Differences between structured and unstructured data and how you should use each for your benefit
Data collection and analysis play a crucial role in the world of digital marketing. Many companies emphasize data collection. That's why it's important to take a closer look at the forms that data comes in. There are two types of data that companies usually collect: structured and unstructured data. These two constitute the sum of a company's data collection.
Therefore, both types of data are essential in the modern digital enterprise; however, you must learn to handle them differently.
What are structured data?
As the word "structured" suggests, the data is highly organized and neatly formatted. It is a type of data that can be placed into tables and spreadsheets. This data is also known as quantitative data. Most companies collect transactional data as structured data, which includes financial information that complies with compliance standards. The best example of structured data is consumer data.
More examples of structured data are credit card numbers, financial amounts, dates, phone numbers, addresses, and product names.
Typical unstructured data generated by humans includes:
Text files: word processing, spreadsheets, presentations, email, logs, etc.
Email: Email has some internal structure thanks to its metadata, and sometimes we refer to it as semi-structured. However, its message field is unstructured and traditional analysis tools cannot parse it.
Social media: data from Facebook, Twitter, LinkedIn, etc.
Websites: YouTube, Instagram, and photo-sharing sites.
Mobile data: text messages, locations.
Communications: chat, instant messaging, phone recordings, collaboration software.
Media: MP3s, digital photos, audio and video files.
Business applications: MS Office documents, productivity apps, etc.
What are unstructured data?
As the term suggests, unstructured data is not organized or properly formatted. It's a significant challenge to collect, process, and analyze unstructured data. Unstructured data, also known as qualitative data, covers everything that structured data does not. Unstructured data increases every year and proves difficult for companies to manage.
Some examples of unstructured data include reports, audio, files, text files, social media comments, opinions, emails, and many more. Typical machine-generated unstructured data includes:
Satellite images: weather data, geographic features, military movements.
Scientific data: oil and gas exploration, space exploration, seismic images, atmospheric data.
Digital surveillance: surveillance photos and video.
Sensor data: traffic, weather, oceanographic sensors.
Difference between structured and unstructured data
From the above information, the differences between structured and unstructured data should be clear. Structured data is easy to collect, analyze, and store, whereas unstructured data is unorganized and requires more work to properly investigate. Unstructured data also covers much more ground than structured data, 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 conveyed is done in an unorganized manner. Despite this, we can still digest and understand that data. Structured data, on the other hand, is more in line with how computers process data. It's perfectly organized and easy to analyze. Being able to analyze unstructured data through computational processes then becomes the challenge.
Conclusion
For the overall success of the organization, companies must properly and effectively analyze all their data, regardless of the type's source. You must know the difference between these two in order to use them effectively in your marketing strategy.
