Enterprise Connect AI 2024 Schedule

Artificial Intelligence (AI) and Machine Learning (ML) projects can provide significant return on investment when they are applied to narrow but difficult business problems and supported by adequate amounts of relevant, quality data. Many such projects start off with high hopes, but get derailed due to fundamental problems with source data which were not known to the organization before embarking on the work. Without some careful analysis prior to beginning work, these defects can crop up as blockers surprisingly late in the AI/ML development process.

In this session, a leading expert proposes an Eyes Wide Open approach to specific assessment of data readiness before starting an AI/ML project, and the establishment of enforceable Go/No Go criteria for any such effort. Areas of analysis include:

• Knowledge Representation
• Data Availability
• Data Quality
• Data Ethics

These criteria are useful measures across any data architecture; this session will illuminate how they become specifically relevant in AI/ML projects. The session will include real-world datasets and a sample analysis of their suitability as inputs to AI/ML.