Essential good practices to consume and produce data for AI implementation

AI data management requires robust ecosystems that balance accessibility with governance, enabling organizations to effectively produce and consume data at scale.

Current data landscape; Organizations face unprecedented challenges in data management, with global data volume doubling in five years and 68% of enterprise data remaining unused.

• Approximately 80-90% of data is unstructured, according to MIT research, creating significant complexity in data utilization
• Modern use cases demand extremely fast data availability, with some requiring sub-10 millisecond access times
• The rise of AI has intensified the need for sophisticated data management strategies

Core principles for effective data management; Three fundamental elements form the foundation of successful data ecosystems.

• Self-service capabilities reduce friction by enabling seamless data discovery and democratizing access
• Automation integrates essential data management functions directly into user tools and experiences
• Scalability ensures systems can grow to meet increasing demands while maintaining performance and reliability

Data production framework; A well-structured approach to data production emphasizes accessibility and governance.

• Self-service portals provide unified control planes for managing storage, access controls, versioning, and business catalogs
• Organizations can choose between centralized platforms, federated models, or hybrid approaches for governance
• Consistent mechanisms for automation and scalability ensure reliable production of high-quality data

Data consumption strategy; Efficient data consumption requires streamlined access and clear organization.

• Centralizing compute within data lakes and implementing single storage layers reduces complexity
• Zone strategies accommodate various use cases, from raw data handling to strictly governed curated zones
• Automated services manage access, lifecycle, and compliance requirements

Storage and infrastructure considerations; Strategic storage design plays a crucial role in data ecosystem effectiveness.

• Minimizing data sprawl through centralized storage reduces system complexity
• Personal and collaborative zones enable experimentation while maintaining governance standards
• Quality control mechanisms ensure data reliability across different usage scenarios

Implementation outlook; Organizations must balance rapid innovation with sustainable data management practices to achieve long-term success in AI initiatives.

• Focus on building trustworthy and accessible data ecosystems
• Implement scalable governance mechanisms that don’t impede innovation
• Prioritize processes that enhance data quality while maintaining flexibility

Future implications: As AI capabilities continue to expand, organizations that establish robust data management foundations will be better positioned to leverage new opportunities while maintaining data integrity and compliance requirements.