The Challenge

Data governance wasn’t new.

When Oakland started working with the client, it had already been attempted multiple times. Strategies had been written. Roles had been assigned. But nothing impactful had truly landed in the day-to-day. Governance existed in theory, not in practice.

Over time, this created fatigue across the organisation. For many teams, governance felt abstract something talked about but not experienced in a meaningful way.

At the same time, the client was entering a critical phase. A new investment cycle was underway, alongside a broader shift towards becoming more data-driven. In a highly regulated environment, the ability to trust and act on data is essential not just for performance, but for compliance and customer outcomes.

Without clear ownership, consistent processes, and confidence in the data, those ambitions were at risk.

The challenge wasn’t to introduce governance. It was to make it stick.

The Results

£85 million in identified value.

One priority use case, focused on customer data, revealed a significant opportunity. By improving the quality and management of this data, we identified solutions with the potential to unlock £85 million in value, enabling better collection and use of existing information. That was the turning point. Because governance was no longer theoretical. It was measurable.

“We are one team. It’s not Oakland versus us; we’re all working towards the same agenda.”

Data Governance Lead

Governance, embedded

We moved governance out of documents and into the business:

• Data governance implemented within a core business function
• Clear ownership across data owners and stewards
• Governance integrated into day-to-day operations

Capability, built

We built the capability to sustain and scale governance:

• 4 data owners, 5 lead data stewards, and 28 data stewards trained across the organisation
• Increased data literacy and understanding
• Internal team equipped to take ownership

Visibility, improved

We created transparency and structure around data:

• Governance dashboards and centralised platforms introduced
• Data issues identified, prioritised, and actively managed
• Greater visibility of data ownership and processes

Governance stopped being something we had to explain. It became something the business could see working.

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The post Turning Data Governance into £85 Million of Opportunity appeared first on Oakland.

The Challenge

The client’s data estate had grown organically over time. Legacy systems and manual processes made business reporting difficult to scale, govern, or trust.

At the same time, the organisation was navigating complex changes. Systems, processes, and data needed to be untangled and rebuilt in a new, independent environment that meets ongoing operational and regulatory demands.

This challenge was not just technical. With dozens of data sources spanning ticketing, revenue, timetabling, and operations, they needed a way to bring structure to complexity – fast and future-proof.

In short, they needed strong data foundations built for what was to come.

The Solution

To deliver this, the client needed a partner who could combine strategy, engineering, and analytics and work as an extension of their team.

Oakland was selected for its specialist data expertise, its Everything Data approach, and over 40 years of experience in data and process excellence.

The focus? Creating a platform that would improve how the organisation operated, not just how it reported.

What was the starting point? The discovery phase, working across the business to understand requirements, prioritise use cases, and define a clear path forward. Working as a natural extension of the client’s team to make sure the solution reflected the real operational needs.

Once Oakland had this knowledge, the next step was to produce a future state architecture and roadmap, which brought clarity and a solution to a complex landscape of more than 20 data sources.

The Partnership

This engagement marked one of the first major collaborations between Oakland and Softcat.

Softcat brought a strong relationship with the client along with expertise in cloud infrastructure and vendor ecosystems. Oakland brought specialist expertise across data strategy, engineering, governance, and analytics.

Together, this created an end-to-end capability from infrastructure through to insight.

The teams worked as one, alongside the client. There were no silos or handoffs. There was shared ownership of both the problem and the outcome.

This is a clear example of Oakland’s Everything Data approach in practice. Focused expertise, delivered in partnership with Softcat, with a clear emphasis on business value.

Looking Ahead

With the foundations in place, the client continues to build on the platform.

New data sources are being onboarded, reporting is expanding, and the organisation is beginning to explore more advanced analytics and AI use cases.

What started as a complex replatforming challenge is now a platform for long term innovation and a step change in how data supports the business. The Fabric data platform has become a foundation for long-term innovation and a step change in how data supports the business.

