Data Management Glossary
AI Data Ingestion
AI Data 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.
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.
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.
Read the blog and watch the video with Komprise COO Krishna Subramanian and eWeek on the related topic of AI inferencing.