Definition
Unstructured
data is information with no preset data model and can’t be stored in a
traditional relational database. They are information, in various conformation,
that doesn't act in accordance with conventional data models, making it
difficult to store and manage in a mainstream relational database [1]. For
example, a standard email is made up of a sender, one or more receivers, sent
time, and a message which sometimes may include one or more attachments to it.
The senders, receivers, and time sent, fit into a structured data model but the
message body contains unstructured information. Without reading the entire
message body, figuring out its meaning and context becomes unachievable [2].
Various forms of unstructured data [1]
Origin
According to Wikipedia, itself a monolith of poor attribution, or at least often accused as such — unstructured data was first collected in 1958. Today, the internet of things (IoT), social media, digital media, and a myriad of mobile/geo-spacial data continue to grow unstructured and big data by the Petabyte, or is it Exabyte [3].
Context and Usage
Unstructured
data, lacking a predefined format, is crucial in AI for its rich context and
diverse applications, enabling natural language processing, image recognition,
and various other AI tasks. AI techniques like NLP and machine learning are
used to extract valuable insights from this data, leading to better
decision-making, improved customer experiences, and more in Industries like
healthcare, finance, retail, manufacturing, and media & entertainment.
Why It Matters
Data is an important resource that businesses make use of to arrive at critical decisions and product experiences. Data has grown rapidly in the last few years. It is of the opinion that about 80% to 90% of global data exists in the form of unstructured data, including rich media, social media, and surveys [4]. From a stand point of view, it is estimated that by 2025, data will grow to over 180 zettabytes globally [5]. Most of these data are in unstructured form. New progress in technological developments in fields such as Artificial Intelligence, Machine Learning, and Natural Language Processing have assisted organizations gain an understandable view of their loads of unstructured data to power their Business Intelligence and Analytics. Businesses and Organizations will make existing products better, improve the organization of internal processes, and empower informed decision-making by embracing AI and ML and studying the insights gained from structuring unstructured data [5].
In Practice
A real-life case
study of a business practicing the utilization of unstructured data can be seen
in the case of Spotify. Spotify processes massive amounts of unstructured data
to power its recommendation system. With these diverse unstructured data
sources, Spotify creates personalized playlists like Discover Weekly and Daily
Mix that have become central to their competitive advantage. Their
recommendation engine processes over 100 billion events each day, using AI to
transform unstructured audio and text into personalized music discovery.
Related Machine Learning Data Categories:
Test Data: Separate dataset used to evaluate a model's performance on unseen examples
Training Data: Data used to train the model
Validation Data: Data for tuning hyperparameters
References
- Barney, N. (2025). What is unstructured data?
- Kleinings, H. (2024). Data Types and Applications: Structured vs Unstructured Data
- Medium. (2018). A Big, Unstructured History of Data.
- Baig, A. (2024). What is Unstructured Data with Examples? – Explained
- Needl. (2021). Structured Vs Unstructured Data: Role Of ML/AI In Deriving Insight