“The Oakland did well to apply their knowledge of data warehousing to that of our business, helping build a modern, scalable data platform in Fabric.”

Client Programme Lead

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The post Rebuilding a data platform from the ground up with Microsoft Fabric appeared first on Oakland.

The Challenge

Ambitious Growth Plans

Data is central to how the firm delivers value. Much of its work across audit, tax, and advisory is inherently data-driven, and the leadership vision is ambitious: to build a future where data underpins decisions and enables teams across the business to work with insight and confidence. There is also a long-term ambition to significantly raise data literacy and analytical capability across employees and partners.

At the same time, the organisation had multiple programmes underway that depended on strong data foundations. These included automation and AI initiatives, and the rollout of a new CRM. The firm recognised it needed an aligned data strategy and delivery plan to support both immediate priorities and future growth.

The starting point was a familiar one for fast-growing organisations. The firm had a small central data function, no formalised data governance, and a fragmented landscape with pockets of maturity but no consistent approach. The business needed direction, alignment, and momentum, not a strategy that sat on a shelf.

The Solution

Oakland partnered with the firm to define a practical, business-led data strategy that balanced short-term delivery with long-term capability building. The approach ensured organisation-wide value through foundational initiatives alongside tangible, high-impact lighthouse projects.

Our work followed Oakland’s proven methodology: Discover, Define, Plan and Execute.

During a six-week intensive engagement, we finalised the data strategy and developed a targeted, value-focused roadmap for the next 18 months.

Discover

Using Oakland’s Data Maturity Framework, we worked with the organisation to assess the current landscape and identify key opportunities to strengthen and scale data capability in line with its ambitious growth journey.

As is often the case in fast-growing organisations, strong pockets of data maturity had developed across the
business, with teams already making extensive use of reporting and insight to support decision-making.

The assessment highlighted several areas where greater consistency and alignment could unlock even more value, including:

• Core systems had grown organically across teams, creating an opportunity to improve integration and data flow across the organisation.
• Key data was held in multiple locations, highlighting the need to establish a more consistent single view.
• Teams were relying on manual, Excel-based processes to meet reporting needs, demonstrating strong demand for more automated and scalable solutions.
• Reporting was delivered through a mix of Power BI and Excel, with an opportunity to align approaches and improve consistency.
• Data accountability often sat with IT, and the organisation recognised the value of establishing clearer business ownership through stronger governance.

Define

The Define stage focused on shaping the data strategy and the recommendations required to support increased data capability and maturity.

Strategic pillars and data strategy components were defined, with recommendations across people, process, and technology. The strategy was designed to connect directly to the organisation’s growth plans and wider digital agenda.

It created clarity on what good looks like, how data should enable key programmes such as CRM and AI, and which capabilities needed to be built first.

Plan

Working in partnership, Oakland created a clear and actionable roadmap to implement the data strategy and
deliver the capabilities required.

The strategy was broken down into incremental components aligned to key inflection points, enabling continuous value delivery. KPIs and measures of success were defined so progress and outcomes could be clearly tracked, supported by a governance programme to oversee delivery.

Execute

Oakland moved quickly into execution across three connected areas: integration, platform, and governance,
supporting the organisation end-to-end from strategy through to early delivery.

First, Oakland supported an assessment of integration platforms. The challenge was not only getting data into a data platform, but connecting a hybrid estate of on-premise and cloud applications and enabling reliable data movement and orchestration across systems.

The organisation already knew an iPaaS approach was required, so Oakland ran a needs and requirements-
led evaluation across multiple vendors, including Boomi, MuleSoft, Workato and Tynes. Based on the organisation’s requirements, Oakland recommended MuleSoft as the most appropriate solution to support API-led connectivity and scalable integration.

In parallel, Oakland helped select and design the right data platform architecture. Options including Snowflake, Databricks and Microsoft Fabric were assessed, balancing the existing technology footprint with future needs.

