Data Management Glossary
AI Data Preparation
What is AI Data Preparation?
AI data preparation is the process of discovering, filtering, organizing, enriching, and delivering the right data, at the right time, to fuel AI and machine learning models. AI data prep includes:
- Data Discovery: Finding the relevant data across fragmented silos
- Data Curation: Selecting high-quality, representative, and useful datasets
- Metadata Enrichment: Adding context through metadata tagging, classification, and labeling
- Data Validation: Ensuring completeness, compliance, and quality
- Data Ingestion: Moving or virtualizing the data into AI pipelines or model training environments
While data preparation has traditionally focused on data that resides in structured databases, data warehouses and data lakes, the real challenge today is unstructured data, which lacks schemas, varies widely in format, and often lives in storage systems that are not visible or accessible to to AI teams.
AI is only as good as the data that feeds it
While much attention has been paid to structured data sources and traditional ETL data pipelines, the next frontier of AI innovation lies in unstructured data: documents, images, videos, logs, sensor files, etc. that are often reside in NAS devices in the enterprise, which make up more than 80% of enterprise data. Yet unstructured data is often overlooked, poorly governed, and underprepared, leading to delays, inaccuracies, and unnecessary costs in AI initiatives.
To deliver meaningful, enterprise-grade AI outcomes, organizations must rethink how they prepare, govern, and manage unstructured data for AI, starting at the source.
Why Unstructured Data Is Often Overlooked—But Critically Important
Many AI projects stall because teams spend too much time looking for, duplicating, or cleaning data rather than building or training models and driving business outcomes. Here’s why unstructured data preparation is often neglected:
- Hard to access: Locked in SMB/NFS/S3 file systems, often deep in cold storage or backups
- No schema: Lacks a clear structure, making it harder to classify or filter at scale
- Siloed ownership: Managed by IT or storage teams, not data scientists (see data silos)
- Metadata gaps: Missing context (who owns it, what’s in it, is it sensitive?)
And yet, unstructured data holds the richest signals for AI, including natural language, conversations, documents, imagery, and behavior logs that can feed foundation models, copilots, and predictive analytics.
Key Steps for Successful AI Data Preparation (Especially for Unstructured Data)
- Global Data Discovery & Visibility: Identify where unstructured data lives across hybrid storage environments—on-prem, cloud, archives, etc.
- Metadata Enrichment & Classification: Use intelligent tagging, NLP, and PII detection to classify data by content, owner, usage, and risk.
- Data Curation & Filtering: Avoid copying petabytes. Use smart filters (age, last access, sensitivity, project tags) to extract only what’s needed.
- Automated Data Movement or Virtualization: Migrate or tier selected datasets to AI pipelines or cloud environments, without disrupting users or production systems.
- Governance & Access Control: Ensure data access aligns with compliance, usage policies, and audit requirements.
- Iterative Refinement: Allow AI teams to request, refine, and improve data sets over time—ideally via governed self-service.
The Role of Komprise: From Storage Optimization to AI Enablement
Komprise was built to help enterprise IT organizations optimize storage at scale, but its true power lies in delivering value-added data services that enable AI and data-driven transformation. Here’s how:
| Value Komprise Delivers | Why It Matters for AI |
|---|---|
| Global metadata index across all file/object data | Enables rapid discovery of useful unstructured datasets |
| Intelligent tagging, classification, and search | Adds the context AI teams need to curate and understand data |
| Smart data movement based on policies and usage | Reduces data sprawl and accelerates pipeline readiness |
| In-place analytics and non-disruptive scans | Avoids costly re-ingestion or duplication of petabytes of data |
| Role-based access and governance policies | Ensures responsible, compliant data access for AI use cases |
Komprise Intelligent Data Management bridges the gap between storage teams and data consumers, helping organizations shift from simply storing unstructured data to strategically activating it for AI, analytics, and innovation.
Unstructured data is no longer a cost to manage, it’s an asset to mine. But to do so effectively, enterprises need to modernize their data preparation approach, rethink collaboration between storage and data teams, and invest in the right unstructured data management solutions that can unlock the potential of unstructured data without breaking the budget or workflow.