Data Management Glossary
AI Data Management
What is AI Data Management?
- Finding and curating the right data
- Moving and preparing data for AI pipelines
- Ensuring data is high-quality, compliant, and properly tagged (see data tagging)
- Optimizing where data is stored and how it’s accessed
- Tracking data lineage and governance for responsible AI
What is the Role of Unstructured Data in AI Data Management?
- LLMs → text, emails, reports
- Multimodal AI → images + text + video
- AI search & retrieval → documents, PDFs, data lakes
- AI-powered compliance → identifying sensitive files and preventing AI data leakage
Komprise for AI Data Management
Data Discovery & Curation
- Find and classify relevant data for AI projects
- Tag and enriches data so AI models can understand it
Data Mobility & Preparation
Data Tiering & Cost Optimization
Storage-Agnostic Metadata Catalog
Governance & Compliance
AI Data Ingestion
What is AI data ingestion?
AI data ingestion is the process of discovering, collecting, and delivering data into AI and machine learning pipelines for training, inference, or retrieval-augmented generation (RAG). It often includes pulling data from file storage, object storage, cloud repositories, and enterprise systems.
Glossary Definition: AI Data Ingestion.
Why it matters:
AI outcomes depend on having access to the right data. Without efficient ingestion, projects stall due to fragmented storage, poor visibility, and slow data access.
How Komprise helps:
Komprise provides a global view of unstructured data across silos, helping organizations quickly identify, move, and prepare the right data for AI initiatives.
AI Data Preparation
What is AI data preparation?
AI data preparation is the process of cleaning, organizing, enriching, and filtering data before it is used by AI models. This can include removing duplicates, classifying files, adding metadata, and selecting relevant datasets.
Glossary Definition: AI Data Preparation.
Why it matters:
Poor-quality data leads to poor AI results. Effective preparation improves model accuracy, speeds training, and reduces wasted compute resources.
How Komprise helps:
Komprise uses analytics and metadata to identify valuable datasets, eliminate stale or redundant files, detect sensitive data and automate workflows that make unstructured data AI-ready.
Unstructured Data for AI
Why is unstructured data important for AI?
Unstructured data includes documents, images, videos, emails, PDFs, and logs. It represents the majority of enterprise data and contains valuable business knowledge, customer insights, and operational context.
Why it matters:
Modern AI models and GenAI systems rely heavily on unstructured data to improve relevance, accuracy, and business context.
How Komprise helps:
Komprise enables organizations to find, classify, mobilize and manage unstructured data at scale so it can be securely used for AI and analytics.
Glossary Definition: Unstructured Data AI
RAG Pipelines
What is a RAG pipeline?
A Retrieval-Augmented Generation (RAG) pipeline combines AI models with enterprise data retrieval. Instead of relying only on model training, it retrieves relevant documents or files in real time and uses them to generate more accurate responses.
Glossary Definition: RAG pipelines
Why it matters:
RAG improves AI accuracy, reduces hallucinations, and keeps answers grounded in current enterprise data.
How Komprise helps:
Komprise helps power RAG pipelines by indexing unstructured data across environments, enabling fast search, metadata filtering, and access to the most relevant enterprise content.
AI Cost Optimization
What is AI cost optimization?
AI cost optimization is the practice of reducing the infrastructure, storage, and compute costs associated with AI workloads while maintaining performance and outcomes.
Glossary Definition: AI Cost Optimization
Why it matters:
AI projects can become expensive due to GPU demand, storage growth, data movement, and inefficient pipelines. Controlling costs is essential for scaling AI successfully.
How Komprise helps:
Komprise lowers AI costs by tiering inactive data off expensive storage, reducing unnecessary data movement, and ensuring only relevant, high-value data is used in AI workflows.