Unstructured data definition
Unstructured data refers to data that is not organized in a designed model or structure. Unstructured data is typically categorized as qualitative and could be human or machine-generated. Unstructured data is the most abundant type of data available and, once analyzed, can be used to guide business decisions and achieve business goals amongst many other use cases.
Unstructured data is typically stored in its native format. This contributes to the challenge of converting this data into actionable insights. While unstructured data can be more challenging to work with than structured data, it also often contains rich, detailed information that isn't available in structured data. As a result, many organizations are investing in technologies like machine learning (ML) and natural language processing (NLP) to better analyze and gain insights from unstructured data.
Examples of unstructured data
Unstructured data is qualitative and it exists in text, image, audio, or video formats. Different examples of unstructured data include:
- Rich media, such as audio or video data, surveillance data, geospatial data, images, and weather data.
- Internet of Things (IoT) data, such as ticker or sensor data from devices.
- Textual data, such as emails, text messages, invoices, records, and productivity applications communications data.
- Scientific data, such as machine-generated space exploration or seismic reports.
- Healthcare data and imaging, such as MRIs, x-rays, and CT scans and other medical data like doctor’s notes and prescriptions.
Additional unstructured data examples will naturally emerge as new data-capturing technology develops.
Structured data vs. unstructured data
Structured data, unlike its unstructured counterpart, is quantitative data that exists in a predefined structure or model. This data is highly organized and therefore easily processed by businesses and machine-learning algorithms.
Think of structured data as the type of data that neatly fits into spreadsheets or relational databases like SQL, MySQL, and PostgreSQL — it can be easily mapped in a pre-defined structure. Structured data is used to manage customer relationships as it provides businesses with information that is easy to interpret: logs, metrics, dates, names, zip codes, credit card numbers, etc.
By contrast, unstructured data is qualitative data and does not have any consistent internal structure. As a result, unstructured data is difficult to interpret without the right set of tools and expertise.
Structured data can give businesses an overview of their customers’ behavior—the what, like names, purchase histories, and geolocation. Unstructured data is better suited to providing businesses with a deeper understanding of their customers’ intent and behavior—the why and how, like product reviews, support tickets, and website navigation patterns.
Challenges of unstructured data
The volume, variety, and disparate quality of unstructured data are common challenges to organizations looking to process, manage, and analyze the data.
- Data volume: Unstructured data is abundant. It makes up 80% of existing data1 and is constantly being generated. The research firm ITC expects that data volume will grow 430% from 2018 to 20252.
- Data variety: Unstructured data is composed of a large variety of data types, such as textual data, image, or video. Large data repositories such as data lakes are required to store unstructured data in one place. The inherent variety of unstructured data also presents a linking challenge — how do you cross-reference images, videos, and text?
- Data quality: The quality of unstructured data is inconsistent, in part because of its variety. Unstructured data can contain errors, inconsistencies, or irrelevant information, which can make it difficult to get accurate information. Preprocessing or cleaning unstructured data to improve quality can be a time-consuming, complex task.
- Analysis: Unlike structured data, which can be quickly queried and analyzed, unstructured data is often text-heavy and doesn't fit neatly into a database. Unstructured data is stored in its native format and is only processed when viewed.
- Security and privacy: Unstructured data can contain sensitive information. Ensuring the security of this data and maintaining privacy can be challenging.
- Integration: Integrating unstructured data with structured data for a holistic view can be complex due to the lack of a predefined data model.
The challenge of managing and analyzing unstructured data is therefore primarily due to the volume of data. An organization can encounter items, objects, or files that can span anywhere from a few gigabytes (GB), such as an email, to several petabytes (PB), such as a full-length media file. So while it can be managed manually, many databases and tools cannot handle this volume and variety of unstructured data. Specific tools and tech are needed to store and process exponentially growing data.
Applications of unstructured data
When analyzed, unstructured data provides businesses with a variety of opportunities. As qualitative data, unstructured data can help businesses better understand their customers, customer intent, and market shifts. This empowers businesses to provide better, more secure, and resilient customer experiences.
Some applications of unstructured data include:
- Improving customer experiences: Analyzing customer support chats, emails, and call transcripts can help identify common customer issues, improve support protocols, personalize customer search experiences, and train customer service representatives more effectively.
- Predicting patient healthcare outcomes: Patient medical records often contain unstructured data like doctor's notes, which can be analyzed to identify patterns, predict patient outcomes, or inform treatment plans.
- Detecting fraud: In financial services, unstructured data can be used to detect fraudulent activity. For example, an analysis of email communications might reveal suspicious patterns that indicate fraudulent behavior.
- Providing recommendations: E-commerce platforms and streaming services can analyze unstructured data, such as product descriptions or movie scripts, to improve their recommendation algorithms.
- Training natural language processing (NLP) models: Unstructured data is crucial in training AI models in NLP. For instance, a chatbot learns from a large corpus of text data that is unstructured in nature.
- Training AI for image recognition: Unstructured data in the form of images is fundamental in training machine learning models for tasks like facial recognition, object detection, and more.
- Providing predictive data analytics: Analyzing unstructured data allows businesses to predict market trends and adjust accordingly.
- Conducting sentiment analysis: Mining unstructured data can give businesses insight into customer sentiment, behaviors, and purchasing patterns. Businesses can also analyze data from social media posts, product reviews, and customer feedback to understand customer sentiment towards their products, services, or brand overall.
