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What is unstructured data: Definition, types, and examples

What is unstructured data: Definition, types, and examples

Enterprise companies are now prioritizing efforts to bring more structure to this type of data, as it is the key to unlocking new types of value for both consumers and internal teams. It’s highly likely that your organization has been accumulating unstructured data in the form of documents, images, videos, etc. for a long time and are likely sitting on a gold mine of untapped potential. 


It’s been also shown that 80% of all data in enterprise is now unstructured, there is a significant opportunity for organizations to drive more value and more use cases aligned to these critical workloads. For further reference, the volume of unstructured data in 2018 was roughly 33 zettabytes. This is expected to grow to 175 zettabytes by 2025 and beyond. These unstructured data use cases can lead directly to revenue generating initiatives or immense cost savings, and the surface area of use cases for unstructured data are only limited by your creativity and the use cases you want to solve.


Definition of unstructured data

Unstructured data is defined as information that is not arranged according to a preset data model or schema, and therefore cannot be stored in a traditional database. Text, images, and videos are three of the common types of unstructured data and many business documents are unstructured or semi-structured such as email messages, videos, photos, webpages, and audio files.  


The challenge arises when organizations attempt to scalably tackle the process of accessing, analyzing, and deriving maximum business value from their unstructured data as this data has been siloed and not easily accessible within an organization. While unstructured workloads were primarily the domain of data science and machine learning teams, with complexity of tooling being a significant barrier to entry, we are now starting to see data analysts and business intelligence teams now have access to platforms that enable these key personas to derive benefit from handling unstructured workloads.


Companies in numerous verticals, including retail, entertainment, insurance, and beyond often have content sitting idle both in their physical and digital asset management systems. Teams are increasingly finding ways to repackage content, provide recommendations deeper into their catalog, and use machine learning and AI to derive insights from their unstructured data in order to answer fundamental business questions. 


Take media and entertainment as an example industry. A head of data and analytics at a media enterprise is likely asking questions such as “Are we consistently tracking the rights of my content across channels? Are we empowering our teams to be more productive when planning their marketing campaigns? How do I give my teams access to more information about customer sentiment and engagement with our products and services?". With unstructured data, it’s not just about leveraging content that exists, but rather how teams can surround that content with more effective marketing and smarter operations.


Types of unstructured data

A few types of the human-generated unstructured data include:

  1. Emails: Email message fields are unstructured and cannot be parsed by traditional analytics tools. That said, email metadata affords it some structure, and explains why email is sometimes considered semi-structured data.

  2. Text files: This category includes word processing documents, spreadsheets, presentations, email, and log files.

  3. Social media and websites: Data from social networks like Twitter, LinkedIn, and Facebook, and websites such as Instagram, photo-sharing sites, and YouTube.

  4. Communication data: Text messages, phone recordings, collaboration software, chat, and instant messaging.


Examples of unstructured data

Enterprises have been collecting vast amounts of unique and proprietary unstructured data and there has been an explosion in this trend with the broad adoption of new technologies such as cloud computing, mobile devices, sensors and cameras, etc.


A few examples of unstructured data generated by machines include:

  1. Scientific data: Oil and gas surveys, space exploration, seismic imagery, and atmospheric data.

  2. Digital surveillance: Reconnaissance photos and videos.

  3. Satellite imagery: Weather data, property and real estate data, and military movements


Leveraging unstructured data

New tools have recently become available to analyze unstructured sources. Powered by AI and machine learning, platforms such as Labelbox can function at near real-time speed and educate themselves based on the patterns and insights they uncover. 


Data analysts and BI teams can now self-serve when curating their unstructured data, providing quick value without having to wait for an AI/ML team to build an expensive model. These types of systems are now being utilized against large unstructured datasets to power innovative applications which include:

  1. Analyzing communications for regulatory, risk, and IP compliance

  2. Gaining insights into widespread customer behavior and preferences

  3. Tracking and analyzing customer social media conversations and interactions


In the past, this has required painstaking work and high budgets for large teams of people to sequentially catalog large data sets using antiquated techniques like “data entry” or expensive techniques like building AI models. This often required high spend on data before even knowing the data’s true value. 


In addition, with the emergence of foundational models, customers who have historically been unable to have access to the resources to build robust NLP, computer vision, or document models can now make sense of their unstructured data and fine tune their own models using these foundational models.


Final thoughts on unstructured data

Fortunately with today’s technology, it’s much simpler. Leveraging a platform like Labelbox Catalog, teams explore areas of value in their data with a fraction of the work by leveraging tools born in the world of machine learning that are now packed for easy use by anyone with an interest in their data.


As an example, Labelbox’s Catalog embeddings capabilities can help you quickly uncover high-level patterns and visually similar data from across all your data sets. This process allows teams to search across millions and millions of data points in minutes rather than weeks. Set up a way to easily search for text data using filters such as annotation, metadata and similarity embeddings to prioritize text snippets to label or create review tasks to fix issues that matter the most.