Data-driven insurance - data culture and data-driven mindset

Data-driven insurance (DDI) is not only about data and the added value it generates. It is also about people.

When you consider the valuable role played by employees at an insurance company, a traditional change management process that is employed, for example, to implement new structures, strategies, processes or behaviours and paves the way for change within the organisation is simply not enough anymore. In making the transition to become a data-driven insurance company, they need to create a new awareness for a data culture that affects everyone at the company. It is essential to create a mindset in which an employee sees the value in the information gained from data and takes action based on data.

People are affected on several levels  both as an individual and as part of an organisational structure.

Organisational change

A data-driven insurance company makes decisions on the basis of data and the models generated from it. Use of data transcends company boundaries. Beyond that, traditional business structures are becoming blurred with regard to the use of data and the adoption of business models, which has an impact on the organisation and collaboration within it. It is necessary to break down silo structures and the boundaries that separate specialist units from the IT department in order to improve collaboration through an interdisciplinary approach. Creating cross-functional teams is just the first step. Data-based business models cover all departments, which is why it is essential to deploy a professionally managed change management process running alongside them. Employees need a framework within the change process to deal with uncertainties and provide fresh ideas, which in turn places them in an active role in the process.

Data-driven mindset

Having information available within a data-driven insurance company enables any employee to access the data that is relevant and visible to them and to make decisions based on this. They require training on how to handle data to ensure correct analysis and careful use of information and quickly solve problems on the basis of KPIs. New principles also need to be established to promote a data-driven mindset within the company. Organisational change and the associated availability of information at the department level help pave the way for this.

However, a data-driven mindset also means relying on data when making a decision instead of trusting your intuition or gut feeling. In an industry like insurance that provide coverage for everything from healthcare to property, this may not leave everyone satisfied. With the transfer of risk, trust from the customer plays a critical role, since policyholders depends on the insurance company’s promise of protection and rely on the fact that it will pay out their claim should it come to that. The refusal to pay a claim can mean economic ruin for many policyholders.

So why rely on data more than the employee’s experience and intuition when it comes to a product that is both emotional and is built on trust?

The mindset is called data-driven not data-based, and there’s a good reason for this. In the insurance industry – or rather in individual processes and lines of business – employees must be given a certain amount of leeway on whether to rely on their intuition or follow the data. It is also important that they are able to analyse and accept errors.

Innovation workshops and data labs

In the past, many projects in the field of machine learning were too small in scale. They focused too much on technology and not enough on the solution space. As a result, many of these solutions never left the proof of concept stage and ultimately came to nothing.

But how do you ‘start the journey’?

As I mentioned at the beginning, the traditional boundaries are blurring both within the organisation and with the world outside the company, which must be taken into account when assessing and planning data-driven business models. It is necessary to create an understanding of current technologies and future trends because looking to the future opens up the space for innovation and potential solutions.

This step can take the form of an innovation workshop carried out with an interdisciplinary team. The workshop is divided into three phases that each cover different aspects:

• 1. Creating an understanding
• 2. Idea generation and development of an innovation roadmap
• 3. Validation of possible solutions and decision-making

The method described above is used to launch an innovation project. It is done in preparation for setting up a data lab, which provides a permanent, company-wide platform for innovation, with the aim of making ideas available in the form of MVPs within a very short period of time. As an internal unit, data labs facilitate close interdisciplinary collaboration with the various different departments, knowledge developers and data scientists who in turn develop and test prototypes and ideas and thus provide data for evaluation. They are permanently established at the insurance company to help generate new data-driven business ideas.

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