Glossary

AI Data Readiness

AI data readiness is the degree to which an organization’s data and supporting infrastructure are prepared for artificial intelligence (AI) workloads. It considers whether the data required for a specific AI use case is accessible, usable, governed, secure and supported by infrastructure that can deliver it at the required scale and performance.

AI data readiness applies to machine learning (ML), generative AI, retrieval-augmented generation (RAG), large language models (LLMs) and other AI applications. These workloads may depend on structured data from databases as well as large volumes of unstructured data such as documents, images, video, audio, logs and scientific datasets.

Data readiness is use-case dependent. Data suitable for training a computer vision model, for example, has different requirements from enterprise documents used by a RAG application. Gartner describes AI-ready data in terms of alignment with the intended use case, qualification of the data and appropriate governance.

Why is AI data readiness important?

AI systems depend on the data available to them. Problems with data quality, accessibility, governance or infrastructure can affect model accuracy, development time and the ability to move AI projects into production.

Organizations often have significant quantities of data without having an effective way to use that data for AI. Information may be distributed across storage systems, applications, clouds and physical locations. It may lack consistent metadata, contain duplicate or outdated information, or be subject to access and regulatory requirements that were established before AI applications were introduced.

AI also increases the importance of unstructured data. Documents, images, video and other objects can produce multiple derived artifacts during AI processing, including extracted text, embeddings, metadata, summaries and classifications. Maintaining relationships between source data and these derived artifacts helps preserve lineage and context.

Improving AI data readiness gives organizations a more reliable foundation for developing, deploying and scaling AI applications.

What are the key components of AI data readiness?

AI data readiness typically involves several related areas.

Data availability and accessibility

AI applications need reliable access to the datasets required by their use cases. Organizations need to identify where relevant data resides and determine how applications, data pipelines and compute infrastructure will access it.

This can become difficult when data is distributed across separate storage silos, clouds or business applications. A data architecture that provides broad access to large datasets can reduce the amount of data movement and duplication required to support AI workflows.

Data quality

Data should be sufficiently accurate, complete, consistent and representative for its intended AI application.

The appropriate definition of quality depends on the use case. AI training datasets may intentionally include edge cases, anomalies or other variations that traditional analytics processes might remove. For this reason, conventional measures of clean data do not automatically make a dataset AI-ready.

Metadata and context

Metadata helps AI systems and data teams understand what information represents, where it originated and how it can be used.

Useful metadata can include classifications, ownership, timestamps, schemas, sensitivity labels, relationships and provenance. Strong metadata practices also make large datasets easier to discover and manage.

Data governance and lineage

Organizations need policies governing who can access data, how it can be used and how changes are tracked.

Data lineage provides visibility into where information originated and how it has been transformed. These capabilities become increasingly important as AI pipelines create derived datasets, embeddings and other artifacts from source data.

Governance can also help organizations apply privacy, security, retention and regulatory requirements consistently throughout AI workflows.

Security

AI systems can expand the number of applications and users interacting with enterprise data. AI data readiness therefore includes controls for authentication, authorization, encryption and data protection.

Organizations also need to understand whether sensitive or regulated information can be accessed by a particular AI application and maintain appropriate controls as data moves through AI pipelines.

Storage performance and scalability

AI workloads can place significant demands on storage infrastructure.

Training and inference systems may need to process very large datasets using GPUs and other accelerated computing infrastructure. Storage must provide sufficient throughput, capacity and parallel access to keep these compute resources supplied with data.

Scalability is particularly important for unstructured data because AI environments may contain billions of objects and rapidly growing datasets.

Data pipelines

AI data pipelines move information from source systems through ingestion, preparation, enrichment and processing stages before it reaches models or AI applications.

Reliable pipelines help ensure that the correct data reaches AI systems consistently. They can also automate operations such as classification, metadata enrichment, validation and transformation.

Gartner includes preparing data pipelines for both model training datasets and live production feeds among the steps involved in making data AI-ready.

How do organizations assess AI data readiness?

An AI data readiness assessment evaluates whether an organization can meet the data requirements of a defined AI use case.

A typical assessment examines:

• Whether the required data exists and can be located
• Whether applications can access the data efficiently
• Data quality, completeness and representativeness
• Metadata and data classification
• Governance and ownership
• Security and access controls
• Data lineage and provenance
• Storage capacity, scalability and performance
• Data pipeline capabilities
• Compliance and retention requirements

The assessment should be connected to specific AI applications rather than treating readiness as a universal property of an organization’s entire data estate. Deloitte, for example, describes readiness across dimensions including availability, volume and diversity, quality and integrity, governance, and responsible data use.

How does storage affect AI data readiness?

Storage provides the persistent data foundation underneath AI pipelines and compute infrastructure. Its architecture can affect how quickly organizations can ingest, prepare, access and retain the large datasets used by AI.

Object storage is particularly relevant because much of the data used by AI is unstructured. Images, video, audio, documents, sensor data and model artifacts can be stored as objects together with metadata that helps applications identify and manage them.

For large AI environments, storage infrastructure may need to provide:

• High throughput for GPU-intensive workloads
• Massive capacity for growing datasets
• Parallel access from multiple applications and compute nodes
• Rich metadata capabilities
• Data durability and availability
• API-based access such as the Amazon S3 API
• Security and access controls
• Lifecycle and retention management
• Support for geographically distributed data

A scalable storage foundation can also allow multiple AI applications to work from shared datasets rather than maintaining separate copies for individual projects.

What is the difference between AI data readiness and AI readiness?

AI readiness describes an organization’s broader ability to adopt and operate AI. It can include strategy, skills, organizational processes, infrastructure, governance, security, models and data.

AI data readiness focuses specifically on the data foundation and the systems required to make that data usable by AI.

An organization can therefore have access to AI models, GPUs and experienced data science teams while still having low AI data readiness if its data is fragmented, inaccessible, poorly governed or difficult to process at scale.

What is the difference between AI data readiness and AI-ready data?

The terms are closely related but describe different concepts.

AI-ready data is data that meets the requirements of a particular AI use case, including appropriate quality, context, accessibility and governance.

AI data readiness describes the broader capability to consistently provide and manage that data. It includes the architecture, storage, pipelines, metadata, governance and operational processes required to support AI applications over time.

AI data readiness is therefore an ongoing capability rather than a one-time data preparation exercise.

How can organizations improve AI data readiness?

Organizations can improve AI data readiness by starting with specific AI use cases and determining what data those applications require.

From there, teams can identify relevant datasets, assess their quality and accessibility, establish appropriate governance, and address infrastructure limitations that could constrain AI workloads.

Common priorities include consolidating or connecting data silos, improving metadata, establishing lineage, automating data pipelines, strengthening access controls and deploying storage infrastructure capable of supporting growing unstructured datasets.

Readiness also needs continuous monitoring. Data sources, AI applications and governance requirements change over time, so organizations need processes for maintaining data quality, observability and control as AI environments scale.

How does Scality support AI data readiness?

Scality provides enterprise object storage for large-scale unstructured data environments, helping organizations establish a scalable storage foundation for AI and data-intensive workloads.

Scality RING supports the Amazon S3 API and provides high-capacity storage for large datasets such as documents, images, video, research data, backups and other unstructured information. Organizations can use a common object storage platform to retain and access datasets across AI pipelines while applying enterprise data protection and security controls.

For AI environments that depend on accelerated computing, scalable object storage can complement GPU infrastructure by providing persistent capacity for training data, source datasets, model artifacts and AI-generated data.

By providing a durable and scalable foundation for unstructured data, Scality can support the storage layer of an organization’s broader AI data readiness strategy.