstructured data. The unstructured data is also called qualitative data which covers everything that structured data doesn’t. Unstructured data grows larger every year and it becomes difficult for companies to manage.
Some examples of unstructured data: reports, audio, files, text files, social media comments, and opinions, emails, and many more.
Typical Machine-Generated Unstructured Data Includes:
- Satellite imagery: Weather data, landforms, military movements.
- Scientific data: Oil and gas exploration, space exploration, seismic imagery, atmospheric data.
- Digital surveillance: Surveillance photos and video.
- Sensor data: Traffic, weather, oceanographic sensors.
Difference between Structured Data and Unstructured Data
From the above information, the differences between structured and unstructured data should become clear. Structured data is easy to gather, analyze, and store while unstructured data is unorganized and requires more work to properly investigate. Unstructured data also covers a lot more ground than the structured variety, with many more examples that are only growing as the internet continues to expand.
In a sense, unstructured data is similar to how we as humans process and analyze information. If you have a conversation with someone, all the information that is conveyed is done so in an unorganized fashion. Despite this, we’re still able to digest that data and understand it. Structured data, on the other hand, is more in line with how computers process data. It’s neatly organized and easy to analyze. Being able to analyze unstructured data through computer processes then becomes the challenge.
From the above points or explanations, the difference between structured data and unstructured data must be clear now. Structured data is easy to collect, analyze, and store. And unstructured data is unorganized and requires more work to properly analyze and investigate. However, for the overall success of the organization, enterprises need to properly and effectively analyze all of their data, irrespective of the source of the type. You must know the difference between these two so that you can effectively use them in your marketing strategy.
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