With the organisation strongly Microsoft-centric and some existing Snowflake presence, Oakland recommended Microsoft Fabric and produced a detailed technical architecture and design document. The initial environment was stood up so delivery could quickly move into onboarding data and enabling use cases in the next phase.

Alongside the technology foundations, Oakland focused on the operating foundations that make platforms valuable.

Oakland deployed a best-practice data operations and governance SharePoint site and began establishing
practical governance ways of working. This included early cataloguing of critical data elements and agreeing
standard definitions for high-value operational reporting, particularly across delivery and financial performance metrics such as utilisation, billing and budget recovery.

A data governance toolkit was also delivered to support the rollout of ownership and accountability, with a clear path to training data owners and stewards in subsequent phases.

Our Softcat Partnership

As long-term trusted advisors to this professional services firm, Softcat was asked for recommendations for a partner who could work closely as a true data partner over the long term.

Oakland was the obvious choice.

Oakland worked closely with Softcat throughout the engagement, building on Softcat’s long-standing relationship with the organisation and leveraging its partner ecosystem to accelerate vendor engagement and technical evaluation, particularly during the integration platform selection.

“We were really impressed with Oakland’s approach to building our data strategy. They understood where we were as a business, worked at pace, and delivered something that was clear, well articulated, and genuinely useful at senior leadership level. The team brought strong experience and were easy to engage with throughout.”

Technology Director

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The post Building a Data-Driven Professional Services Firm: A Data Strategy Journey appeared first on Oakland.

The Challenge

The organisation was grappling with two primary issues related to fibre network planning and sales processes:

1. Data Sharing and Monetisation Issues

The company struggled to monetise and share its existing fibre network data effectively across departments. Their network planning data was disconnected from customer acquisition efforts, creating inefficiencies and making it difficult for the sales team to identify and target businesses that could be served by their existing network.

2. Inefficient Sales and Network Planning Processes:

The sales team lacked visibility into the availability of fibre infrastructure, resulting in a reactive process. They would only assess fibre availability after receiving customer inquiries, often resulting in missed opportunities or inefficient resource allocation. Sales teams had to consult the network planning team to determine if existing fibre could service a new customer or if new infrastructure would be required, leading to delays and additional costs.

The reactive nature of this process not only caused operational inefficiencies but also contributed to the company’s significant reliance on leasing fibre lines from competitors, further increasing operational expenses (OPEX). Additionally, the company’s ability to identify strategic fibre expansion opportunities was limited, causing them to miss out on valuable contracts.

Engaging Stakeholders

We began by facilitating workshops with key stakeholders from both the Enterprise Sales Team and the Network Planning Team. These workshops allowed us to understand their pain points, workflows, and data needs, ensuring that the final solution addressed their specific challenges and objectives.

Data Visualisation and Mapping

A core part of the solution was the integration of GIS mapping data, and customer data into an interactive, visual dashboard. This dashboard enabled the sales team to instantly view where fibre was available in relation to potential customers, giving them the insights needed to make faster, more informed decisions.

Building the User Interface

The solution provided an intuitive interface design that would enable sales teams to quickly identify which customers could be served by existing fibre lines, and where new infrastructure was needed – as well as the propensity of those customers to buy further services.

The mapping capabilities gave them a clear view of fibre availability, including proximity to key business districts, reducing the time spent searching.

The Results

By identifying opportunities to expand existing infrastructure, rather than relying on costly leased lines from competitors, the company now has the blueprint to delivering a tool that will drive tangible value and generate revenue from their data.

“The integration of fibre availability data has completely changed the way we approach network planning and sales.

“The solution has streamlined our processes, reduced our reliance on leased fibre, and helped us proactively target customers in high-demand areas. It has not only improved our operational efficiency but also positioned us to grow our business in a more cost-effective and strategic manner.”

Digital Enablement Manager

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The post Smarter Network Planning with Fibre Data for a Large Telecommunications Organisation appeared first on Oakland.

