Client Overview
The client is a global provider of automotive data, delivering insights across vehicle production, sales performance, and component-level intelligence. Its customers rely on this data to inform supply chain strategy, investment decisions, technology planning, and market forecasting across regions.
Over time, the client’s digital ecosystem evolved across multiple standalone platforms, each built for specific data sets and use cases. As the portfolio expanded, complexity increased across product structure, subscription models, and operational workflows.
Leadership initiated a multi-year modernization effort to unify platforms, simplify entitlements, and rethink how AI should support users working with complex data. The objective extended beyond consolidation to fundamentally improve how customers move from raw data to actionable insight.
Customer Challenge
Key challenges included:
Fragmented data access
Users searched across multiple tools, manually reconciled outputs, and interpreted raw datasets independently.
High effort to reach insight
Data availability did not translate into clarity; understanding required significant time and expertise.
Inconsistent subscription and entitlement models
Access rules and contracts varied across platforms, creating operational friction and internal delays.
Unclear AI positioning
AI was initially framed as a standalone feature rather than embedded across workflows.
Ecosystem complexity
Fifteen years of accumulated features and business logic created structural fragmentation across product, data, and governance layers.
What was at stake?
Without structural modernization, user friction would increase as data volumes and feature sets expanded. Platform consolidation alone would not resolve inefficiencies if the product direction remained centered on access rather than understanding.
The organization required a unified product strategy that simplified data navigation, embedded AI into workflows, and aligned global teams around a coherent direction for long-term platform evolution.
Ness Solution
Ness partnered with the client on a multi-year initiative to unify fragmented platforms, redefine the product north star around time-to-insight, and embed AI directly into the user workflow. The transformation addressed product structure, entitlement logic, and interaction design in parallel.
Key initiatives
Discovery and ecosystem mapping
Conducted a four-month cross-regional assessment of platform structures, data flows, subscription logic, operational workflows, and user journeys to identify duplication and structural complexity.
Product north star reframing
Shifted the program goal from platform consolidation plus AI features to a clear focus on reducing time-to-insight, guiding prioritization across design, AI, and architecture decisions.
AI embedded into workflows
Integrated AI across reports, charts, and quantitative exploration to clarify terminology, summarize content, and support interpretation within existing workflows rather than through a standalone interface.
Computational notebook introduction
Designed and implemented a built-in computational notebook enabling users to pull data into a single workspace, ask natural-language questions over quantitative data, and explore comparisons without switching tools.
Subscription and entitlement redesign
Standardized back-office workflows and clarified contract and access management rules across products.
Cross-practice alignment for implementation
Established structured collaboration across product, UX, AI engineering, frontend, architecture, cloud, and governance teams across Europe, the U.S., and Asia.
Engagement model
Ness delivered the program through cross-practice collaboration spanning product strategy, design, AI engineering, frontend development, architecture, cloud, and governance. Teams operated across Europe, the U.S., and Asia while maintaining alignment around a single product direction.
Workshops and structured alignment sessions reduced ambiguity between discovery and implementation, ensuring consistent decision-making across disciplines and regions.
What Changed?
These changes established structural coherence across product architecture, data workflows, and operational governance.
Business Outcomes
Strategic impact
The initiative established a unified foundation across data platforms, entitlement logic, and interaction models. By redefining the product goal around time-to-insight, the organization shifted from platform management to outcome-oriented product strategy.
Embedding AI directly into quantitative workflows positioned the platform for long-term evolution beyond static reporting. The computational notebook introduced a scalable interaction model that integrates analysis and interpretation within a single environment.
Collectively, the transformation provides a durable framework for future product expansion, AI integration, and global alignment across complex data ecosystems.
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