{"id":9597,"date":"2025-07-28T13:55:31","date_gmt":"2025-07-28T13:55:31","guid":{"rendered":"https:\/\/weareoakland.com\/?post_type=case_studies&p=9597"},"modified":"2025-11-26T16:04:32","modified_gmt":"2025-11-26T16:04:32","slug":"becoming-ai-ready-a-data-strategy-and-platform-build-for-simplify","status":"publish","type":"case_studies","link":"https:\/\/weareoakland.com\/case-studies\/becoming-ai-ready-a-data-strategy-and-platform-build-for-simplify\/","title":{"rendered":"Becoming AI Ready: A Data Strategy and Platform Build for Simplify"},"content":{"rendered":"\n
\n
\n

The Challenge:\n<\/h2>

Fragmented data!<\/h3>\n

Simplify is the UK\u2019s 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.<\/p>\n

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.<\/p>\n

Simplify had a clear digital vision – Simplify 2.0 focused on AI, automation, and innovation. But without a strong data foundation, that vision couldn\u2019t be realised.<\/p>\n <\/div>\n<\/section>\n\n\n

\n
\n

The Solution:\n<\/h2>

A strategy for data and an Azure data platform<\/h3>\n

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.<\/p>\n <\/div>\n<\/section>\n\n\n

<\/div>\n\n\n\n

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

Using Oakland\u2019s Data Maturity Framework, we assessed Simplify\u2019s current state and identified key pain points, including:<\/p>\n\n\n\n

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

    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 \u201cpsychic conveyancing.\u201d<\/p>\n\n\n\n

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

    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\u2019s sustainability and adoption.<\/p>\n\n\n\n

    This included defining their data platform architecture recommending Databricks as the core analytics engine – ideally suited to Simplify\u2019s AI ambitions, whilst also delivering on conceptual designs and activation strategies to support initiative mobilisation.<\/p>\n\n\n\n

    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.<\/p>\n\n\n\n

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

    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:<\/p>\n\n\n\n

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

      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.<\/p>\n\n\n\n

      Governance mechanisms and KPIs were established to oversee the delivery process.<\/p>\n\n\n\n

      Execute<\/h3>\n\n\n\n

      In a 12-week \u2018Lighthouse\u2019 delivery we were able to launch:<\/p>\n\n\n\n

        \n
      1. Data platform development<\/strong> \u2013 Delivering the Azure data platform, leveraging OMP and its templatised Terraform scripts (Infrastructure as Code) rapidly accelerating deployment.<\/li>\n\n\n\n
      2. Data ingestion<\/strong> \u2013 Connecting and ingesting raw data from two key case management systems.<\/li>\n\n\n\n
      3. Data transformation<\/strong> \u2013 Delivering highly curated and conformed data using medallion architecture around an agreed enterprise model.<\/li>\n\n\n\n
      4. Case Prediction Model<\/strong> \u2013 Machine learning insights on case completion risk and timing.<\/li>\n<\/ol>\n\n\n\n
        \n
        \n

        The Outcome:\n<\/h2>

        An AI ready data platform in 12 weeks<\/h3>\n

        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:<\/p>\n

          \n
        1. Security by design<\/li>\n
        2. DevOps automation<\/li>\n
        3. Modular, future-proof architecture<\/li>\n
        4. Full documentation and best practice guidance<\/li>\n<\/ol>\n <\/div>\n<\/section>\n\n\n
          <\/div>\n\n\n\n

          From Local Models to Scalable Insights<\/h3>\n\n\n\n

          One of the first use cases delivered was the case prediction model. Previously manually developed, the model required 90% of one person\u2019s time just to prep data. With OMP:<\/p>\n\n\n\n

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

            This capability helped Simplify offer partners insight-driven forecasting to reduce lost revenue and prioritise effort where it matters most.<\/p>\n\n\n\n

            Embedded Governance & Training<\/h3>\n\n\n\n

            Recognising Simplify\u2019s starting point, Oakland introduced a lightweight, scalable governance model using SharePoint to establish early data standards and glossaries without overengineering.<\/p>\n\n\n\n

            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.<\/p>\n\n\n\n

            <\/div>\n\n\n
            \n