The Solution

From the outset, it was clear that this wasn’t just a matter of implementing a new data platform. The problem ran deeper, touching governance, process, ownership, and culture. So instead of rushing to deploy new technology, Oakland began by getting to know the business, understanding how data flowed (or didn’t), identifying bottlenecks, and uncovering the root causes of mistrust in the data.

A Data Maturity Assessment

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

Rather than prescribing a one-size-fits-all solution, Oakland built a roadmap grounded in the organisation’s 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’t just be captured and stored, but managed, modelled, and used to drive performance across the business.

Engineering the Technical Platform

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. This made Microsoft Azure a natural fit.

Oakland conducted a detailed review of architectural options before recommending a hybrid approach centred on Microsoft Azure and Databricks. 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’t just about fixing today’s problems – it was about laying the groundwork for AI and advanced analytics in the future.

A Single Client View

At the heart of the solution was the goal of creating a single client view. Data from the company’s most essential systems – finance, service desk, and core operational platforms – was identified as critical. Integrating these sources would allow teams to gain a unified, trusted view of each customer.

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.

Discover

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.

Define

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.

Plan

The Plan phase focused on establishing governance frameworks and architectural blueprints, while mapping out a phased roadmap for growth.

Execute

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.

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.

A Cultural Shift in the Right Direction

Perhaps most importantly, the business now has a foundation to build on. Its data is managed, modelled, and visualised through Power BI, supported by a scalable Azure-based architecture that’s ready for future challenges – from advanced analytics to AI initiatives. The cultural shift is already underway: data is no longer a blocker or an afterthought but an enabler, woven into how the organisation operates and grows.

“This was such a great project to work on.

“The impact was clear almost straight away. Seeing teams come together around a single source of truth and leaders making confident, data-driven decisions has been brilliant. What’s even better is knowing this is just the beginning and our client has a solid, scalable foundation that’s setting them up for everything from advanced analytics to AI.

Matt Peckham, Oakland Principal Architect

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The post Delivering a Data Transformation for a Leading UK-Based Digital Infrastructure Provider appeared first on Oakland.

Unstructured Data

Over the years, the company has sold millions of technology products, ranging from software licenses and cloud services to hardware, networking equipment, and peripherals. The records of these sales were scattered across multiple systems, from SQL databases to countless Excel spreadsheets.

With thousands of people entering data over the decades, often as free text, in varying formats and levels of detail, consistency was difficult to achieve. The result was around eight million product records, but no reliable, standardised way to analyse or understand them.

Some entries were rich and detailed, with product names, codes, and metadata accurately recorded. Others were little more than shorthand notes, sometimes vague, sometimes incomplete, and occasionally completely unexpected (a “Barbie doll” had somehow made its way into the database).

Key Issues

This inconsistent data meant that the business couldn’t confidently answer some of its most important commercial questions, such as:

• Which products are driving revenue and margin?
• Which lines have been discontinued, and which are growing fastest?
• What product areas should the business focus on

For many companies running off legacy systems this lack of visibility can be a major limitation. The finance and sales teams were relying on gut instinct instead of trusted insight, which created risk and inefficiency.

Oakland was selected for its reputation as a trusted data and AI solutions provider. The team’s role was to bring order to complexity and create a scalable, AI-driven foundation for more informed, data-led decision-making.

“Working with Oakland made a huge difference for us.

“We had millions of product records over many different systems and very little consistency leading to poor data quality, and honestly, it was a mess. Ultimately, we had no structure, which led to no easy way to get insights. The Oakland team jumped in, rolled up their sleeves, and helped us turn all that chaos into a clear, organised AI solution (which was a first for us) that actually made sense for our business users.”

Head of Data

This is where an AI-driven data foundation could make the difference.

To unlock insight, Oakland needed to transform this vast, unstructured product database into a structured, navigable taxonomy.

Our data engineering and AI teams worked together to design a custom classification framework that mapped each product to the company’s five key business areas and drilled-down into more detailed categories beneath them.

