Insurers have long had access to vast amounts of data, the volume of which has continued to grow at speed in the digital age. As an industry that relies heavily on actuarial models and traditional underwriting processes, ensuring the efficient and effective management of that data to harness its value to solve business problems is ever more important.

However, many insurers find it challenging to unlock the true value of data when it comes to delivering personalised customer experiences and innovative products offerings, and driving operational excellence in areas such as claims cost management – while on top of all that growing sustainably.

In this blog, we will explore some of the challenges around unlocking the value of data and how insurers could address them.

Disjointed executive sponsorship and limited data funding, leading to ‘start-stop-restart-stop’ data deliveries

Focused sponsorship and commitment from leadership with joint investments across business teams are key to solving business prioritisation and data challenges. Every data project should start with a clear business case, including a roadmap for delivery that can be monitored with clearly defined KPIs, such as ROI, user satisfaction scores of data products launched, and usage base monitoring for data products.

Insurers need to be linking clear returns on investment for data projects that will solve prioritised business problems. This will help reduce the stop-start data projects that occur in firms. One useful way of obtaining senior leadership sponsorship and agreed funding is to demonstrate value quickly through proof of concepts (POC) that demonstrate how critical business objectives (e.g. cost savings) can be achieved once the POC is taken into production.

The haphazard selection of use cases for change across the organisation can lead to duplication of effort and a damaging lack of focus

Insurers should adopt a domain-centric operating model when curating data use cases to enable marginal cost of development and accelerate the reuse of assets.

For example, as seen in the next figure, by taking a big picture view to spot similar patterns and common solutions, insurers may be able to to eliminate siloed ways of working by looking across the underlying data requirements, such as customer, claims and suppliers. This more holistic perspective will also help identify where harmonised platforms and architecture can be leveraged to enable industrialisation across a range of data solutions, from business intelligence to advanced analytics.



When curating and defining a portfolio of use cases that aligns to business objectives, it is important to identify the interdependencies and relationships between the constituent use cases with a view to unlocking reusability and increasing realisation of value over time. This approach also means time to value (i.e. production) can be accelerated.

The key is to start simple, proof it via a POC, get the delivery right, track against ROI, build confidence amongst sponsors, senior leadership and end users – this will attract more buy-in to further build on use cases and foundations and over time the insurer’s data capabilities will be strengthened, while also encouraging the consistent application of advanced tools which will organically ensure a more upskilled team.


Limited business and data/IT collaboration around data delivery leads to siloed ways of working and low levels of data product adoption once deployed

This challenge is closely linked to the use case selection challenge, in that there needs to be close collaboration and tight expectation management when it comes to what the business needs and what the technical teams can deliver.

A key step is to collate the common data pain points across business teams and determine a common need for change during the discovery phase. This curation and collaboration will help the data/IT teams to plan the delivery roadmap for building iterative, reusable and scalable data products and assets that delivers incremental value and ROI.

The most successful insurance data projects have both a business sponsor and a technical sponsor who work together on the end-to-end delivery, and involve business SMEs in the ‘discovery, design, build, test, operate, transfer’ (DDBOT) delivery framework.

For example, when an insurer looks to digitise and improve the customer experience, the insurer needs to be reviewing customer touch points across the customer journey – an example of a simple customer end-to-end insurance journey is seen next.

If an insurer understands how each of their target segments behave or prefer to be service along the above E2E journey, they will be more effective in prioritising change projects that will improve customer experience.

To obtain competitive advantage, insurers should be looking on ways to improve collaboration across their business teams such marketing, sales & distribution, claims, complaints and underwriting teams with their data/IT teams. If an insurer is able work collaboratively across these teams to understand the common pain points that are blocking the insurer from achieving its strategic ambition, it will get a clearer strategic understanding of the common business/data/IT challenges face in each business team, and it will drive better prioritisation analysis to design a more pragmatic change implementation roadmap of use case cases where business and IT/data can deliver change that looks to build incremental value of reusable assets over time.

Cultural resistance to adoption of modern ways of working and/or modern technology

Insurers should address this challenge with sensitivity. It is important to be clear on the underlying reasons for change and have a thoughtful approach for addressing and communicating them. Strategies to adopt include:

• Clear communication and transparency around the value of the data changes and how these will benefit employees individually – e.g. automating the production of reports allows them to focus on more value-added activities, such as analysing the report’s insights for quicker decision-making.
• Involve employees in the DDBOT delivery framework so that they feel a part of the change and become data champions for the data products/assets when deployed – e.g. if an insurer is planning on shifting to PowerBI from Excel reporting or SAS reporting, it may want to consider uplifting the skills of its BAU data reporting analysts by assigning them to work with the transformation team as part of the data delivery programme.
• Conduct data literacy training to provide comprehensive education on the new processes, tools and technologies; and demonstrate quick wins on how these translate to greater workload efficiencies.
• Regularly evaluate employees’ data literacy levels, and tailor career and learning pathways to address gaps that are impeding delivery of the business strategy.
• Appoint data champions to advocate change by sharing their positive experiences and providing peer support.

Summary

The above challenges are not new to insurers – not least because solving them is often easier said than done.

However, those insurers who have invested in improving their data foundations through a focused delivery roadmap for lasting change are beginning to reap the returns of this investment. They are beginning to generate cost savings – whether through operational efficiencies, streamlined and personalised customer services, or even cross-selling/upselling to existing customers – because they are harnessing the power of their data effectively and efficiently.