Strategies executives use to build AI-ready data foundations

The foundation of successful AI implementation lies in creating robust data management strategies, as demonstrated by leading executives who emphasize the critical importance of proper data governance before deploying artificial intelligence solutions.

Strategic foundations and personnel priorities: L&G’s group chief data and analytics officer Claire Thompson emphasizes that establishing strong data foundations is crucial for future innovation and business value.

• A clear connection between data strategy and tangible business outcomes helps justify the investment in proper data management
• Close collaboration between data teams and IT departments is essential for effective data governance
• Data quality by design principles help prevent downstream issues and reduce the need for remediation
• The goal is to leverage clean data for enhanced customer experiences and automated processes

Technology infrastructure and data mastery: DWF’s CTO Jon Grainger advocates for a proactive approach to data strategy implementation, focusing on fundamental elements before exploring AI applications.

• The firm employs cloud-based SaaS products and open APIs to manage various data entities
• Each SaaS product maintains a specific identity within the enterprise architecture
• SnapLogic technology ensures reliable connections between services, APIs, and users
• The system can automatically correct inconsistencies in data entry and enrich information through API integration

Cross-industry collaboration and standardization: NSTA’s director of corporate and CFO Nic Granger highlights the importance of extending data strategy beyond organizational boundaries.

• The Offshore Energy Digital Strategy Group (DSG) coordinates data collection efforts across UK public bodies
• Three key workstreams focus on data standards, interoperability, and cross-sector digitalization
• Common data toolkits enable seamless data sharing between different platforms and organizations
• The strategy emphasizes developing digital skills and maintaining cybersecurity best practices

Future implications: While implementing comprehensive data strategies requires significant time and resources, organizations that establish these foundations position themselves advantageously for AI adoption and digital transformation. The emphasis on data quality, standardization, and cross-organizational collaboration suggests a shift toward more integrated and sophisticated data ecosystems that will enable advanced AI applications while maintaining data integrity and security.