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].
Related Terms
- NLP (Natural Language Processing): Analyzing and understanding human language in text data.
- Image recognition: Identifying objects and scenes within images
- Speech recognition: Converting spoken language into text
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.
- 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