Key suggestions
Prioritize End-to-End Planning for Data Modernization Initiatives
Firms should invest in comprehensive transition strategies that address not only technology upgrades, but also change management, operational continuity, and staff retraining. Anticipating the time, cost, and resource demands of modernization will help reduce disruption and accelerate ROI.
Tackle Manual Data Handling with Scalable Automation and Governance
To reduce operational risk and meet regulatory standards, firms must focus on replacing manual processes with automated data capture, lineage tracking, and validation tools. Embedding governance controls early in the data lifecycle will drive greater accuracy and compliance.
Make Explainability and Collaboration Core to AI Development
With AI underperformance often tied to cost overruns and lack of transparency, firms should prioritize explainability and involve domain experts, business leads, and compliance teams from the outset. Structured retraining and agile testing cycles will improve both outcomes and accountability.
Shift from Project-Based Transformation to Data-as-a-Product Thinking
Successful transformation requires moving beyond one-off data projects to establishing long-term data product ownership, supported by clear metrics, ESG alignment, and cross-functional teams. This shift will better position firms to scale insights and ensure data quality across the enterprise.
