Data Management Glossary
AI Data Ingestion
What is data ingestion for AI?
AI Data Ingestion (or AI ingestion) is the process of discovering, preparing and moving data from various sources such as applications and storage systems into AI tools and services for processing, analysis and/or training machine learning (ML) models. AI data ingestion in corporate environments consists primarily of leveraging unstructured data, such as user documents, PDFs, chat and text files, multimedia files, or instrument data.
Why is unstructured data ingestion for AI challenging?
For unstructured data, such as files, images, videos, sensor logs, and documents, the process preparing and feeding data into AI and machine learning pipelines is far more complex than with structured databases. Challenges include:
- Data Volume and Scale: Unstructured data makes up over 80% of enterprise data and often spans petabytes and billions of files, overwhelming traditional ETL pipelines.
- Data Sprawl: Files are scattered across on-premises NAS, cloud object stores, and SaaS applications, making it difficult to locate and aggregate relevant datasets.
- Lack of Metadata: Unlike structured data, files often lack consistent metadata, making classification and filtering for AI readiness a major hurdle.
- Performance Bottlenecks: Moving massive datasets into AI pipelines can cause latency, downtime, and high network costs.
- Governance and Compliance: Sensitive data may be inadvertently exposed without controls to filter, tag, or enforce access policies.
Since unstructured data is highly distributed across storage silos in enterprises, storage IT professionals need automated systems to search across petabytes of corporate data stores, check for sensitive data, tag data so that it can be discovered more easily and move data to AI with audit reporting.
Why is governance important for AI ingestion?
AI data governance is an important discipline to ensure safe AI data ingestion processes, since corporate data used in AI can lead to sensitive data leakage, compliance violations and inaccurate or unethical outcomes without the proper guardrails. Data management systems can help by classifying and segmenting data for use or restrictions in AI and also deliver a means to audit and investigate derivate works as needed for data security, privacy and overall compliance requirements. IT organizations need to establish processes and policies for collecting, storing, processing, and using data within AI systems.
AI data workflows are also intrinsic to AI data ingestion as they deliver the automation and controls to quickly discover, classify and move data to AI tools, including enriching metadata, so users can more easily find and use data in projects.
Enterprise IT directors overseeing large, petabyte-scale data estates will increasingly need to adopt highly-efficient, safe and accurate methods for AI data ingestion, as department heads expand their requests for AI projects.
How can Komprise address the AI ingestion challenges for unstructured data?
Komprise provides a smarter way to prepare unstructured data for AI ingestion by providing:
- Data Visibility and Analytics: Identifying which unstructured datasets exist, who owns them, how often they’re used, and where they live.
- Intelligent Indexing and Metadata Enrichment: Adding custom tags and metadata to files so the right subsets of data can be easily found and fed into AI pipelines.
- Automated Data Movement: Tiering, migrating or copying only the right data sets directly in its native format to cloud or AI platform, without disruption or vendor lock-in.
- Elastic Data Workflows: Running parallelized, high-performance migrations to feed AI pipelines faster.
- Governance Built-In: Ensuring policies, access controls, and audit trails remain intact as data is mobilized.
The challenge of AI ingestion for unstructured data lies in its scale, fragmentation, and lack of metadata. Komprise Intelligent Data Management addresses these issues by making unstructured data visible, searchable, governed, and portable so enterprises can deliver the right data to AI faster and at lower cost.
Read the blog and watch the video with Komprise COO Krishna Subramanian and eWeek on the related topic of AI inferencing.
What are key considerations for AI data ingestion?
Effective AI data ingestion requires ensuring that data is accessible, high-quality, and in the right format. Key considerations include handling unstructured data at scale, preserving metadata for context, avoiding data silos, ensuring security and compliance, and delivering the right data to the right AI workflows without unnecessary duplication. These considerations help organizations reduce the cost and complexity of preparing data for AI while maximizing accuracy and AI ROI.
What are some common AI data ingestion issues?
Common issues include dealing with unstructured or messy data, inconsistent formats, and missing or incomplete metadata. Organizations often face hidden costs from rehydration or excess data movement. Performance bottlenecks when ingesting large volumes of data and lock-in to specific AI pipelines or storage systems are also frequent challenges. Addressing these issues requires scalable, flexible solutions that can process data in-place without heavy lifting or duplication.
What are the potential risks and costs of not ingesting the right data to AI?
If the wrong or incomplete data is ingested, AI models can produce biased or inaccurate results, leading to flawed decision-making. Operationally, ingesting unnecessary data increases storage, compute, and cloud costs. It also creates compliance risks if sensitive or regulated data is fed into AI systems without proper AI data governance. Ensuring that only the right, well-classified data flows into AI pipelines helps reduce costs, improve accuracy, and mitigate security and regulatory risks.