A living, growing model rooted in data

Using large language models (LLMs) and a “human-in-the- loop” approach, we trained an AI system to read each product entry and decide what type of product it was, grouping similar items into meaningful, hierarchical categories.

At the highest level, products were grouped into hardware, software, or services. From there, the AI created more specific subcategories such as edge devices, compute, networking hardware, or enterprise applications.

Crucially, the system wasn’t limited to pre-defined labels. If it encountered a new kind of product, for example, 3D printers, it could automatically create a new category and slot it into the taxonomy. This adaptive classification ensured that the structure could evolve along with the business.

The result was a taxonomy that reflected the client’s real-world view of their product universe, not a generic industry template, but a living, growing model rooted in their data and their language.

Human expertise meets machine intelligence

While the AI handled the heavy lifting, human expertise remained vital. Oakland’s data and AI consultants collaborated closely with subject matter experts from across the business, from sales to finance and product management, to review, refine, and validate the AI’s classifications. This human-in-the-loop approach ensured that the final taxonomy not only made technical sense but also made business sense.

Instead of asking people to manually tag thousands of individual records, a process that would have taken years, we gave them a structured, explainable model to review. Adjustments could be made in hours, not months.

Scaling to production

Once the taxonomy was validated, Oakland built a scalable pipeline to classify hundreds of thousands of product records quickly and accurately.

We started with a sample of 20,000 products to train and test the model, then scaled to 300,000 records covering three full financial years, representing the majority of the company’s recent revenue.

The solution was deployed in a secure cloud environment using Azure’s OpenAI service, with custom tooling built by Oakland to manage data flow, compute requirements, and error handling.

This allowed us to process vast amounts of unstructured data in parallel, achieving what would previously have required years of manual effort in a matter of hours.

AI-driven efficiency and ROI

The time and cost savings were eye-opening.

Running the AI model across 300,000 product records costs less than £100. By contrast, a human team classifying the same volume manually would have taken around three years, not to mention the mind-numbing tedium of manually tagging thousands of product lines (precisely the kind of monotonous work AI was made for).

That’s a 1,000x improvement in speed and efficiency.

“Oakland didn’t just throw tech at the problem, instead choosing to work alongside our IT and business teams, making sure the solution was fit for what we really needed. Their mix of AI know-how and practical business sense meant we got results fast. Now, we can actually trust our data, answer important questions, and spot new opportunities in our product data.

“Oakland set us up with a solid foundation for our new LLM, helped us with all the documentation and handover of this new solution, and we’re in a much better place to move forward with confidence in our product data thanks to them.”

Head of Data

Your Turn

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The post AI Product Classification Solution at Scale appeared first on Oakland.

Discover

Using Oakland’s Data Maturity Framework, we assessed Simplify’s current state and identified key pain points, including:

• Low data quality and governance maturity
• Disconnected, manual analytics processes
• Lack of scalable infrastructure to support AI-driven use cases
• High time-to-insight and inefficient data workflows

We captured the voice of the business, which revealed a strong desire to return to their position as a digital leader in the market and offer a hyper-personalised customer experience, something they referred to as “psychic conveyancing.”

Define

The define phase focused on designing the vision and key pillars of the data strategy and associated initiatives to deliver on these, assessing the necessary changes across people, process, technology and data, ensuring the strategy’s sustainability and adoption.

This included defining their data platform architecture recommending Databricks as the core analytics engine – ideally suited to Simplify’s AI ambitions, whilst also delivering on conceptual designs and activation strategies to support initiative mobilisation.

We also identified a critical early use case: a case prediction model that could forecast the likelihood and timing of case completions vital for optimising revenue and operations.

Plan

We co-created a clear and actionable roadmap to implement the data strategy and deliver the required capabilities, grounded in commercial outcomes and prioritised use cases.
The roadmap focused on:

• Developing critical data foundations
• Enabling competitive advantage through better use of data
• Delivering strategic initiatives, such as real-time case
  forecasting
• Unlocking the value of the wider ecosystem (e.g., introducers,
  estate agents)

The roadmap was broken down into incremental components that met required inflection points, aligning to their Simplify 2.0 vision, with a more detailed plan, resourcing and budget prepared for first phase execution.

