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How to accelerate insurance data modernization with process intelligence

Data modernization is a term we hear a lot lately, and for good reason. McKinsey estimates that 40% of enterprise IT budgets today get spent on “tech debt” - that is simply fixing and maintaining existing data infrastructure. Insurers have their share of data and technology debt.

Many insurance companies run on decades old technology that needs specialized care to keep running in today’s digital-first world. For them, the cost of maintaining existing data systems inhibits a truly transformative shift in digitalization.

What exactly is data modernization?

So what is data modernization? It’s simply the process of updating and transforming an organization's data capabilities to match the increasing demands of consumers in the digital age. Data modernization involves upgrading databases, integrating siloed data sources, and implementing advanced analytics tools in order to provide a better experience or value to the customer. Keep in mind the focus on customer, and not for modernizing data for its own sake.

insurance data modernization

How does data modernization impact insurance firms?

In the context of the insurance industry, data modernization is not just a technological upgrade – it's a strategic necessity. Insurance is already a sector where data is the lifeblood of competitive advantage. Gartner recently identified data modernization as the top priority for investment of global insurance CIOs in 2023.

Data modernization is not only an IT challenge - it impacts the core of insurance workflows and services. The modern insurance customer demands a seamless and personalized experience. They expect insurers to understand their unique needs and provide quick and tailored solutions. With data modernization, insurance companies can enhance customer experience through personalization and by improving speedy service.

The case for data modernization in insurance

Every decision made, from underwriting policies to settling claims, relies on accurate and timely data. However, the traditional data infrastructure of many insurance companies is unable to keep pace with the rapidly evolving digital landscape. According to Okta, the average large business now utilizes 187 different applications. Most insurance companies use at least that amount of different apps, web portals or productivity tools. This is where data modernization comes into play.

Data modernization allows insurers to break down silos in data and workflows to speed up the time to value. By embracing data modernization, insurers can deliver superior customer experiences, streamline operations, manage risks effectively, ensure regulatory compliance, and foster innovation, and win market share in a competitive marketplace.

Data modernization is easy to prioritize but difficult to execute. In today’s digitalized workplace, insurance specialists rely on a wide variety of business applications, productivity tools and enterprise resource systems to get their work done. According to recent research by Workfellow, half of white collar work in enterprise businesses is spent in repetitive tasks. In transactional functions, such as underwriting or claims processing, the portion of repetitive, digitalized work can even be higher. 

How to overcome challenges to insurance data modernization

The key roadblock for data modernization in insurance is that knowledge-intensive work already happens across a vast number of business applications and IT systems. Shifting to a cloud-based enterprise resource planning system doesn’t have the same benefit in insurance as it would in production and supply chain focused industries. At Workfellow, we’ve found working with our insurance customers that 90% of work happens outside of the core system of record, like the ERP or CRM.  

So, how can data modernization be accelerated across the fragmented insurance data landscape?

Process intelligence is a natural first step in any insurance data modernization journey. In simple terms, process intelligence is the technology-driven analysis of process data to deliver insights for process improvement. Today, you can use a multitude of process intelligence tools, or build your own solution using data science and your preferred business intelligence platform.

You can use process intelligence like an x-ray into the digital health of your organization, showing in detail the interaction of tasks, processes and workflows across your different IT systems. It can be used to diagnose the “as-is” state of your digital processes in order to streamline, remove or automate your most repetitive and time-consuming activities. 

Instead of relying on opinions to form your modernization roadmap, process intelligence gives you data-driven insights that can be readily benchmarked as you progress in continuous improvement and digital transformation. Process intelligence can run on the workstations of core teams or even external partners without impacting the pace and efficiency of work.

Process intelligence vs. process mining - which is better for insurance?

For insurers, process intelligence gives the benefits of process mining, but without the typical data hassle of integrating separately into each enterprise IT system. As most work happens across standardized teams and operating procedures, modern process intelligence solutions can be configured to measure productivity while maintaining anonymity and data privacy of the employees.

In summary, data modernization is not a luxury, but a strategic imperative for insurance companies in today's digital age. While the complex nature of digitalized insurance processes presents challenges to data modernization, process intelligence can be leveraged to accelerate modernization initiatives based on actual work and system data.

Written by

Lari Numminen

Chief Marketing Fellow

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