Enterprise leaders are moving beyond experimentation in data and AI, focusing instead on scaling capabilities that deliver measurable business outcomes. Yet despite growing recognition of AI’s strategic potential, only 5% of enterprise AI projects reach production.
At the same time, investment continues to prioritise the maintenance of legacy systems over innovation. Platforms are extended but not fundamentally modernised to improve data usability or trust. And enterprises are still plagued by the persistent challenges of yesterday: fragmented landscapes fraught with duplicated, inconsistent, and inaccessible data. In the absence of a unified, governed foundation, the industrialisation of AI is significantly hindered and remains one of the key barriers to production.
Syniti’s Q2 platform release, with enhanced support for Databricks and Snowflake, is designed to address these structural challenges. It represents not just expanded integration, but a step toward a more coherent data strategy—one that aligns data quality, governance, and platform execution.
This is not simply about cleaning data or migrating schemas. It is about establishing a trusted data foundation that enables the business—and AI—to operate, analyse, and act with confidence in real-time.
Platforms such as Databricks and Snowflake have become central to enterprise data strategies because they offer scalable, unified environments for data engineering, analytics, and AI. While the value of these platforms lies in their ability to consolidate workloads that once was previously distributed across multiple systems, platform adoption alone does not automatically resolve the underlying issues.
Without consistent data quality, robust governance, and clear alignment to the business, organizations risk replicating the same challenges in a new environment. As organization's adopt of AI matures, their AI models remain constrained, and the expected returns from modernization are delayed.
The enhanced support for Databricks and Snowflake introduced in Syniti’s Q2 Release embeds data quality, validation, and governance capabilities directly into modern data platforms, ensuring that the data flowing into analytics and AI pipelines is accurate, consistent, and usable from the outset. It's a signal toward a broader trend across the industry: approaching data not as a byproduct of systems, but as a managed asset that must meet defined business standards.
One of the most consistent challenges we observe across clients is the complexity of migrating and modernising data estates. In the fragmented data architectures that persist in most organizations still today, legacy environments often contain deeply interconnected data structures, custom logic, and undocumented dependencies.
Moving this into a modern platform without introducing risk requires a more sophisticated level of automation, architectural discipline, and consolidation that traditional approaches cannot provide.
The combination of Syniti capabilities and Capgemini accelerators addresses this through a more industrialised and rationalised approach to migration and transformation. Metadata-driven processes, automated schema and code conversion, and integrated validation frameworks enable organisations not only to move data with greater speed, but also to reduce unnecessary duplication and regain control over total cost of ownership.
This approach shifts modernisation from a series of one-off projects to a repeatable and optimised capability. By rationalising overlapping platform functions and establishing a more unified data foundation, organisations can reduce migration timelines, minimise disruption, and eliminate the hidden cost of paying for the same data capability multiple times. It also creates a simplified architecture that better supports ongoing innovation and scalability.
The increasing focus on AI has elevated the importance of data quality, governance, and architectural coherence. AI systems depend on access to reliable, well-structured data. But in many enterprises, duplicated data pipelines, fragmented governance tools, and parallel AI platforms continue to reduce both the quality and trustworthiness of that data.
A unified, governed data foundation is a prerequisite for meaningful AI adoption and for becoming an intelligent enterprise. By consolidating data access, governance, and processing into a more coherent architecture, organisations can eliminate redundant data copies, ensure consistent lineage, and provide a single source of truth that underpins analytics and AI at scale.
Platforms such as Databricks, when combined with capabilities like Microsoft Fabric and SAP Business Data Cloud, support this by bringing data engineering, analytics, and AI workloads closer together while reducing overlap across platforms. This not only improves data integrity and governance but also unlocks latent value in existing technology investments that are often underutilised.
This is particularly important as organisations move toward advanced use cases such as agentic AI and real-time decisions, where there is little to none margin for error and the need for traceability, auditability, and consistent governance becomes more pronounced. A unified data foundation enables these capabilities while simultaneously reducing complexity and cost—turning data architecture from a technical challenge into a strategic enabler of enterprise intelligence.
The enhanced support for Databricks and Snowflake also reflects the continued evolution of the broader ecosystem. The partnership between Syniti and Capgemini is designed to combine platform expertise with deep capabilities in data quality, migration, and governance.
This integrated approach allows organizations to address both the technical and operational dimensions of data transformation. It brings together the scalability and flexibility of modern platforms with the discipline required to ensure that data remains accurate, compliant, and aligned to business objectives.
The result is a more balanced model for data modernization, where platform adoption and data readiness progress together rather than in isolation.
The next phase of enterprise transformation will be defined by the ability to move from fragmented, legacy environments to unified, AI-ready platforms, without compromising data quality or governance. Organisations that succeed will treat data as a strategic asset and actively rationalize their data portfolio, eliminating duplication & reducing complexity while strengthening control and trust.
This is not just about technology. A Data First approach is now essential to scale analytics and AI at pace.
The Q2 release represents a tangible step in this direction. By deepening the integration with Databricks, Snowflake & SAP BDC platforms we are able to embed data quality at the core of your modernization programme, accelerating delivery while reducing architectural overlap and cost… and delivering fundamentally better business outcomes.