Unstructured data examples, applications and use cases

Unstructured data use cases are scenarios in which organizations extract value from information that doesn’t fit neatly into rows and columns. Examples include text files, social media posts, multimedia files and more.

In the era of big data, organizations generate and collect large volumes of raw data and information from a wide range of sources such as webinars, documents and digital interactions. Most of that data is unstructured: free-form content such as meeting transcripts, sensor readings and video footage that defy traditional tabular format.

While structured data—information neatly stored in spreadsheets, relational databases and predefined schemas—remains critical, being data-driven extends beyond rows and columns. In fact, analysts estimate that up to 90% of enterprise-generated data falls into this unstructured category.¹ For many organizations, that’s a vast reservoir of untapped “dark data,” ripe with potential but locked behind complexity.

By applying technologies like artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) to unstructured datasets, organizations can optimize data management and turn previously trapped information into valuable insights.

Enterprise data generally falls into three types of data categories based on its format and the presence (or absence) of a predefined schema: structured, semi-structured and unstructured.

Structured data is highly organized in predefined formats and stored in tabular form, such as Excel spreadsheets or relational database management systems (RDBMS). It follows a strict schema, making it easy to query using structured query language (SQL). Examples include customer names, product IDs and phone numbers.

Semi-structured data doesn’t follow a fixed schema but still contains metadata, tags or semantic markers that help systems interpret it. This includes data formats like XML, JSON and CSV files which are often exchanged through application programming interfaces (APIs), stored in NoSQL databases like MongoDB or maintained within data lakes.

Unstructured data lacks a consistent format, making it difficult to store in traditional rows and columns. Examples of unstructured data include both textual and nontextual content: social media posts, audio files, text documents, sensor data from Internet of Things (IoT) devices and JPEG or PNG images. Often, this type of data requires AI-powered tools and analytics platforms to extract meaningful insight at scale.

While high-quality structured data remains essential for reporting and transactional systems, unstructured and semi-structured content make up the majority of business intelligence today. Together, these types of data expand the scope of an organization’s data analysis, integration and automation.

While structured data can be neatly charted and analyzed, unstructured data is less like a spreadsheet and more like a landscape: vast, unpredictable and full of potential. With the right data analytics tools, organizations can map that terrain and uncover meaning in places traditional data processing can’t reach.

More specifically, unstructured data creates value through various use cases, including:

• AI performance
• Operations and automation
• Customer and user insight
• Risk, compliance and governance
• Public and societal intelligence

Unstructured data drives innovation in AI by powering model training, optimization and governance. When leveraged effectively, it can help enhance accuracy, context and adaptability across next-generation AI systems.

Operational processes often rely on documents and artifacts traditionally interpreted by humans. Automation through AI-powered tools can help eliminate bottlenecks and address some of the challenges of unstructured data, such as manual entry and data structures. 

Unstructured data reflects how people communicate, capturing customer feedback in ways structured data typically can’t. By embedding data models with qualitative insight and structured data, organizations can elevate both customer experiences and service outcomes.

Regulated industries such as finance and healthcare rely on unstructured data sources to help detect and respond to threats. A strong unstructured-data governance practice can help organizations balance access with data security, protecting trust and reputation as they scale.

Unstructured data powers real-world impact beyond the enterprise, enabling faster decision-making and more resilient communities. From monitoring ecosystems to identifying emerging public health trends, these use cases demonstrate how data-driven intelligence serves the broader good.

Insights don’t happen by accident. Organizations that treat unstructured data as a living asset often follow a lifecycle that moves from discovery to deployment, with governance embedded at every step. The following six steps outline how organizations can operationalize unstructured data and capture trapped business value:

Organizations can begin by taking inventory of their unstructured data sources. This process can include mapping data across on-premises and distributed file systems, including cloud-based repositories and data warehouses, to provide full visibility. Assigning stewardship for this work helps maintain accountability and ensure alignment with business priorities.

As data volumes grow, organizations can benefit from data lakes and cloud storage platforms that support scalability in both volume and variety. These storage solutions retain data in its native format, allowing data engineering teams to reuse and analyze information without costly conversion or loss of fidelity. Solutions such as Azure Blob Storage, IBM Cloud Object Storage or open source solutions like MinIO provide scalable data storage for unstructured content that requires access or reuse over time.

Strong data governance ensures that information remains trustworthy and compliant throughout its lifecycle. Organizations can apply metadata to classify and track data assets or use AI-powered analytics tools to manage quality control. Embedding automation into this process can also help standardize review cycles and maintain integrity across all datasets.

Linking contextual cues such as text, audio and video files with operational metrics can provide a more complete view of performance and risk. Organizations often connect structured and unstructured inputs through unified data models and automated data pipelines, supporting consistent analysis and faster decision-making.

Insights only deliver value when activated. Organizations can embed business intelligence into real-time workflows—rather than static dashboards—by using automation and AI agents. Operationalizing analytics and AI in this way can help ensure that discoveries lead directly to measurable outcomes.

As capabilities mature, organizations can evaluate outcomes and expand successful initiatives. Tracking datasets, algorithms and use cases helps maintain alignment with organizational goals, while continuous improvement reinforces unstructured data management as a long-term competency.

Unstructured data is no longer an inconsequential byproduct of everyday business. Instead, it’s a core driver of innovation, automation and smarter decision-making. With architecture and governance as their compass, organizations can navigate the hidden terrain of unstructured data—transforming insights into impact and complexity into a competitive advantage.