Companies are inundated with vast amounts of unstructured data—information that doesn’t fit neatly into rows and columns. From emails and social media posts to images and videos, unstructured data is expected to account for 80% of global data by 2025.
Despite its abundance, this data often remains untapped due to its sophisticated, complex nature. Artificial intelligence (AI) can offer powerful tools to analyze, interpret, and extract actionable insights from unstructured data.
In this article, we’ll explore the nature of unstructured data, how it is used, and how AI is revolutionizing its management and analysis.
What Is Unstructured Data?
Unstructured data doesn’t conform to a predefined data model or format. As a result, it’s challenging to store and analyze using traditional relational databases. Unlike structured data—which is neatly organized in tables—unstructured data can be chaotic and complex, existing in various formats like text, images, videos, audio files, and sensor data.
Examples of unstructured data include text documents like emails, reports, and social media posts; multimedia files such as photos, videos, and audio recordings; sensor data from IoT devices and GPS logs; and human communication like conversations, feedback, and reviews.
This data is highly valuable because it often contains rich, context-driven information that can provide deep insights into customer behavior, market trends, and operational efficiency. However, extracting these insights requires advanced tools and techniques—such as AI.
Unstructured data has many purposes across various industries. Unstructured data generally falls into two categories: human-generated and AI-generated.
Human-Generated Unstructured Data
Human-generated unstructured data includes customer sentiment analysis, where businesses use social media posts, reviews, and feedback to gauge customer satisfaction and sentiment. This data is essential for developing customer-centric strategies and improving product offerings.
In market research, text documents, reports, and articles provide valuable insights into market trends, competitor activities, and industry developments. Companies use this information to stay ahead of the competition.
Additionally, legal documents, contracts, and compliance reports contain unstructured data that businesses must manage and analyze to ensure adherence to regulations and standards.
AI-Generated Unstructured Data
AI-generated unstructured data includes outputs from generative AI models like GPT-4 and DALL-E, which generate text, images, and videos used in content creation, marketing, and even product design.
AI algorithms also analyze unstructured data and generate insights, predictions, and recommendations. For example, AI can analyze customer interactions to predict churn or identify upselling opportunities.
How is Unstructured Data Stored?
Storing unstructured data requires flexible and scalable storage solutions that can handle many data types and sizes.
Data lakes are large storage repositories that hold structured, semi-structured, and unstructured data in their raw form. They are scalable and allow for easy storage and retrieval of massive amounts of data. Tools like Hadoop and cloud-based services like AWS S3 can help with data lakes
NoSQL databases can store unstructured and semi-structured data. They offer flexibility in data storage and can handle large volumes of data. Some good examples include MongoDB, Cassandra, and Couchbase.
Cloud storage services—i.e., Amazon S3, Google Cloud Storage, and Microsoft Azure—provide scalable storage options for unstructured data. They deliver high availability, data redundancy, and easy integration with AI and analytics tools.
Effective management of unstructured data involves the use of metadata, which provides descriptive information about the data, making it easier to search and retrieve. Additionally, indexing and tagging unstructured data improves accessibility and usability.
Unstructured Data vs. Structured Data
The main difference between Unstructured Data and Structured Data is in format and organization.
Structured data comes in rows and columns that are often held in relational databases. The best examples include customer databases, financial records, and sales data.
Structured data is more straightforward to study using traditional data analysis tools and techniques, which makes it excellent for generating reports, dashboards, and predictive models.
In contrast, unstructured data doesn’t have a predefined format; it also includes text, images, videos, and other multimedia content. As a result, it can be challenging to organize into rows and columns.
Likewise, analyzing unstructured data requires advanced tools—such as AI—to process and interpret for valuable insights. Companies often store unstructured data in data lakes, NoSQL databases, and cloud storage solutions.
Nonetheless, although structured data is easier to handle and analyze, unstructured data often contains richer, more complex information that—when analyzed correctly—can provide deeper insights.
Ideally, you must integrate both types of data to gain a comprehensive understanding of your operations and market.
The Role of AI in Unstructured Data Management
AI offers advanced capabilities that traditional methods simply can’t match. The technology helps you handle the complexity and scale of unstructured data more effectively. In return, it leads to improved insights and operational efficiency.
Here’s how AI can impact unstructured data management:
1. Rapid Processing of Unstructured Data
AI excels at processing vast amounts of unstructured data quickly and accurately. One of the key techniques is Natural Language Processing (NLP), which enables AI systems to understand and analyze text data.
NLP is brilliant at breaking down the meaning of the text, identifying key themes, and extracting relevant information.
Additionally, computer vision plays a crucial role because it lets AI interpret and analyze images and videos.
2. Categorization and Clustering
AI algorithms are incredible at categorizing and clustering unstructured data, which makes it easier to manage and retrieve. For example, AI can automatically group customer feedback into key categories: positive, negative, or neutral sentiment.
This classification helps businesses understand customer opinions and trends more comprehensively.
3. Enhanced Data Retrieval
AI-powered search tools enhance the retrieval of unstructured data. They understand the context and semantics of queries—which improves the accuracy of search results.
Unlike traditional search methods that rely on exact matches, AI-driven search tools can interpret the intent behind a query and deliver more relevant results.
