Data Management Glossary
Data Indexing
Data indexing is the process of scanning, extracting, and organizing metadata from data assets so they can be easily searched, filtered, and analyzed, without moving or modifying the data itself.
Think of it like the index of a book: it doesn’t hold the content, but it tells you where to find it, and what’s inside.
In unstructured data environments, indexing captures:
- File name, path, size, type, and timestamps
- Ownership and access controls
- Content-specific metadata (e.g., PII, tags, custom attributes)
Why is Data Indexing Important for Unstructured Data Management?
Unstructured data (documents, images, videos, logs, etc.) lacks inherent structure. It’s scattered across silos—NAS, object stores, cloud storage —and traditional tools struggle to understand it.
Without data indexing, you’re flying blind. Indexing enables:
- Visibility across multi-vendor, multi-cloud environments
- Searchability without scanning petabytes manually
- Policy-driven actions like data tiering, deletion, archiving, or data tagging
- Audit and compliance by identifying sensitive or orphaned data
Data Indexing vs. Data Classification: What’s the Difference?
Data indexing is about knowing what you have. Data classification is about understanding what it means.
- Data Indexing: Organize and make data discoverable. It captures metadata (e.g., file size, type, date, access). The first step of indexing is during scanning.
- Data Classification: Group and label data based on type, sensitivity, etc. It typically captures content meaning (e.g., confidential, personal). Classification is often built on top of indexing.
Both are foundational for unstructured data governance and AI.
Data Indexing and AI Success
AI models rely on high-quality, well-prepared data. Indexing ensures:
- The right data can be found and curated
- Redundant or irrelevant data is filtered out (see ROT data)
- Sensitive or regulated data is handled properly
- Labeled or tagged datasets can be used for training AI/ML models
Without indexing, your AI initiatives waste compute on noise—or worse, expose your enterprise to risk.
How Komprise Does Data Indexing—and Why It Matters
Komprise uses a deep, distributed, storage-agnostic global file index (metadatabase) that:
- Crawls across NAS, cloud, and object stores
- Gathers both standard and custom metadata
- Does this in-place—without needing to move or copy data
- Supports tagging, search, and data workflows based on indexed attributes
Komprise Global File Index benefits include:
- Cost savings by identifying cold data to tier or delete
- Data-driven decisions about what to move to cloud or AI pipelines
- Improved compliance by surfacing stale, sensitive, or ownerless data
- Faster AI project execution by delivering relevant, labeled, and accessible data
Indexing and Unstructured Data Management
Data indexing is the foundation for making unstructured data usable and is recognized to be an essential ingredient to AI data readiness. AI data pipelines depend on having indexed, curated, and context-rich data. Komprise provides intelligent, in-place indexing to help enterprises reduce cost, manage risk, and fuel AI success—without being tied to any one storage vendor.