The Key to a Successful Data Transformation Strategy
Our research has revealed that firms are prioritizing modernization with a focus on agility, efficiency, and resilience. The top drivers of data transformation include the rise of cloud and AI/ML technologies (55%), competitive pressures from fintechs (49%), and the need to enhance operational efficiency (44%). Concerns around cybersecurity (41%) and evolving customer expectations (40%) also weigh heavily on strategic agendas.
Over the next 12–18 months, firms plan to overhaul legacy systems, embrace modular and cloud-native architectures, and embed observability, automation, and ESG metrics into core data strategies. A recurring theme is the shift from project-based transformation to treating data as a product, which emphasises ownership, scalability, and integrated compliance. Firms are also expanding self-service capabilities, aligning with global regulatory frameworks, and building collaborative, cross-functional data environments.
Success will be defined by real-time operational data usage, predictive insights integrated directly into business decisions, and frictionless onboarding of new business units. Respondents aim to foster a culture of governed, trusted data where dashboards show live intelligence, analytics tools scale with the business, and data informs decisions across departments. Seamless interoperability, improved audit readiness, and widespread AI adoption underpinned by high-quality data pipelines are key benchmarks of a mature transformation journey.
Question 1: What are the top three factors influencing your firm's data transformation strategy?
(Respondents were asked to select three options)
The availability and maturity of new data technologies (e.g., Cloud, AI/ML)
Competitive pressures from Fintechs and other financial institutions
The need for improved operational efficiency
The growing threat of cyberattacks and data breaches
Increasing customer expectations for personalized services
The desire to unlock new revenue streams and business models
Evolving regulatory requirements
Challenges with existing legacy data infrastructure: The limitations and costs associated with outdated systems.
The need for better data-driven decision making
