The FTSE-250 UK-based life insurance provider, Just Group plc (Just), required an enterprise data platform to deliver its strategy to become a more data-driven organisation. 

    BJSS carried out workshops with Just to scope requirements, identify use cases and ensure data governance could be embedded in the business and data teams. The work carried out by BJSS was completed in a time-effective manner, with the first use case being delivered within just eight weeks.  

    The work of the BJSS team will help Just uncover savings and revenue growth to provide a significant return on investment.  

    The Challenge

    The workshops offered an opportunity for BJSS to understand the requirements of Just. As a result, the following areas of interest were identified for adoption: data platform/infrastructure architecture, data governance, data engineering, data science and the use of citizen data scientists, Power BI, and data visualisation.  

    The capabilities of modern data platforms using Microsoft Azure were also discussed in these initial workshops. A use case was identified for a Proof-of-Value (PoV) project that could demonstrate the capabilities of Microsoft Azure and serve as a design blueprint for the architecture of the enterprise data platform. 

    The agreed use case was to provide insights that would allow Just to optimise its Lifetime Mortgage prospect pipeline. The Just team had suspected that more revenue could be realised with improved insight into the prospect pipeline.  

    The Solution

    Using a framework and accelerators from BJSS, the Lifetime Mortgage pipeline was identified as requiring new insights to optimise revenue generation and business information/management information (BI/MI) dashboards were delivered to employees working on the project to support these decisions. 

    A team of six was formed to take on the project. This team included a delivery manager, a data architect, two data engineers, a platform engineer and a data analyst. It was at this stage that the work began in discovering the different data sources, mapping out business processes and gaining a solid understanding of how different customer interactions generated data in source systems. 

    In addition, a minimal data platform was created that provided a means to ingest, cleanse and validate the data. The data was then conformed to a data model and presented to business decision makers. A model was created for Just aligned to its sales process, including all the KPIs required to track and understand performance and the customer journey. It was vital to ensure that all Just’s non-functional requirements around security, performance and scalability were also met. The BJSS team then created the report and worked with business users to make sure it accurately showed the required figures. 

    When it came to the first iteration of the report, the confidence of seeing Just’s data in Power BI and the ability to interact with it was a massive leap in capabilities. This initial step allowed for further iterations of the report to be developed. 

    Simultaneously, meetings were held with other teams and stakeholders to gain an understanding of the data that they consumed and produced and how data was used by their systems and processes. The information allowed for the enterprise data platform architecture to be built to meet the future needs around document processing, executing scalable actuarial models, advanced analysis of data, and data sharing with third parties. As well as all the functional needs and capabilities for the platform being captured, so too were the data governance, privacy, retention and security requirements, with these being employed to help design the new platform. 

    At the end of the PoV phase, Just measured the value delivered by the data and analytics that were produced by the BJSS team and found that the theory that insight could help improve outcomes for customers was correct. New analytics highlighted areas for improvement within data capture to reduce friction for customers. This allowed the team to maintain timely communication with the customer on their application journey. Overall, this improved the efficiency of the sales team and led to better customer outcomes. 

    Following this, the BJSS team shifted its focus to scaling the platform and building the enterprise data platform fully, bringing more data sources to the platform and providing training to the teams at Just. An additional four data sources were integrated into the start of the “Customer 360” model. BJSS also continued to support and evolve the datasets and analytics that were provided to Just through the PoV.  

    The BJSS team ensured all the data pipelines were feeding information into the data governance tooling, enabling Just to catalogue data assets, view the quality of data being ingested, align data to business glossaries and to record the lineage of all data processing.  

    Outcomes

    The partnership between BJSS and Just resulted in wide-ranging outcomes across all aspects of the business. Data governance was embedded through the data team to ensure regulatory compliance was met and to ensure high-quality, trusted analytics were delivered to end users. Data governance and data compliance teams at Just were provided with training on adopting the tooling. Data proficiency was increased in business and data teams and rapid iteration of analytics became possible. 

    In terms of outcomes for Just colleagues, a well-architected review of existing cloud estate identified new skills and roles for operations and management of the platform and data. While delivering the data platform for Just, BJSS also helped train internal teams to use the tools. Since then, BJSS has assisted Just with data pipeline performance and optimisation, both through assessment of new pipelines created by their new delivery partner and by providing augmentation to their team to deliver some of the recommendations, with pipeline processing time halved.  

    The data scientists at Just have been supported to create dashboards using natural language processing (NLP) models to analyse customer complaints and survey feedback data to support their obligations to Financial Conduct Authority consumer duty regulations. Ongoing data architecture support is being provided to enable future initiatives and to help steer the data engineering and analysis teams. 

    Partnering with BJSS has resulted in greater data insights for Just Group. The organisation can now identify and irradicate areas of potential revenue loss. The enterprise data platform has been a critical deliverable of their data strategy, resulting in Just becoming a more data-driven organisation. 

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