Unstructured data is information that does not follow a predefined format. It can be found in various formats such as emails, PDFs, images, audio, video, and social media posts. This data is stored in formats that don’t conform to traditional relational database structures. It is often used for generative artificial intelligence (AI) models and natural language processing (NLP) to enable a model to learn from rich and diverse datasets.

AI models use unstructured data to identify hidden patterns or trends, or to extract meaningful patterns, topics, or classifications. The data remains in an undefined format until it needs to be transformed or converted. It is usually more complex to organize, process, and analyze unstructured data than structured. The datasets are large in quantity and include a wide variety of information which can evolve.

Various industries use unstructured data, such as healthcare, for its predictive analysis capabilities in supporting patient diagnostics, or retail for providing personalized marketing recommendations.

Why is unstructured data important?

Unstructured data is useful in training AI models. Because the data reflects real-world interactions and language, it can help AI models generate more contextually relevant and accurate outputs for enterprise applications. It also enhances an AI model’s training process by allowing the model to use a wide variety of data sources to perform tasks such as data classification and summarization.

There are occasionally valuable insights within unstructured datasets that might not have otherwise been detected, such as unknown or atypical relationships between data points. This is especially helpful in business intelligence (BI) and predictive analytics. 

As unstructured datasets are constantly increasing in volume, they enable new insights to be derived from the dataset and highlight potential enterprise growth. It can help organizations to understand customers better by analyzing customer sentiment based on real-world information. 

This insight can be used to develop new services and improve existing ones such as customer support by using real-time customer feedback. It is estimated that 80–90% of data is unstructured. This means there is a wealth of data that is ready to be analyzed whenever it is required.

Key features of unstructured data

Unstructured data has several key features which affect the way it is processed and used, such as data complexity and insight quality.

No fixed format or schema

Rows and columns that are typically used when organizing data are absent from unstructured data. It does not follow a fixed format or schema. This means it is flexible in its applications and can be organized dynamically. The process for organizing unstructured data is typically more time-consuming as it requires more effort to define categories.

Diverse formats

There are many diverse formats of unstructured data such as text (emails and reports), images, videos, audio, PDFs, and social media posts. This variation in formats can make data analysis complex. 

Context-dependent meaning

Context-dependent meaning refers to interpreting the context, nuance, and relationships within the dataset. For example, the tone or sentiment of text, or interpreting different objects and their relationships within an image. Traditional tools struggle to accurately parse this information and assess its relevance. 

Difficult to analyze without AI

Efficient analysis of unstructured data requires advanced technologies such as NLP, computer vision, or machine learning (ML). This is because the data lacks a set framework and is disorganized when raw. Using AI for analysis ensures extracted insights are accurate, relevant, and usable in enterprise applications. 

Rich but noisy

Variation in data formats can complicate data processing and analysis. Unstructured data contains valuable insights and irrelevant or redundant information that must be filtered. For example, an unstructured dataset can support analysis of customer sentiment or trends, but also include images that have no effect on final output.

What are the benefits of using unstructured data?

There are multiple benefits associated with using unstructured data. 

Flexibility

Data that is unstructured is highly flexible and can be used in various ways. This makes the data adaptable for different scenarios and has multiple practical applications such as for customer insights or personalized marketing.

Diversity

Unstructured data usually comprises various file formats, enabling insights from a wide range of sources. As there are no restrictions in data sources, the information can be used in different  ways to generate outputs. Diversity in data can enrich insights and provide hidden value if there are trends or patterns within the set that were not previously identified.

Detailed information

Due to variation in data formats, unstructured data typically contains more detailed and granular information. This includes nuances, sentiments, and specific details that may not be captured when using structured data. The level of detail allows for greater analysis to enrich the depth and accuracy of outputs.

Analysis

Deeper analysis can be performed using unstructured data as it does not follow a rigid schema. Tools such as artificial intelligence, including machine learning, can highlight unknown patterns, trends, or relationships in datasets to provide insights that would not be identified by a human. This process can also be automated and achieved quicker when using AI.

What are the challenges of using unstructured data?

Unstructured data can present several common challenges. It requires advanced tools and techniques to organize and prepare it for use.

Data quality

Data is taken from a wide variety of sources, meaning it is typically disorganized, and the quality inconsistent. Poor data quality can negatively affect analysis and output. Using low quality data can lead to inaccurate and unreliable insights.

Data management

Data management and storage when using unstructured data can be complex. This is due to the size of the dataset constantly changing. This makes it difficult to appropriately store the data, requiring a large amount of storage, and scaling it as required. 

Complex analysis

Complex or sophisticated analysis is usually required to derive insights, as the data does not follow a defined, or rigid, schema. This is also due to the variety of formats that comprise a dataset. Analysis can be achieved using specialized tools like AI techniques, including deep learning or neural networks.

Security and compliance

The variety and volume of unstructured data can introduce security concerns and compliance risks. Improper storage or handling of data may lead to violations of industry regulations such as GDPR and HIPAA.

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