{"id":9824,"date":"2025-11-28T11:29:34","date_gmt":"2025-11-28T11:29:34","guid":{"rendered":"https:\/\/weareoakland.com\/?post_type=case_studies&p=9824"},"modified":"2025-11-28T11:58:50","modified_gmt":"2025-11-28T11:58:50","slug":"delivering-data-transformation-for-digital-infrastructure-provider","status":"publish","type":"case_studies","link":"https:\/\/weareoakland.com\/case-studies\/delivering-data-transformation-for-digital-infrastructure-provider\/","title":{"rendered":"Delivering a Data Transformation for a Leading UK-Based Digital Infrastructure Provider"},"content":{"rendered":"\n
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The Client\n<\/h2>

A leading UK-based digital infrastructure provider had built a strong reputation for enabling its customers to accelerate digital transformation, meet regulatory requirements, and future-proof their operations.<\/p>\n

But while they were empowering clients across the UK to scale and innovate, their own data story told a different tale.<\/p>\n <\/div>\n<\/section>\n\n\n

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The Challenge:\n<\/h2>

Internally, the business faced challenges that many enterprise data leaders will recognise. Data was fragmented, visibility was low, and trust in the numbers was almost non-existent. This made answering critical questions – such as how many customers were being served, what services were active, or when contracts were due for renewal difficult.<\/p>\n

Teams worked in silos, and operational decisions were often made based on gut instinct rather than insight. Sales and support lacked a unified view of their customer base, with metrics varying depending on who was asked and the systems they accessed.<\/p>\n

The organisation wasn\u2019t just looking for a technology fix. They were seeking a trusted partner one who could step back from tools and vendor loyalties to define a strategy that would deliver real, measurable value. What they needed was a transformation in how data was managed, trusted, and used across the business.<\/p>\n

And that\u2019s where Oakland stepped in.<\/p>\n \n\n \n What we do <\/span>\n<\/a> <\/div>\n<\/section>\n\n\n

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The Solution<\/h2>\n\n\n\n

From the outset, it was clear that this wasn\u2019t just a matter of implementing a new data platform<\/span><\/a>. The problem ran deeper, touching governance<\/span><\/a>, process, ownership<\/span><\/a>, and culture. So instead of rushing to deploy new technology, Oakland began by getting to know the business, understanding how data flowed (or didn\u2019t), identifying bottlenecks, and uncovering the root causes of mistrust in the data.<\/p>\n\n\n\n

A Data Maturity Assessment<\/h2>\n\n\n\n

A data maturity assessment<\/span><\/a> formed the foundation of this discovery phase. It revealed what many suspected: low levels of data governance, no formal ownership roles, inconsistent data quality<\/span><\/a>, and no shared vision of how data could be used as a strategic asset.<\/p>\n\n\n\n

Rather than prescribing a one-size-fits-all solution, Oakland built a roadmap grounded in the organisation\u2019s real- world needs. The first step was to establish a robust data management framework. Ownership roles were defined, stewardship responsibilities assigned, and new governance processes introduced. This work laid the foundation for a deeper transformation – one where data wouldn\u2019t just be captured and stored, but managed, modelled, and used to drive performance across the business.<\/p>\n\n\n\n

Engineering the Technical Platform<\/h2>\n\n\n\n

Once the governance groundwork was in place, attention turned to engineering the technical platform. The organisation had already been using Microsoft Power BI tools and was beginning to explore Microsoft Fabric<\/span><\/a>. This made Microsoft Azure<\/span><\/a> a natural fit.<\/p>\n\n\n\n

Oakland conducted a detailed review of architectural options before recommending a hybrid approach centred on Microsoft Azure and Databricks<\/span><\/a>. Databricks was chosen not only for its analytical power but for the scalability, flexibility, and interoperability it offered to future-proof the data estate. This wasn\u2019t just about fixing today\u2019s problems \u2013 it was about laying the groundwork for AI and advanced analytics in the future.<\/p>\n\n\n\n

A Single Client View<\/h2>\n\n\n\n

At the heart of the solution was the goal of creating a single client view. Data from the company\u2019s most essential systems \u2013 finance, service desk, and core operational platforms \u2013 was identified as critical. Integrating these sources would allow teams to gain a unified, trusted view of each customer. <\/p>\n\n\n\n

For the first time, customer data would no longer live in disconnected systems; it would be brought together, modelled, and visualised through Power BI dashboards accessible to those who needed them.<\/p>\n\n\n\n

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Oakland Modular Data Platform\n<\/h2>

Using the Oakland Modular Platform (OMP), the team built and deployed the core platform in just 12 weeks. Alongside the platform itself, Oakland developed a custom finance data connector to pull information into the environment \u2013 designed not just for this project, but as a reusable asset for future Oakland clients.<\/p>\n

The delivery approach was structured around four phases: Discover<\/strong>, Define<\/strong>, Plan<\/strong>, and Execute<\/strong>.<\/p>\n <\/div>\n<\/section>\n\n\n

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Discover<\/h3>\n\n\n\n

During the Discover phase, Oakland conducted the initial data maturity assessment and worked with leadership to understand the current state and define a vision for where they needed to go.<\/p>\n\n\n\n

Define<\/h3>\n\n\n\n

In the Define phase, the architectural approach was outlined, selecting Databricks and Azure as the backbone of the platform, with Power BI as the front-end reporting layer. Plans were also developed for future enhancements, including product-level reporting and improved financial processes.<\/p>\n\n\n\n

Plan<\/h3>\n\n\n\n

The Plan phase focused on establishing governance frameworks and architectural blueprints, while mapping out a phased roadmap for growth.<\/p>\n\n\n\n

Execute<\/h2>\n\n\n\n

In the Execute phase, the team brought everything together. The platform went live with a production-ready single client view, integrating data from key business systems and delivering insights that had previously been hidden or inconsistent. <\/p>\n\n\n\n

Governance improvements and architectural assessments were embedded into the process, ensuring that the platform was not only technically sound but operationally sustainable and strategically aligned.<\/p>\n\n\n\n

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