What is unstructured data?
Unstructured data is information, in many different forms, that doesn't follow conventional data models, making it difficult to store and manage in a mainstream relational database.
The majority of new data generated today is unstructured, prompting the emergence of new platforms and tools to manage and analyze this data. These tools let organizations more easily use unstructured data for business intelligence (BI) and analytics applications.
Unstructured data has an internal structure but doesn't contain a predetermined data model or schema. It can be textual or nontextual, human-generated or machine-generated.
Text is one of the most common types of unstructured data. Unstructured text is generated and collected in a range of forms, including Word documents, email messages, PowerPoint presentations, survey responses, transcripts of call center interactions, and posts from blogs and social media sites.
Other types of unstructured data include images, audio and video files. Machine data is another category of unstructured data that's growing fast in many organizations. For example, log files from websites, servers, networks and applications -- particularly mobile ones -- yield a trove of activity and performance data. In addition, companies increasingly capture and analyze data from sensors on manufacturing equipment and other devices connected to the internet of things (IoT).
Structured vs. unstructured data
The main differences between structured and unstructured data are the types of analysis the data can be used for, the schema used, data format types and the ways the data is stored. Traditional structured data, such as transaction data in financial systems and other business applications, conforms to a rigid format to ensure consistency in processing and analyzing it. Sets of unstructured data, on the other hand, are maintained in formats that aren't uniform.
Structured data is stored in a relational database that provides access to data points that are related to one another using columns and tables. For example, customer information kept in a spreadsheet and categorized by phone numbers, addresses or other criteria is considered structured data. Other examples of structured data systems include travel reservation systems, inventory registers and accounting remittances.
As this information is categorized, it's considered to be more searchable by both humans and algorithms in data analysis. Database administrators often use structured query language (SQL), which enables effective search queries of structured data in relational databases.
Structured and unstructured data are often used together. For example, a structured spreadsheet of customer data could be imported into an unstructured customer relationship management system.
What is unstructured data used for?
Because of its nature, unstructured data isn't suited to the transaction processing applications that often handle structured data. Instead, it's primarily used for BI and analytics.
Customer analytics is a popular application of unstructured data. Retailers, manufacturers and other companies analyze unstructured data to improve customer experience and enable targeted marketing. They also do sentiment analysis to better understand customers and identify attitudes about products, customer service and corporate brands.
Predictive maintenance is an emerging analytics use case for unstructured data. For example, manufacturers can analyze sensor data to detect equipment failures before they occur in plant-floor systems or finished products in the field. Energy pipelines are monitored and checked for potential problems using unstructured data collected from IoT sensors.
Analyzing log data from IT systems highlights use trends, identifies capacity limitations and pinpoints the cause of application errors, system crashes, performance bottlenecks and other issues. Unstructured data analytics also aids regulatory compliance efforts, particularly in helping organizations understand what corporate documents and records contain.
Unstructured data techniques and platforms
In the past, unstructured data was often locked away in siloed document management systems, individual manufacturing devices and the like. That approach made unstructured data into what's known as dark data, unavailable for analysis.
But things changed with the development of big data platforms, primarily Hadoop clusters, NoSQL databases and the Amazon Simple Storage Service (S3). They provide the required infrastructure for processing, storing and managing large volumes of unstructured data without the need for a common data model and a single database schema.
Challenges of unstructured data
There are several challenges associated with unstructured data. The most common include the following:
- Storage requirements. Unstructured data often consumes large amounts of storage because of its diverse formats, such as audio, video and multimedia files.
- Data management complexity. Managing unstructured data across different repositories and file systems can be challenging without specialized tools.
- Analysis difficulty. Extracting valuable insights from unstructured data requires advanced technologies, such as generative artificial intelligence (AI) and natural language processing (NLP).
- Integration issues. Integrating unstructured data with structured data in data warehouses or data lakes can be complex and difficult to execute.
- Real-time processing. Handling real-time unstructured data, such as live social media feeds, demands infrastructure and sophisticated algorithms.
Examples of unstructured data
There are several different kinds of unstructured data. The most common include the following:
- Audio files, such as podcasts and recordings.
- Social media posts, including tweets, Instagram updates and Facebook statuses.
- Text documents and text files, such as reports, articles and PDFs.
- Images, videos and other multimedia formats.
- Web pages containing dynamic and varied content.
- Emails and correspondence.
- Real-time data streams, such as IoT device outputs.
- Chatbot conversations and NLP-processed text.
How to manage unstructured data
There are several ways to successfully manage unstructured data. The most important steps include the following:
- Data lakes. Unstructured data can be stored in a data lake alongside structured data sets for improved accessibility.
- Advanced tools. Technologies like generative AI, NLP and other data science techniques are used to process and analyze unstructured data.
- Cloud storage. Cloud-based data storage offers scalability for unstructured data.
- Metadata. Well-defined metadata makes indexing and searching of unstructured data easier.
- Automated processes. Automation tools streamline data ingestion, categorization and analysis.
- Data sources. Connect unstructured data sources with structured systems for comprehensive analytics and reporting.
- File systems. Regularly reviewed and optimized file systems ensure unstructured data is stored efficiently.
What is semistructured data?
Semistructured data is largely unstructured, but it uses internal tags and markings that separate and differentiate various data elements, placing them into pairings and hierarchies. Semistructured and unstructured data are often compared, but they're different.
Email is a common example of semistructured data. The metadata used in an email enables analytics tools to easily classify and search for keywords. Sensor data, social media data and markup languages like XML and NoSQL databases are examples of unstructured data that are evolving for greater searchability and can be considered semistructured data.
Next-generation unstructured data analysis tools
A variety of analytics techniques and tools are used to analyze unstructured data in big data environments. Other techniques that play roles in unstructured data analytics include data mining, machine learning and predictive analytics.
Text analytics tools look for patterns, keywords and sentiment in textual data. At a more advanced level, NLP technology is a form of AI that seeks to understand meaning and context in text and human speech, increasingly with the aid of deep learning algorithms that use neural networks to analyze data.
Newer tools aggregate, analyze and query all data types to enable greater insight into corporate data and improved decision-making. Examples include Azure Data Services, IBM Cognos Analytics, Microsoft Power BI and Tableau.
According to Gartner, the storage of unstructured data is expected to increase in the future.
Unstructured data is a fast-growing form of data. Learn how to manage this type of data to boost business performance.
