Data Management Glossary
Unstructured Data Governance
Unstructured data governance is a growing practice in enterprise IT as data volumes have exploded and organizations need to manage data assets to reduce risks and costs and ensure data is discoverable for new uses. Unstructured data includes text documents, emails, images, videos, social media posts, audio files, sensor data and other data types that do not fit neatly into traditional structured databases. Unlike structured data that can be organized into tables and fields, unstructured data lacks a predefined format, making it challenging to manage, search, and mine for new insights.
An unstructured data governance strategy can involve many components:
- Data Discovery and Inventory: Organizations need to identify and catalog unstructured data to manage it properly. This involves locating data stored across various repositories, including file shares, cloud storage, email systems, and more. A thorough inventory delivers holistic visibility into data assets to inform decision-making.
- Data Classification and Tagging: IT managers need the ability to tag and segment unstructured data based on its sensitivity, importance, and relevance to the organization. This includes tagging data with metadata that indicates details such as owners, purpose or project, security (such as containing PII), compliance requirements and other identifying characteristics of the file contents.
- Access Control and Security: Implementing access controls ensures that only authorized individuals can access and modify sensitive unstructured data. This involves defining user roles, permissions, and authentication mechanisms to safeguard the data from unauthorized access or breaches.
- Data Retention Policies: Organizations need to establish policies that dictate retention policies for unstructured data. Doing so helps ensure compliance with legal and regulatory requirements, lowers the risk of retaining unnecessary data and lowers costs of data storage and backups.
- Data Privacy and Compliance: Data privacy regulations such as GDPR, HIPAA, or CCPA require proper handling and protection of personal and sensitive data. Unstructured data governance includes procedures to ensure compliance with these regulations—such as how and where regulated data is stored.
- Data Lifecycle Management: This involves managing data from creation to deletion. It includes processes for capturing, storing, migrating, archiving, and deleting unstructured data as its needs and value to the organization change.
- Search and Discovery: Deep search capabilities aided by metadata and content indexing help users find relevant unstructured data quickly.
- Data Analytics and Insights: Extracting valuable insights from unstructured data requires tools and techniques for data analysis, such as natural language processing (NLP), text mining and sentiment analysis.
- Data Stewardship: Assigning data stewards responsible for managing and overseeing specific sets of unstructured data can help ensure that data is properly maintained, accurate, and up-to-date.
- Monitoring and Auditing: Regularly monitoring and auditing unstructured data governance processes is important for compliance, security, and to reduce risks and improve outcomes from analytics and AI initiatives.
Read more in the blog on data governance tips for generative AI.
Unstructured data governance is critical for maintaining data quality, security, compliance, and deriving meaningful insights from the vast amounts of unstructured data that organizations generate and store. Proper governance practices contribute to better decision-making, reduced risks, and improved overall unstructured data management.
Learn how Komprise is bringing new data governance features to its unstructured data management solution in this blog.