Delivering business value for insurance companies

Recapping a discussion moderated by Stijn Christiaens and featuring insurance data experts from Deloitte UK 

Insurance is a data-intensive business. Insurance companies need data to better assess risks and price policies competitively, but also profitably. They need data to better manage claims, ensuring customer satisfaction but also mitigating fraud. And they need data to better manage their assets and liabilities, helping to plan cash flows and drive sustainable profits.

Given the data-intensive nature of their operations, it is natural that many insurance companies are looking towards the cloud as an infrastructure choice to help enable more agile data operations.

On that topic, I had the opportunity to speak on a webinar that brought together insurance data experts from Deloitte in the UK, and data and analytics leader Peter Jackson, who heads up group data sciences for Legal & General. In case you missed the session, I am summarizing some of the key themes in the remainder of this blog (or you can access a replay here).

Drivers for cloud adoption

When looking at drivers for cloud adoption, there are many factors, both internal and external, that attract organizations to the cloud, notes Toby Waldock, an insurance partner and head of the data modernization practice at Deloitte.

Internally, business leaders see cloud technologies as offering the potential to collaborate more effectively, transform products and services, improve customer experience, manage channel interactions and drive advanced analytics. At the same time, technology leaders see cloud architectures enabling more scalable and agile IT operations, while also potentially saving costs (in particular capital expenditure). Bridging this gap in expectations are data leaders, who play a vital role in any successful cloud strategy.

Beyond those internal drivers, external factors impacting cloud adoption include competitive threats (from agile insure-tech upstarts, unencumbered by legacy technology) and the impact of regulatory changes. An example of this kind of regulatory driver is the FCA’s pricing review (due in 2022), which could trigger new business processes requirements. This in turn prompts greater emphasis on data and analytics, says Deloitte insurance partner Reny Vargis-Cheriyan.  

Data governance holds key to cloud migration

Whatever the reasons driving cloud adoption, organizations need to see data governance as a crucial factor supporting successful migrations. Good housekeeping is one aspect of governance and expanding on that analogy, Deloitte senior manager Nikhil Deepak notes that cloud migrations share similarities with moving to a new house. They are both opportunities to de-clutter, getting rid of things we no longer need, and procuring new things that are more suited to our new environment.

Oscar Lowe, a director in Deloitte’s insurance practice, notes that data governance is a common thread running through all three key phases of cloud migration – including pre-migration planning, migration execution, and supporting ongoing operations in the cloud. It is therefore vital that organizations implement an enterprise data catalog, so they know exactly what data they have and where it is stored, while data lineage is also crucial in helping to understand data flows and dependencies.

Mobilizing data governance programs

Recognizing that cloud offers an increasingly compelling proposition to insurance companies, and that data governance plays a crucial role in effective cloud adoption, we concluded with insights into how to mobilize a successful data governance program, with Peter Jackson, former head of group data sciences at Legal & General, outlining six key steps:

  1. Education and communications: One of the biggest challenges of data governance lies in securing buy-in from across the organization, explaining not only the benefits, but also what is required to achieve those benefits. “This is a long journey that needs constant sponsorship,” notes Jackson.
  2. Framework: Data governance requires frameworks to be in place to ensure everyone is aware of their roles and responsibilities, key processes are clearly defined, risk frameworks are in place, and ultimately a blueprint is established to ensure everything runs smoothly.  
  3. The technology: Procurement of technology can then start supporting those frameworks but doing so effectively means establishing clear ownership and accountability, both from an IT and business perspective.
  4. The skills: The right people with the right skill sets will then be required to bring those frameworks and technology to life. “You mustn’t underestimate how hard it is to find the skills,” says Jackson.
  5. The program: Once you have the previous steps in place, organizations need to define what their data governance program will look like and which areas to prioritize. “We believe that to eat the elephant in one go would be overwhelming and it would be hard to show early success that would fuel the program,” says Jackson.
  6. The communications: The final piece in the puzzle, bringing everything around full circle, is to continue communicating with stakeholders to ensure the data governance journey remains on track. “You have to keep communicating to the business ‘what has been done, what is the value of what has been delivered so far, what is going to happen next and what are the implications of that,” says Jackson. 

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