Unstructured Data

What is unstructured data?

Unstructured data refers to qualitative, complex, unorganized data in text, audio, and visual files. Some examples of unstructured data sources include social media posts, user-generated customer reviews, PDFs, emails, and video recordings. 

The lack of organization and predefined formatting make it challenging to collect and analyze this data type, but when interpreted and analyzed appropriately, unstructured data can provide worthwhile information. Businesses use statistical analysis software to perform complex analyses, including organizing, interpreting, and presenting data sets.

Types of unstructured data

Unstructured data can be textual, non-textual, human-generated, or machine-generated. Below are some typical kinds of unstructured data:

• Text comprises documents, e-mail messages, presentations, or text messages. 
• Geospatial data includes global positioning system (GPS) information or location data shared through mobile phones.
• Multimedia data covers unstructured data such as images, videos, and audio files.
• Sensor data is generated from sensors like accelerometers and other devices.
• Web data is processed from websites as hypertext markup language (HTML), cascading style sheets (CSS), or Javascript. 
• Financial data includes invoices, bank statements, and other fiscal records.
• Rich media encompasses all advertising or media platforms such as social media, entertainment, surveillance, or podcasts.

Examples of unstructured data

Individuals and businesses generate large volumes of unstructured data in their daily lives and operations. It comes in various types, such as:

• Emails. Unstructured data is commonly found in the form of emails. Emails generally contain blocks of unstructured text data and file attachments of different types and sources.

• Text files and documents. Plain text files, Microsoft Word documents, Google Docs, PDF files, HTML files, and other word processing formats can contain unstructured data in written content. 

• Log files. Many systems and applications generate log files of unstructured data regarding various events and activities. System logs, application logs, security logs, and web server logs are examples. 

• Images. JPEG, PNG, GIF, and TIFF files are different image types containing unstructured data. Image files store visual information and data. 

• Videos. MP4, MOV, and AVI files are different types of video files of unstructured data. This can include recorded content, media streaming, and video clips. 

• Audio files. MP3, WAV, and FLAC files are different types of audio files with unstructured data. Some common examples of audio files in the workplace include voice recordings, customer service calls, and interviews.

• Sensor data. Various devices use sensors to measure and record physical and environmental data. These include GPS data and thermometer recordings. 

• Social media data. Instagram posts and stories, Facebook status updates, and posts on X are all unstructured social media data. It has no predefined structure, whether it’s text-based, an image, or multimedia content.

• Internet of Things (IoT) data. The IoT offers data such as device statuses, metadata, sensor readings, or CCTV footage. 
• Medical records. The healthcare industry produces enormous volumes of human-generated and machine-generated unstructured data, which helps healthcare workers provide appropriate treatment. Medical imaging devices like endoscopes, laparoscopes, surgery robots, and biosignal data are examples of machine-generated big data.

Benefits of unstructured data

Unstructured data includes various content types and comes with several benefits and deep insights despite its lack of structure. Some of the key benefits of unstructured data include the following.

• Easy to collect and store. Since unstructured data doesn’t follow a specific format, it can be collected quickly, in its raw form, without predefined schemas, tables, or other data models. Once the data is collected, organizations can store it on shared or hybrid cloud servers. 

• Provides more granular information than structured data. Unstructured data is raw and unfiltered, which means it can offer more detailed insights. For example, businesses can use specific data and verbiage in customer service emails to improve their customer service team performance and build a knowledge center based on different aspects of the email data.

• Useful in multiple ways, more than once. Teams can use unstructured data more than once and analyze it for multiple purposes because it doesn’t have a predefined structure or follow a set of rules. Businesses can extract knowledge from the subjective information, opinions, and nuances of unstructured data sets.

• Leads to better customer service. Unstructured data is collected from tracking emails, messages, live chats, and customer-raised tickets. Companies pinpoint improvement areas by analyzing this data.
• Helpful in marketing. Marketing teams determine customer requirements and purchasing patterns by assessing unstructured data. It helps in planning dedicated marketing campaigns. 
• Results in better decision-making. Organizations use unstructured data to find trends that could benefit the business. It gives management and critical stakeholders more information to make efficient decisions.
• Brings back customers. Unstructured data, or big data, reveals much about a customer's needs, preferences, likes and dislikes, or purchase behavior. Companies can evaluate the data to make better decisions about how to keep existing while acquiring new ones.
• Works well with data lake storage. Data lakes accommodate massive storage for unstructured data. They also have a pay-as-you-use pricing that helps organizations cut costs.

Challenges of unstructured data

Even with all of the unstructured data’s many benefits, it presents challenges for organizations due to its lack of a predefined format. Some of the key challenges of unstructured data include:

• Volume and scalability concerns. Unstructured data often accumulates rapidly. While some may occupy little space, larger files consume available storage, straining resources and creating growth issues. Storage solutions can also be costly if no one tends to the maintenance of unstructured data.

• Quality. Although unstructured data may be more granular than structured data, it often contains errors and inconsistencies. Extracting insightful information from data sets with errors, unnecessary information, and inconsistencies requires intricate processing. 

• Access to siloed data. Unstructured data sometimes resides in isolated and disconnected data sources and repositories that can’t be integrated. These silos lead to redundant data that wastes space. On the flip side, without integration capabilities, organizations may need to exclude some siloed data, which can cause gaps in identifying patterns and trends.

Unstructured data vs. structured data

There are key differences between unstructured data vs. structured data that are important to understand.

Unstructured data lacks a predefined structure; it includes various formats and types, such as images, audio, text, and videos. Due to its raw format and lack of framework, unstructured data requires advanced technologies, like statistical analysis and natural language processing techniques, to gather helpful lessons from the information. 

In comparison, structured data is highly organized with an explicit schema that defines the data types and relationships between pieces of information. It’s easier to process and analyze than unstructured data.

Unstructured data is a type of big data. Read more to learn about big data and data analytics.