Data Management Glossary
Data Curation
Data curation is the process of organizing, managing, and maintaining data so that it remains accurate, accessible, and useful over time. It involves not just storing data, but also enhancing the value of data through activities such as cleaning, validation, annotation, integration, and preservation. Data curation for unstructured data (text documents, images, videos, audio files, emails, social media posts, etc.) refers to the process of organizing, enriching, and managing data that doesn’t have a predefined structure (such as tables or databases).
Data Curation of Unstructured Data
- Data Ingestion: Collect data from various sources (e.g., sensors, emails, social media, scanned files).
- Data Classification: Identify and categorize data by type, source, or topic using AI/NLP tools or manual tagging.
- Metadata Enrichment: Add metadata (e.g., author, timestamp, topic, language, sentiment) to help organize and retrieve the data.
- Data Cleaning: Remove noise or irrelevant parts (e.g., removing stop words from text, trimming silence from audio).
- Content Extraction: Use tools to extract meaningful information: (OCR (Optical Character Recognition) for scanned documents, speech-to-text for audio, NLP for summarizing or tagging text.
- Data Annotation: Label parts of the content for AI training or classification (e.g., tagging entities, labeling emotions in text).
- Indexing and Storage: Organize the data in searchable repositories using data lakes, NoSQL databases, or content management systems. See Global File Index.
- Access Control and Governance: Apply rules to manage who can access the data and how it can be used.
- Preservation and Versioning: Archive the data, ensure format sustainability, and track versions over time.
Growing Importance of Proper Data Curation of Unstructured Data
As the category of unstructured data management emerges, enterprises are increasingly looking for data curation and data classification strategies to:
- Unlocks Insights: Makes dark data (unused unstructured data) useful for analysis and decision-making.
- Support AI & ML Initiatives: Clean, labeled unstructured data is critical for training machine learning models.
- Improve Searchability: Helps users and systems find relevant content faster.
- Ensure Compliance: Helps meet legal or regulatory obligations related to data management.
Data curation for unstructured data transforms messy, raw information into a structured, searchable, and valuable resource. It combines technical tools (like UDM, NLP and OCR) with careful organization and governance to make unstructured data usable and meaningful.
