AI Data Platform Readiness Assessment

In today’s digital landscape, business executives face significant challenges in building scalable and sustainable capabilities for machine learning (ML) and artificial intelligence (AI). AI algorithms rely on high-quality data to generate insights and inform decisions. Without AI-ready data, organizations risk undermining the integrity of their AI solutions and losing customers’ trust. EY helps organizations revitalize their data infrastructure, ensuring they have AI-ready data to drive impactful outcomes.

Six Guidelines for building AI Readiness:

• Proactive AI integration: Embed AI in strategic planning and ensure data environments are AI-ready
  
  
• Build platforms: Improve access and connectivity, with infrastructure to support AI
  
  
• Seamless integration: Ensure integration of transactional data and strong master data as a foundation
  
  
• Governance and compliance: Apply governance and compliance across all data to safely manage AI-related risks.
  
  
• High-quality data: Recognize that high-quality data is critical for reliable AI outcomes and automation
  
  
• Enhanced data access: Improve data access, reduce inconsistencies, and tighten service levels to support scalable AI.
   

Understanding the AI-Ready Data Platform Framework

Our assessment is based on the AI-Ready Data Platform Framework, which consists of the following key components:

Data & AI Strategy: Establish a clear strategy that aligns data initiatives with business goals. This involves defining how data will be used to drive AI projects and ensuring that the organization is prepared to leverage AI technologies effectively.

Data Architecture: Design a scalable and flexible data architecture that supports the integration of various data sources and types. This architecture should facilitate data accessibility and ensure that data can be efficiently processed and analyzed for AI applications.

Master Data Management: Implement Masterer Data Management practices to ensure that the organization has a single, accurate view of its critical data entities. This includes establishing data standards, definitions, and governance processes to maintain data integrity across the organization.

Data Risk & Compliance: Identify and manage data-related risks, including compliance with regulations such as GDPR and other data protection laws. This involves assessing data privacy, security measures, and ensuring that data handling practices meet legal requirements.

Data Governance: Establish a robust data governance framework that defines roles, responsibilities, and processes for managing data assets. This includes data stewardship, policy enforcement, and ensuring that data is used ethically and responsibly.

Data Quality: Focus on improving data quality through continuous monitoring and validation processes. High-quality data is essential for effective AI model training and decision-making, ensuring that insights derived from data are accurate and actionable.

How our AI Data Platform Readiness Assessment helps you

Our comprehensive assessment enables you to determine the specific requirements of your AI use cases, ensuring your data meets expectations for quality and diversity. We implement ongoing data governance practices to maintain metadata alignment and qualification, evolve data management practices, and establish structured governance initiatives with defined roles and processes to track regulatory changes and assess their impact on your organization.

Prepare for the future

As it is often more costly and complex to ensure compliance when AI systems are operational, we recommend that organizations start preparing early. This includes setting up a register for all AI applications used, risk rating them, and implementing adequate data governance and management practices.