As part of our series on Integrated, Digital Risk Modeling (IDRM) we now explore Rapid Customization.
When you buy a business suit, you start with the same product as the next person and then make any necessary alterations. The length of the sleeves, the hem, perhaps the waistline. A bit of tailoring to make it bespoke and ensure it’s a perfect fit for you. You don’t wear it home off the rack and you don’t design a new suit from scratch. It’s 90% ready, and you and the tailor do the rest to make it yours.
That’s the OptimEyes’ approach to enterprise risk modeling. It starts with a template and default settings that reflect best practices, experience, and common preferences. Then it adjusts to reflect your industry, unique set of risks, the weight you give to specific vulnerabilities, and your strategic objectives, business priorities, and risk appetite. This customization enriches the platform, enabling the generation of risk quantification and exposure analytics that are accurate and specific for a particular organization.
We make these adjustments quickly, so we call it Rapid Customization. Like the suit, it’s functional off the shelf, but its optimal value is derived through customization.
For example, one organization’s biggest threat may be a cyber attack by state-sponsored criminals, while another’s is geopolitical pressure on their supply chain that could impact their business continuity and revenue stream. These two entities need to weight, monitor, measure, benchmark, and quantify their divergent risks differently – so they can formulate the right protective measures and most effective responses.
On the other hand, standard settings may be appropriate for most of the platform’s capabilities. These might include dashboard reporting and the default weightings applied to data architecture, hygiene, operations, and processes. Whatever an organization needs to make the platform their own, OptimEyes will deliver – just like a tailor perfecting a bespoke suit.
This is part of a series on the benefits of Integrated Risk Modeling.