Home Learn Unstructured Data Demystifying Unstructured Data Management Harnessing unstructured data empowers businesses to uncover rich insights from intricate sources such as emails, social media, and multimedia files. Request demo Unstructured Data Overview Definition Challenges Unstructured Data Management Related Terms Resources Overview Data Powers Everything in Today’s Digital Business World Data provides the raw material for informed decision-making, and it allows businesses to identify patterns, spot trends, make strategic choices, and predict future outcomes. That is, of course, if the data can be aggregated and analyzed intelligently. Not all data is created equal, and unstructured data is a great example of this. Unstructured data is information that doesn’t follow a predefined format and lacks a consistent data model that is easily organized into traditional database tables, making it difficult to search, sort, and analyze using traditional methods. Common sources of unstructured data include text documents, emails, social media posts, images, audio files, and video files. These types of data are often stored in data lakes or blob storage systems designed to handle large volumes of diverse data, which also can strain storage resources. While unstructured data can be difficult to process, it also promises access to rich insights on customer sentiment, user behavior, market trends, and emerging trends. Wrangling non-standard data formats can often challenge organizations, but businesses will miss out on critical information that can drive strategic business decisions and growth if they neglect to manage unstructured data. definition What is Unstructured Data? Unstructured data can take on many shapes and formats, which is why it introduces challenges for many enterprise organizations. But also due to its nature, unstructured data can contain a wealth of meaningful insights into customer intentions, user behavior patterns, and critical business information that could help shape decisions and drive growth. According to industry watchers, approximately 80% of the world’s data will be unstructured by 2025. Unstructured data lacks a predefined structure and can vary greatly in style and content. That means every organization today has unstructured data in its domain. Here are a few examples: • Documents: PDFs, PowerPoint presentations, text files, body copy in emails, and more types of documents are considered to contain unstructured data. • Social media posts: Updates across social networks, comments on corporate blogs, and community discussions on web pages also meet the unstructured data definition. • Media files: Video files and audio recordings contain unstructured data and require specialized approaches and skills to extract the meaningful data for greater analysis. Unstructured data by definition can be difficult to define because it varies from different sources. But with no specific format or organization, it is difficult to process and analyze. Challenges Challenges of Unstructured Data As promising as unstructured data can be, clearing the hurdles of properly processing and managing the data is critical for businesses hoping to gain critical insights from these data sources. • Data Volume and Variety It may seem obvious, but the amount of unstructured data and the differing types are the first challenge in managing unstructured data. • Management Complexity Because it lacks a consistent format, unstructured data is difficult for organizations to classify, categorize, and manage effectively. Often organizations must rely upon and invest in specialized tools and expertise to properly process and analyze unstructured data. • Data Quality Issues Unstructured data varies significantly in accuracy, relevancy, and quality, which can lead to inaccurate analysis and insights gleaned from it. It is imperative that unstructured data is carefully collected, cleaned, correlated – and validated – due to its very diverse nature. • Storage and Scalability Concerns The volume of unstructured data generated across an organization can overwhelm traditional storage systems, and in some scenarios, it could require advanced infrastructure to scale and manage the large data sets adequately. • Analysis Sophistication Unstructured data also represents challenges with data analysis. Extracting meaningful insights from it often requires sophisticated approaches such as natural language processing (NLP) and machine learning. Both of these techniques can be extremely resource-intensive and require specialized skills. • Security and Privacy Worries Because it doesn’t follow the standard formats of other data sources, unstructured data potentially can contain hard-to-distinguish sensitive data that can be difficult to protect. This raises concerns over increasing risks around data breaches as well as falling out of compliance with privacy regulations. • Data Integration Struggles Integration can become complex when the unstructured data is collected alongside structured data. To make sense and extract meaning from the varied sets of data likely will require data transformation, normalization, and mapping processes to create a unified view and to conduct comprehensive analysis. Unstructured Data Management Mastering Unstructured Data Management To successfully manage unstructured data, organizations must put several processes in place. From conducting deduplication and compression to applying data governance, unstructured data management requires a well-documented process that leverages NLP and machine learning to unlock valuable insights from the disorganized data. Here are some steps that can help: 1. Data discovery and identification helps organizations locate all the sources of unstructured data across an environment to get an accurate picture of the volume and types of data. 2. Metadata enrichment adds descriptive tags to the data to provide context and improve searchability of the data. Some metadata tags can include file type, creation date, author, and overall subject matter. 3. Data lakes and other data storage solutions can be put in place to handle the large volume of diverse data types and remove the need for traditional relational databases. 4. Data cleansing and standardization helps organizations address inconsistencies and errors within unstructured data, which will ultimately improve the data quality and reliability for future analysis. 5. Applying advanced analytics such as machine learning and NLP as well as text-mining techniques enables organizations to extract meaningful insights from the unstructured data sets. 6. Establishing data governance policies and enforcing compliance are critical steps in managing unstructured data. The policies and procedures will enable organizations to control access, manage data quality, and follow regulatory requirements related to sensitive unstructured data. Understanding unstructured data management involves comprehending how to effectively protect, manage, and access diverse file-based data such as documents, emails, and images across various systems. It requires utilizing tools that perform backup, archiving, and data governance as well as capabilities that tackle granular recovery, deduplication, and indexing to optimize storage. It is critical to enable effective search and retrieval of unstructured data while also adhering to compliance requirements. Unstructured data represents an untapped well of meaningful insights into customers, trends, and the business, and businesses cannot afford to ignore the value buried in the data. Related Terms What is File and Object Backup? File and object backup solutions offer data protection for two different unstructured data types: object storage and file systems. Learn more What is Data Loss Prevention? Data protection is everyone’s job, not just an IT function. Data loss prevention (DLP) is part of a company’s security policy to prevent the loss, leakage, misuse, or access of data by unauthorized parties. Learn more What is Data Protection? Data protection refers to the practices, technologies, and policies that are used to safeguard data against unauthorized access, loss, corruption, and other threats. Learn more related resources Explore related resources View all resources video Improve unstructured data management with Metallic File and Object Archive Service Demo Datasheet Cyber Resilience for File and Object Data Informed data placement for acompliance-ready archive. 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