These applications of unstructured data provide businesses with a number of benefits.
Mitigate security risk
Analysis of telemetry data can help glean valuable insights and keep users informed of real-world cybersecurity threat phenomena and trends. Through use of a modern security information and event management (SIEM) tool, security teams can search at scale across massive amounts of any kind of data, including unstructured data, to assist with monitoring and compliance, threat detection, prevention, and hunting, and incident response.
Improve operational resilience
With the need to ensure that applications are optimized for availability and performance, organizations need to be able to observe the unstructured data that is being produced by their systems. Logs and metrics can indicate in real time that user demand is exceeding capacity or a server error is affecting performance. When the root cause is known, it can be addressed.
Enhance customer experience
Businesses can deliver a better user experience by providing a better search experience to customers by managing unstructured data. Rich search additions improve the front-end and back-end search experience for customers and developers alike. A customer can easily find that yellow toy with stripes for their child or an employee can easily find the file, image, or video clip they need, no matter what environment it’s in.
How to manage and analyze unstructured data
By nature, unstructured data has no predefined structure that enables easy management and analysis. So, in order to analyze unstructured data, you first need to manage it by defining a structure. This allows you to store, organize, and secure your unstructured data.
Organized unstructured data is then ready for processing and analysis. These analyses provide organizations with actionable insights.
A variety of tools and technologies that enable you to manage and analyze unstructured data are available.
Natural language processing (NLP): NLP is a technology that focuses on the interaction between computers and humans through natural language. The goal of NLP is to read, decipher, understand, and make sense of human language in a valuable way.
Machine learning (ML): Machine learning is a subset of artificial intelligence (AI) that enables computers to learn and make data-based decisions, improving performance over time without being explicitly programmed. It uses statistical techniques to identify patterns in structured and unstructured data to make predictions or decisions.
Data lakes: Due to its variety and volume, unstructured data can be stored in data lakes or where the data is created (at “the edge”). Data lakes are suited to large volumes of various types of data. Data lakes accommodate data in native format, so video, audio, text, and documents can all be stored together.
Content management systems (CMS): As an application, CMS enables businesses to store, retrieve and search, index, and publish unstructured data on the web.
How organizations leverage unstructured data
Organizations in various industries leverage unstructured data in numerous ways. From healthcare to manufacturing, unstructured data enables organizations to provide better service based on insight.
The healthcare industry benefits from unstructured data at various layers of operation. A sophisticated chatbot can enable healthcare professionals to understand speech patterns to indicate a specific illness. A health logging app can help identify health risks when the data is processed. By merging unstructured data with structured data, health professionals can derive patient care outcomes.
Predictive data analytics are crucial to the world of finance to track market trends and shifts. This intelligence allows organizations to adjust accordingly. On a granular level, unstructured data is used to create documents for loans, mortgages, business plans, and contracts. Unstructured data analysis also supports the fight against financial crime. Organizations can identify fraudulent signatures, or identify and respond to phishing scams.
For public sector organizations, data is a strategic asset. Organizations can maximize their value to decrease costs, simplify operations, and reduce tool and data sprawl with a holistic data strategy that integrates cybersecurity, logging, and AIOps.
Telecom companies are able to get more out of data by breaking down silos to deliver telco-as-a-service and improve the availability of the network. By putting unstructured data to work, they can deliver faster data analysis and automate processes to deliver better customer experiences.
Data mining and predictive data analytics are common marketing practices used to identify and understand market opportunities and trends, customer needs, and customer behavior and intent. Marketing professionals generate and consume unstructured data to better communicate with customers and ultimately improve customer experience.
Unstructured data, such as plans, models, and blueprints, is a necessary component of manufacturing practices. The ability to manage and analyze unstructured data in agriculture can help predict and manage yields. The automotive industry relies on unstructured data to understand and meet demand.
As the technology to manage and analyze unstructured data evolves, so will the ability of organizations to make use of their unstructured data.
Future trends of unstructured data
Recent artificial intelligence (AI) and machine learning (ML) developments are ushering in a new era for the use of unstructured data. As AI and machine learning technology develop, so does the ability to process unstructured data and merge structured data with unstructured data for better business insights.
As new ways of capturing data are developed, the applications of unstructured data continue to grow. Facial recognition is already commonplace to most smartphone users. Facial recognition technology developments now enable emotion recognition, which can be key in healthcare and customer service.
As virtual personal assistant technology becomes readily available, unstructured data will also help to increase productivity. Certain tasks are automated so users can improve efficiency and output. With virtual personal assistants, doctors can spend more time with patients and less time filling out paperwork.
Manage and analyze unstructured data with Elastic
As you bring in unstructured data, you can process and apply a structure that allows you to use it. Elastic provides a number of unstructured data management solutions.
Elasticseach Relevance Engine for AI delivers organizations with a powerful set of tools for building AI-powered search applications that utilize unstructured data.
Discover Elasticsearch to store, search and analyze your unstructured data for use cases including search, observability, and security.
1 "The Future of Data Revolution will be Unstructured Data" by Priya Dialani, Analytics Insight, October 2020, https://www.analyticsinsight.net/the-future-of-data-revolution-will-be-unstructured-data/ (Accessed June 1, 2023)