Accelerating Time to Value Through a Use-Case Approach

A data strategy cannot be a static piece of paper that is delivered piece by piece. A data strategy that isn’t connected to the overall business strategy and goals, will not be successful in the current competitive environment insurers find themselves. Alignment to the business strategy is key, and when the market dynamics change or disruption occurs and the CEO needs to pivot, the data strategy can also pivot, reflective of the new course setting. As business strategies today have changed to be more emergent, agile, and entrepreneurial, the data strategy needs to be reflective of that.

Gone are the days when we could slowly transform through long, arduous data governance programmes that took two to three years to implement all of the necessary principles, policies, and procedures. These programmes are largely academic in nature and haven’t been able to add the value that has been promised. This type of delivery is expensive, slow, and difficult to quantify, preventing insurers from meeting their objectives and capturing value.

This is why we advocate for a “use case” approach (data initiatives that create the link to deliver the business goals such as revenue, profitability, cost, and efficiencies) that enable business value to be created.

Our approach is to identify specific business goals that everyone in the organization recognizes and demonstrate how an effective data capability delivers business outcomes and results, which allows us to agree a costed approach to delivery.

We start with a set of priority data initiatives that underpin the strategic goals, the foundations that are required to deliver these goals i.e. people, process, data, and technology, ensuring we deliver the most benefit to the organization, in the short to medium term (3-6 months) with the right use case. We then follow that up iteratively to deliver further use cases while rolling out and building on the foundational capability.

This is a building-block approach that reuses data and capabilities from previous use cases, making it more efficient to scale, fix quality issues and deliver with agility. For example, with a use case such as minor damage to vehicles, the key starting question is how we can use data more effectively to automate the process, reduce costs, and improve customer experience.

Use cases should be delivered in waves, with the first wave improving approximately 80% of the data and capabilities required for all of the company’s foundational use cases. The higher this percentage, the more quickly subsequent waves of use cases can be implemented (eventually moving beyond foundational use cases to differentiating use cases).

These are not technology use cases, but rather use cases focused on delivering business outcomes and results, that have the right level of people, process, data, automation, and technology that will enable them. We don’t believe in dumping data into a data lake and then figuring out what to do with it; this creates far too much data and technical debt and marginalizes both data and business teams. As many companies have experienced, this approach is expensive and delivers very little business value.

The true art of sequencing is in selecting the right prioritized use cases that will yield results quickly while also accelerating future use cases, leveraging the capabilities that have been built incrementally.