4. Automation of Data Management Tasks
AI also automates many tasks associated with managing unstructured data; this reduces the need for manual intervention and increases operational efficiency. AI can automatically tag and classify documents, identify duplicates, and extract key information from unstructured sources.
Here are the most common industry applications of AI:
- Healthcare: In the healthcare sector, AI plays a pivotal role in organizing and analyzing medical records, imaging data, and patient feedback. AI systems can identify patterns in medical images, streamline patient records, and analyze patient feedback to improve care.
- Finance: In the financial industry, companies use AI to process and analyze a wide range of data—including financial reports, news articles, and social media content. AI algorithms can also detect trends, assess risks, and support investment decisions by analyzing vast amounts of data.
- Marketing: AI is also transforming marketing by analyzing customer interactions across various channels. Through AI-driven analytics, you properly observe customer behavior, preferences, and engagement patterns.
Can AI Learn from Unstructured Data?
Yes, AI can learn from unstructured data; however, it must use advanced techniques to draw meaning and generate insights.
For example, Natural Language Processing (NLP) enables AI to interpret human language. With sentiment analysis and topic modeling, AI can learn from customer reviews, social media posts, and emails.
Machine learning and deep learning also allow AI to recognize patterns in unstructured data. For instance, machine learning algorithms can analyze large datasets to identify trends, predict outcomes, and make recommendations.
AI uses computer vision to learn from image and video data; it can detect objects, recognize faces, and even interpret the emotions of individuals in photos or videos.
Learning from unstructured data presents challenges, including dealing with noise, variability, and complexity. Advancements in AI—such as pre-trained models and transfer learning—are helping overcome these challenges by enabling AI to effectively learn from diverse and complex datasets.
Here are 6 common practical examples:
1. Sentiment analysis: AI uses Natural Language Processing (NLP) to analyze customer reviews, social media posts, and emails to gauge public sentiment and emotional tone.
2. Topic modeling: AI applies NLP to categorize and understand the main themes and topics in large volumes of text data, such as customer feedback or news articles.
3. Trend identification: Machine learning algorithms analyze vast datasets to recognize emerging trends and predict future patterns based on historical data.
4. Recommendation systems: AI leverages deep learning to analyze user behavior and preferences, providing personalized recommendations for products, services, or content.
5. Image recognition: AI uses computer vision to detect objects, classify images, and identify patterns within visual data, such as categorizing photos or analyzing medical images.
6. Facial recognition: AI can recognize and verify individual faces in images or videos, enabling applications for security and personalized user experiences.
AI Techniques for Analyzing Unstructured Data
AI can operate several powerful techniques to analyze unstructured data. The 3 best options include:
1. Natural Language Processing (NLP)
NLP is a branch of AI that focuses on the interaction between computers and human language. You can use it to analyze text data by enabling machines to understand, interpret, and generate human language.
Applications of NLP include sentiment analysis, where businesses understand customer opinions and emotions by analyzing text data from reviews, social media posts, and surveys.
NLP also involves entity recognition, which identifies and categorizes entities within a text—such as names of people, organizations, dates, and locations—useful in organizing and retrieving information.
Language translation is another application, where NLP powers machine translation services. It allows real-time translation of text and speech between different languages.
2. Machine Learning and Deep Learning
Machine learning and deep learning are subsets of AI, which allow computers to learn from data and make informed decisions Examples include spam detection in emails or topic categorization in news articles.
Clustering is another application where AI groups similar data points together, including clustering customers based on purchasing behavior or segmenting markets.
3. Computer Vision
Computer vision allows AI to analyze and interpret visual data like images and videos. Applications of computer vision include object detection used in applications like self-driving cars, security systems, and retail analytics.
Facial recognition also uses AI to verify individual faces in images or videos. Companies use facial recognition in security, authentication, and personalization services.
In addition, image classification is another application where AI categorizes images into predefined classes. These may include identifying product images or classifying medical images for diagnostic purposes.
What Insights Can AI Extract From Your Unstructured Data?
- Sentiment analysis: AI can analyze customer feedback and social media posts to gauge public sentiment toward a brand, product, or service.
- Customer preferences: AI helps to understand customer needs and preferences by analyzing patterns in text data.
- Market trends: AI can detect emerging trends by analyzing news articles, blogs, and reports.
- Strategic decision-making: Insights from AI can help businesses make data-driven decisions.
- Brand monitoring: AI allows you to track and assess how a brand is perceived in the market.
- Competitive advantage: You can stay ahead of competitors by using AI-driven insights on market dynamics.
Artificial intelligence will play an integral role in the future of data, particularly unstructured data. The volume of this data—coupled with its complexity—presents challenges and opportunities for businesses across industries.
Data teams that embrace GenAI will lead this exciting new era, while those that don’t risk falling behind. AI’s rise may also enable data teams to perform ELT more efficiently, which is why I believe those who adopt a platform-based approach will excel in both ELT for analytics and AI initiatives.
Boomi Enterprise Platform is fully committed to this transformation, integrating generative AI into both our workflows and our product. We’ve reimagined the next generation of data pipelines to be AI-powered.
With our Copilot, you can easily connect to any data source that has a REST API endpoint.
Even if we don’t have a native connector for a specific source, we’ll still help you access all your data effortlessly, regardless of your data engineering expertise.
Stay tuned for our plans as we continue to integrate AI, enabling users to do more with their data.