Cyber risk management can be challenging for (re)insurers — but these problems are not going to be addressed by unnecessarily adding another tool. They need data they can trust in a format that aids decision-making as part of their daily workflow.
Where change management comes in
Analytics are only as valuable to the extent they are used, so thoroughly embedding them within an organization is key. Some (re)insurers may bite off more than they can chew when it comes to change management. Getting this transition period right is crucial for teams who want to use data to its maximum potential. If your teams are overwhelmed with new tools and irrelevant data that can’t fit into their existing workflows, the expense of the transition won’t be worth it.
Gain value with incremental changes
While the change management process can seem like a big project, it’s important to embed changes on a micro level to achieve quick wins. Start small by embedding the tool within the immediate teams who will get most value out of it and then reach out further, focusing on outcomes — as we outline below.
This blog will delve into how insurers can solve overarching problems using the appropriate cyber analytics, as well as how to use cyber data effectively so organizations can reach targets.
How to ensure analytics are being leveraged
There are many ways to go about implementing a change management process that involves a new tool. As mentioned, focusing on delivering outcomes based on company objectives is key.
Set a vision
Being able to envision how analytics will be used in the future will put (re)insurers on the right path to building the right structures to get there. Teams need to think about how analytics will be used immediately to help them reach their short-term goals. This vision should be:
- Comprehensive — the role of analytics should be defined across all operations within an organization. This vision should be tied to a company’s overall risk philosophy so that it can be thoroughly adopted at all levels.
- Credible — teams should think about whether there is a clear path to get from where they are now to where they want to be in the future.
- Strategic — the strategic direction is set, but the tactics could shift based on market developments so teams should be ready to react accordingly.
Place people at the center
When carrying out change management, the people of the organization should be at the center. Companies must consider what degree of change will occur for employees’ day-to-day work, how knowledge is shared and how the overall organization’s philosophy will change.
Change management plans should be commensurate with the level of change and they should:
- Define the job roles and skills of the future, e.g. asking questions like how will the team spend its time in the future? What skills are needed to be successful?
- Incorporate employees’ strengths into the vision
- Understand the degree of change and build a change management plan in line with this
Other questions to ask when embedding analytics within an organization include do people know what the data is and do they trust it? Embedding a new tool shouldn’t feel overwhelming, but it’s important to roll out this change thoughtfully with small steps — several people and roles will potentially be a part of this and need to know how to use the data effectively, ensuring there is a return on investment.
A successful transformation should build excitement across a business, with employees being involved in the rollout of this technological addition. Creating a strategy around the technology and then fitting this to the changes in roles can ensure that the transition is more smoothly and successfully adopted across teams.
Potential common pitfalls to watch for
Implementing the right strategies when embedding cyber analytics into an organization is critical, but it is only the first step to success. (Re)insurers also need to be aware of any potential common pitfalls they could encounter.
1. Lack of confidence
One of these pitfalls is lack of confidence. If leaders in the organization don’t have confidence in the tool they’re adopting, this can lead to a lack of use across the teams. Leaders must set an example and make clear the value that the addition of data and analytics will provide for the organization.
2. A rushed rollout
(Re)insurers also need to recognize the educational component to an analytics transformation. They must apply gradual ramp-ups during this change period, ensuring that teams can take the time they need to get to grips with a new tool. This will also help employees to build trust in the new data and analytics presented to them.
3. Lack of transparency
Some scenarios and use cases will be better suited to guidance from the data and it’s important that (re)insurers are transparent about this from the start. This will help during the adoption period, as users will know what to expect from these analytics.
4. Working in a silo
When implemented in a silo, analytics can work for some parts of the organization but fall flat overall. Siloed execution leads to a lot of new tools with little overarching philosophy, leading to the organization being unable to fully commit to their investment. A culture must be built to match the kinds of outcomes desired from the use of new data and analytics.
5. Perfection is the enemy of the good
Some cyber (re)insurers may not be convinced by the change in tool, thinking that cyber data is not quite up to scratch yet, especially when compared to property data. However, improvements in cyber data are continuously being made and the most successful (re)insurers are appropriately leveraging cyber analytics. Utilizing data only when it’s considered perfect will prevent (re)insurers from getting ahead.
It’s important to take small steps to keep up with those organizations and ensure cyber teams are able to reach better outcomes. Implementing this change under the right circumstances will make this even smoother.
Maximize value out of cyber analytics
Transformation is key for organizations who want to effectively embed cyber analytics into their operation. In order to make the transition as smooth as possible, (re)insurers must consider the business they partner with.
What to look for in a partner
A cyber analytics partner should not only understand the cyber insurance value chain, but also the complexity of the cyber threat landscape so that they can properly provide analytics that are relevant to (re)insurers. A cyber analytics partner should also provide:
- Transparency in processes and framework used
- Accurate and reliable data
- Easily accessible experts
These experts should range across multiple domains, such as cyber analytics, cybersecurity, underwriting, operational transformation and change management. The right partner will provide the appropriate resources needed, making it easier to leverage the analytics across the company and help reach overarching business goals.
The analytics partner used should also have the experience needed to see across different domains and parts of the organization in order to identify gaps in the analytics rollout, and plug in the necessary operations where they see gaps. Analytics are only valuable if people in the business are using them, so rolling out this operation effectively is essential to delivering on the necessary outcomes based on company objectives and achieving long-term profitability.