Governance mechanisms and KPIs were established to oversee the delivery process.

Execute

In a 12-week ‘Lighthouse’ delivery we were able to launch:

1. Data platform development – Delivering the Azure data platform, leveraging OMP and its templatised Terraform scripts (Infrastructure as Code) rapidly accelerating deployment.
2. Data ingestion – Connecting and ingesting raw data from two key case management systems.
3. Data transformation – Delivering highly curated and conformed data using medallion architecture around an agreed enterprise model.
4. Case Prediction Model – Machine learning insights on case completion risk and timing.

From Local Models to Scalable Insights

One of the first use cases delivered was the case prediction model. Previously manually developed, the model required 90% of one person’s time just to prep data. With OMP:

• Model training became faster and more accurate
• Versioning and model monitoring were integrated
• Predictions could now be made at the introducer level (e.g., Purplebricks), providing tailored insights
• Estate agents could receive an early warning on cases at risk of falling through

This capability helped Simplify offer partners insight-driven forecasting to reduce lost revenue and prioritise effort where it matters most.

Embedded Governance & Training

Recognising Simplify’s starting point, Oakland introduced a lightweight, scalable governance model using SharePoint to establish early data standards and glossaries without overengineering.

We also delivered detailed training, runbooks, and role-based skills assessments to ensure the platform could be owned and evolved internally. Strategic support continues through technical assurance as Simplify builds internal capability.

“The Oakland team did a great job in analysing and defining the data definitions, quality rules, and governance for this use case. Their professional approach in collaborating with both the business and data teams has reinforced the importance of using our data in a more controlled and trustworthy manner. Given that this project started in Simplify with no clear requirements or technical and business specifications, the well-documented materials covering every aspect of the project will serve as a staged process for our future data science and ML projects.”

– Mike Brace, Director of Data Operations & Strategy

The post Becoming AI Ready: A Data Strategy and Platform Build for Simplify appeared first on Oakland.

The Challenge

Their existing on-premises data warehouse struggled to support the growing analytical demands necessary for new propositions, such as fully analysing data on article submission trends, author and institution information, and enabling AI integration within solutions. The dispersed nature of their data sources, with varying formats and inconsistent quality, limited their insights and hindered their ability to grow and expand. Recognising the need for a more robust and future proof solution, Emerald planned to transition to a cloud-based data platform to ensure sustainability, innovation and growth.

The Solution

After a competitive pitch against a number of data consultants, Oakland was selected based on our exceptional service quality and deep understanding of Emerald’s goals. Oakland’s infrastructure and expertise were well-aligned with Emerald’s vision for a future-ready data analytics platform.

The Goal

Oakland was tasked with building a cloud-based data analytics platform designed to propel Emerald into the future. This platform needed to provide comprehensive data governance and quality functionality, reconciling conflicting data from diverse sources. Key requirements included:

• Integration of both batch and streaming data from disparate source systems.
• Secure and robust departmental self-service reporting capabilities, alongside support for data science and future machine learning initiatives.
• Financial viability and supportability by a dedicated internal data squad.
• Significant reduction in manual effort for reporting, integration, and maintenance.
• Compliance with data privacy laws, particularly GDPR.

This project was not solely about technology; it also involved identifying the necessary people, skills, workflows, and processes to sustain and enhance the new platform. By addressing these multifaceted needs, Oakland aimed to empower Emerald Publishing to fully leverage their data, driving the business forward in a data-driven publishing landscape.

The Technical Components

Microsoft Azure

Microsoft Azure offers a range of benefits for building a data platform, making it a popular choice for organisations looking to modernise their data infrastructure.

