The Challenge:
Fragmented data!
Simplify is the UK’s market-leading conveyancing business and has a bold ambition: to be the go-to home move partner of choice, supporting customers not just through conveyancing, but across the complete home-moving journey.
However, their existing data capability was limiting that ambition. Their data was fragmented, difficult to access, and largely underutilised. Many teams were working off local files, with analysts spending up to 90% of their time wrangling data. A significant amount of insight was locked away, unable to scale, automate, or drive meaningful decisions.
Simplify had a clear digital vision – Simplify 2.0 focused on AI, automation, and innovation. But without a strong data foundation, that vision couldn’t be realised.
The Solution:
A strategy for data and an Azure data platform
Oakland partnered with Simplify to design a robust data strategy and implement a modern, scalable data platform using the Oakland Modular Platform (OMP). Our work followed our proven methodology: Discover, Define, Plan & Execute.
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:
- Data platform development – Delivering the Azure data platform, leveraging OMP and its templatised Terraform scripts (Infrastructure as Code) rapidly accelerating deployment.
- Data ingestion – Connecting and ingesting raw data from two key case management systems.
- Data transformation – Delivering highly curated and conformed data using medallion architecture around an agreed enterprise model.
- Case Prediction Model – Machine learning insights on case completion risk and timing.
The Outcome:
An AI ready data platform in 12 weeks
Oakland deployed the OMP, creating a resilient, scalable Azurebased platform in a matter of weeks. Unlike typical consultancy accelerators or PoC-first SaaS tools, OMP is production-ready by default, incorporating:
- Security by design
- DevOps automation
- Modular, future-proof architecture
- Full documentation and best practice guidance
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 Results
A clear data strategy rooted in business outcomes and the creation of a resilient, scalable Azure-based platform in a matter of weeks using the Oakland Modular Platform.
By landing a fit-for-purpose data platform, Simplify dramatically accelerated its reporting and analytics capabilities, cutting the time to produce new or updated reports from months to just days or weeks. Through automated data pipelines and streamlined transformations, the platform significantly reduced the manual burden on data analysts, engineers, and scientists. This has resulted in an estimated annual saving of over £200,000 in resource time, with scalable infrastructure that now supports faster insights and greater agility across the business.
Simplify now has the foundation to scale advanced analytics and AI across the whole business.
“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