AI AND BI PIPELINES

AI-ready data is the curated set, not the whole estate.

AI-ready data starts with knowing which files are current, approved, and allowed. Diskover indexes the estate, enriches each record with owner, project, version, and sensitivity, and delivers curated datasets to your AI and BI pipelines, so models train on the best files rather than the most.

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AI-ready data: a curated training set of 2,418 pathology slides filtered from 1.2 million files in the Diskover metadata catalog

WHY CURATION

Every file in a training set costs compute.

A duplicate, a stale version, or a restricted file costs the same compute time as a good one. Diskover selects the set from the metadata catalog first, so your pipelines receive files that are current, approved, and allowed.

AI-ready data: context from Diskover plus content from the AI platform gives better models

HOW IT WORKS

How Diskover prepares AI-ready data.

Diskover indexes every file in place, enriches each record with business context, and curates the set with filters and tags. The data moves only when a workflow delivers it to your lakehouse, BI tool, or AI pipeline.

How Diskover prepares AI-ready data: index in place, enrich, curate, and deliver to AI and BI pipelines

BENEFITS

Why it matters.

Context models can use.

Owner, project, version, and sensitivity travel with every file, so teams and models know what each file is.

Fewer errors from bad inputs.

Duplicates, stale versions, and unapproved files are filtered out before a pipeline runs, so models learn from the right files.

Reports on the same catalog.

Each file in a curated set carries the set’s tag, showing which files went in, for audits and retraining.

Datasets you can trace.

Every file in a curated set carries the set’s tag, so you can see later which files went in, for audits and for retraining.

PROOF

Why AI-ready data pays off.

$250K

in storage overspend avoided at a cancer research institute, while its AI data pipelines kept running

Source: Diskover customer

60%

of AI projects unsupported by AI-ready data will be abandoned through 2026, Gartner predicts

Source: Gartner, 2025

7%

of respondents say their organization’s data is completely ready for AI

Source: Cloudera, 2026

USE CASES

How data preparation turns noise into signal.

Life science and healthcare.

THE PROBLEM
Research and clinical data grow across sites, and temporary and test files fill the storage that AI pipelines need. Without sample and run context, results are hard to trace or reproduce.

WHAT DISKOVER ADDS
Pathology and sequencing files are indexed across every site. The BAM plugin adds sample ID, platform, reference build, and read group to each file record, so teams filter research-ready datasets and clear temporary data out of core storage.

Semiconductor and EDA.

THE PROBLEM
Design data spreads across thousands of volumes and projects, and debug output, scratch files, and old versions sit next to the approved design files. Teams risk training on the wrong ones.

WHAT DISKOVER ADDS
Every design file is indexed with its project, version, owner, and tags. Approved versions are identified at a glance, debug and scratch output is filtered out, and golden datasets are curated for analytics, automation, and AI.

Media and entertainment.

THE PROBLEM
Media sits across several data centers and storage platforms, with no clear view of which assets are final, which are active, and which are stale.


WHAT DISKOVER ADDS
The MediaInfo and OpenEXR plugins add metadata like resolution, codec, and camera settings. Production status comes from systems such as Xytech Media and ShotGrid, so teams curate approved, final assets for analytics and AI.

Energy, oil, and gas.

THE PROBLEM
Exploration, drilling, and production files sit across sites and decades, often in formats and folder structures that differ by team, region, or acquired company.

WHAT DISKOVER ADDS
Seismic, well, and production files are recognized by file type, project path, and age across every site, even after a merger brings in new file systems. Teams then assemble consistent datasets for BI and AI.

Financial services.

THE PROBLEM
A data compliance application needs an accurate file inventory before it can classify anything. Without one, files on old systems or forgotten shares go unchecked.

WHAT DISKOVER ADDS
The Diskover catalog feeds the compliance application and serves as a source for Snowflake. Every file arrives with its owner, age, and location, so classification runs on a complete inventory.

AI on shared drives.

THE PROBLEM
An AI assistant or RAG project points at the whole shared drive, so it reads duplicates, old drafts, and files it shouldn’t use.


WHAT DISKOVER ADDS
The catalog filters by owner, age, project, and tag, removes duplicates, and hands the AI platform only the files that qualify.


AI AGENTS

From AI-ready to agent-ready.

AI agents choose their own data while they work, so the whole estate has to be described, current, and tagged. Read why agent-ready data is a different standard, and see how the AI Connector lets AI tools and agents search the same catalog.

AI-READY DATA

A person picks the set.

A team chooses the files before the model runs. They filter the catalog by owner, project, version, and tag, review the selection, and tag the approved set. The model trains only on that set, and you can trace every file in it later.

AGENT-READY DATA

The agent picks the files.

The agent chooses files while it works, one question at a time. It searches the catalog with the same group permissions as your team, so it sees only what it is allowed to see. Tags tell it which files are current and approved, and a person approves each tag it adds.

FAQ

Questions about AI-ready data.

AI-ready data is data that has been defined, described, and filtered so a model can use it with known boundaries. See the full definition in the AI and data readiness glossary.
In four steps. Diskover indexes every file in place, enriches each record with business context, curates the set with filters and tags, and delivers it to the pipeline when a workflow calls for it.
The data stays in place while the catalog is built. Files move or copy only when a workflow delivers the curated set to its destination.
Curated metadata goes to Snowflake, the Dell Data Lakehouse, and BI tools through the REST API, the Python SDK, or Parquet output. The files stay in place, or move by policy to the storage your AI pipeline reads.
Every file in a training set costs graphics processing unit (GPU) time. Removing duplicates, stale versions, and restricted files before training means each GPU hour is spent on files that improve the model.

See it on your own data.

We’ll index a sample of your environment and show you what’s recoverable against your current storage spend.

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