Azure offered:

• Scalability: Azure allows you to scale resources up or down based on your needs, ensuring cost-efficiency and performance optimisation.
• Comprehensive Services: Azure provides a wide range of integrated services including databases, analytics, and machine learning, facilitating end-to-end data management and analysis.
• Security: Robust security features, including advanced threat protection and encryption, ensure your data remains secure and compliant with regulations.
• Integration: Seamless integration with existing Microsoft products and other third-party services enhances productivity and collaboration.
• AI and Machine Learning: Built-in AI and machine learning capabilities allow you to derive insights and drive innovation from your data.

Microsoft Azure Synapse

Given the size and nature of Emerald’s data, which was heavily oriented towards a data warehouse approach, Azure Synapse Analytics emerged as a natural fit. Synapse, often regarded as the Azure equivalent of a data warehouse, provided the robust environment needed to handle complex queries and largescale data processing.

Emerald’s existing infrastructure was already familiar with SQL Server environments, and Synapse allowed for a seamless transition while offering enhanced capabilities for data integration, analysis, and reporting.

Apache Spark

Spark is known for its ability to handle large datasets and perform complex analytics at scale, making it a powerful addition to the data platform.

Emerald’s long-term goal included the integration of machine learning capabilities. Spark’s support for PySpark provided a natural fit for these advanced data science initiatives.

With Microsoft Fabric on the horizon, incorporating Spark pools into the architecture ensured that Emerald’s platform would be adaptable to future developments in the Microsoft ecosystem.

Balancing Familiarity and Innovation

One of the critical decisions was balancing new technology with Emerald’s existing expertise. While moving to Spark, we introduced a new query language (PySpark); we made this transition smooth through extensive knowledge transfer and collaboration.

We conducted numerous training sessions and hands-on workshops, enabling Emerald’s team to become proficient with PySpark and effectively leverage the new capabilities.

Phased Approach

The project was executed in two key phases:

• Phase 1: Infrastructure Setup: Using Terraform, we established the core infrastructure, ensuring it was scalable, secure, and aligned with Emerald’s needs. This phase also included the initial onboarding of data sources and the implementation of repeatable data quality reporting functionality.
• Phase 2: Enhancing Capabilities: With additional budget allocation, we expanded the data sources and focused heavily on integrating machine learning capabilities, transitioning Emerald’s ML processes from legacy systems to a more modern and streamlined environment within Azure.

The combination of Azure Synapse and Spark allowed us to build a platform that not only meets Emerald’s current needs, but also positions them for future growth. This strategic choice of technology ensures that Emerald can continue to innovate in the rapidly changing publishing landscape, with a robust data platform that supports everything from basic analytics to advanced machine learning.

“Right from the start, the relationship with Oakland has been a strong one. There was a close cultural match between our two organisations, and it genuinely felt like we were working as one team. This kind of open and genuine working relationship isn’t always the norm, but with Oakland, it certainly has been.
The level of collaboration and adaptability throughout the project has been exceptional, making the entire experience incredibly positive. I couldn’t be more pleased with how we’ve worked together.”

– Daniel Molesworth, Emerald Publishing

The post Leveraging Microsoft Azure for Market Expansion and Growth appeared first on Oakland.

Challenges:

• Difficulties in accessing and sharing data
• Fragmented and duplicated databases
• Uncertain data quality
• Lack of control over data assets, compromising trust and assurance in data

Client’s Objectives:

The client approached Oakland for assistance in addressing these challenges and achieving the following goals:

• Establish confidence and trust in data through right-sized data governance and data quality frameworks.
• Enhance business operations and asset efficiency by ensuring accessibility, usability, and quality of insights derived from data.
• Establish a data-driven culture and an insight-led workforce through data governance training and support.

