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Strengthening Data Foundations in a Complex Investment Environment
A large New Zealand-based sovereign investment organisation was partway through a major data platform transformation, replacing a legacy environment that had been in place for over a decade.
While strong progress had been made in modernising the technology, introducing cloud capabilities and evolving data architecture, the organisation recognised that technology alone would not deliver the outcomes they were aiming for. There was a need to ensure that data was not only well-built, but well understood, trusted, and sustainable over time.
Cyma partnered with the organisation to provide independent validation and ongoing support, helping turn a technically sound platform into a scalable, long-term data capability.
Date: 2024 | Client: Financial Services Organisation | Sector: Financial Services
The Challenge.
The organisation was navigating multiple layers of change at once. Alongside the platform rebuild, they were redesigning their data architecture and beginning to shape a new operating model.
However, a number of challenges were emerging.
Data modelling sat within the data engineering team, rather than as a defined practice. While the team had built high-quality, fit-for-purpose models, there was limited formal experience in data modelling approaches and standards. Documentation was inconsistent and difficult to maintain, and ways of working were evolving quickly in a fast-paced project environment.
This created uncertainty. Model design decisions were being questioned, and the connection between technical choices and business value was not always clearly articulated.
At the same time, the transformation was already underway and could not be paused. There was a reliance on a small number of key individuals, and broader architecture and governance practices were still maturing.
Without intervention, there was a risk that good work would become difficult to sustain, misunderstood by stakeholders, or fail to deliver its intended long-term value.
What we did (together).
Cyma worked alongside the client in a lightweight, collaborative way, combining independent assessment with ongoing, embedded support.
We began with a focused health check, reviewing the data models, supporting documentation, and architectural context. Through targeted discussions with the team, we built a clear understanding of both what was working well and where the risks were.
One of the most important outcomes of this phase was confirming that the data models themselves were strong, well-architected, pragmatic, and aligned to business needs. The challenge was not the quality of the work, but how it was supported, communicated, and sustained.
From there, we provided ongoing advisory support through a flexible, on-demand model. Rather than introducing heavy frameworks, we worked as a sounding board and partner—supporting real decisions as they happened.
Together, we:
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Validated and strengthened modelling approaches
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Helped navigate key design trade-offs
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Identified gaps in documentation and processes
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Simplified how information was captured and shared
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Supported a shift from project delivery to a more sustainable, ongoing capability
Strengthened foundations for long‑term value
Validated and reinforced a modern data platform so it could scale, evolve, and deliver real business outcomes over time.
Turned good technology into trusted capability
Helped connect data design decisions back to business value, improving clarity, confidence, and stakeholder trust.
Enabled the team through partnership, not process
Worked alongside data and engineering leaders as a sounding board - supporting real decisions in an active transformation.
Practical support, delivered efficiently
Provided the right expertise at the right time through a lightweight, on‑demand model, no heavy frameworks, no unnecessary overhead.
The Result.
The engagement created a step change in both confidence and clarity.
The team gained independent validation that their models were fit-for-purpose and future-ready, reducing uncertainty and reinforcing the direction they were taking.
They were better equipped to explain and defend their design decisions, improving conversations with stakeholders and building trust in the platform.
Clear focus areas were established to improve sustainability, including more practical approaches to documentation, stronger process maturity, and a more intentional operating model.
Most importantly, the organisation was positioned to move beyond a project mindset, towards a scalable, business-aligned data capability that could evolve over time.
Why it worked.
This engagement worked because it focused on what actually mattered.
Rather than over-engineering new frameworks or slowing delivery, we met the team where they were, working within an active transformation and supporting decisions in real time.
We combined independent validation with practical guidance, helping the team build confidence in their existing work while strengthening the areas that would enable long-term success.
The flexible, on-demand model meant the organisation could access the right expertise at the right time, without unnecessary overhead.
Most importantly, the focus stayed on outcomes, not just how the data was built, but how it would be understood, trusted, and used by the business.