Tangible Outputs:

Our engagement yielded tangible outputs, showcasing the transformational impact of our work:

• Data Governance Framework: Tailored to the organization’s model, outlining a structured approach to data governance.
• Data Quality Framework: Detailing proactive and reactive processes to implement data quality across various data domains.
• Data Governance E-learning Training: Provided comprehensive understanding of data governance, its key components, and benefits for all employees.
• Identification of Key Stakeholders: Identified Data Owners, Stewards, and other key stakeholders for crucial data domains.
• Capacity Building Training: Created Data Ownership and Data Stewardship training to enhance capacity and improve data literacy necessary for responsibilities.
• Data Governance Operating Model: Established a comprehensive operating model for effective data governance.
• Results:
• Data governance has evolved from a “nice-to-have” capability to a critical operating capability. A thriving Data Governance community with committed Data Owners and Stewards has streamlined accountability and responsibility for data. The client now operates with increased confidence and trust in their data, paving the way for more efficient operations and informed decision-making.

In conclusion, our collaboration with the major UK Utility stands as a testament to the transformative power of effective data governance in enhancing organisational efficiency and building a data-centric culture.

Water Utilities

The post Transforming Data Governance for a Major UK Utility appeared first on Oakland.

Operating across 26 regions, they build homes all over the UK through their respected brands; Bovis Homes, Linden Homes, and Countryside Homes leading their retail sales and Countryside Partnerships driving growth through their strategic partnership model.

As part of Vistry Group’s mission to deliver sustainable homes, the company needed to improve its sustainability reporting. Their current reporting had grown organically, and after a number of previous mergers and acquisitions, their data estate was complex. This was further complicated by a federated regional business model, leading to challenges in standardising processes and driving consistency in reporting.

“It was clear Oakland had the experience and expertise to help us to deliver our goals. Their presentation stood out and was clearly well thought out, not the cookie cutter pitch we had seen from others.”

Tailoring the solution

Oakland began with a focused 5-week “define” phase to understand Vistry Group’s current state and required processes. To successfully engage with the regional business units, we needed a robust plan and toolkit that was easily understood and didn’t blind everyone with jargon.

Oakland’s rapid MVP processes would allow Vistry Group to engage the business alongside the launch of their new sustainability data collection tool. This was a critical driver to land initial BU engagement and adoption.

The process

• Discovery workshops with key stakeholders on vision, current state, emissions reporting requirements, and strategic data use cases.
• 3rd party partner and contractor consultation and review of existing reports, processes, and requirements.
• Design workshops with Group Sustainability on sustainability reporting processes and framework.
• Engagement planning workshops.
• Creation of initial engagement plan, templates, processes,and framework.

“Our current sustainability reporting has grown organically and isn’t scalable. We have a heavy reliance on 3rd parties and most development so far has been reactive.”

Oakland delivered a comprehensive transformation plan to deliver Vistry Group’s ongoing and future reporting needs. The current state summary and consolidated findings provided a clear narrative of the existing challenges and the compelling case for change.

This transformation included documenting reporting requirements to clearly define business needs and priorities. A thorough gap analysis and recommendations across data capability ensured that Vistry Group can meet their vision by delivering the required sustainability processes.

Standardised end-to-end processes were established, creating clear value streams from data definition, acquisition, transformation, and analysis through to reporting and continuous improvement. A defined framework, including roles and responsibilities, was developed, driving consistency and standardisation across the group.

An engagement toolkit and plan were also created to support communication with business units in subsequent phases, ensuring buy-in and adoption. This cohesive approach has equipped Vistry Group with a robust and future-proof sustainability reporting system, capable of generating ongoing value and supporting their sustainability strategy.

Beyond the original scope, Oakland added additional value through documentation of associated sustainability data models. The provision of a future data and solution architecture to support their vision and delivery of sustainability reporting processes. In addition to creating strategic use cases for further value realisation from data beyond initial sustainability reporting obligations.

In conclusion, the consistent and standardised reporting processes have not only streamlined operations but also positioned Vistry Group to leverage data as a strategic asset, driving further value and ensuring robust sustainability practices across the organisation.

“I have never worked with a consultancy I couldn’t fault. The delivery was first rate and very detailed.”

Julia Anukam – Senior Sustainability Manager

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