Overcoming Legacy Data Hurdles

World economies are rapidly moving toward organizing around connected ecosystems, offering products and services to customers via fully digital experiences based on real-time analytics. By now, we all know that competing successfully in this new era requires harnessing and using data to drive intelligent actions on-the-fly.  

Traditional P/C insurers can no longer use legacy systems, processes, and approaches. Winners in the insurance industry’s rapidly evolving landscape will be those who move past legacy challenges by adopting data platforms that help them stay competitive. 

In this series of three blogs, you’ll gain insights into practical steps insurers are already taking to modernize their data systems and approaches. First up is an overview of current affairs from an industry expert. Later, we’ll dive into the key technology and organizational considerations insurers need to address. Finally, we’ll present the real-world case of Pekin Insurance and the journey it’s taking to become a streamlined, data-driven company. 

Understanding What Insurers Need to Overcome 

Although the insurance industry has always used data for determining pricing strategies, it only recently started to consider data as an asset rather than a cost of doing business. “That’s the paradigm shift driving today’s wave of data transformations,” said Novarica’s Jeff GoldbergSVP of Research and Consulting, during our P/C data modernization webinar. 

This shift is generating enthusiasm in the insurance industry to use data assets to achieve competitive success. For most, this requires a modernization journey that starts with understanding the primary challenges that need to be addressed and resolved along the way. 

An insurer's data modernization hurdles figure infographic

Limited Company-Wide Data Integration, which typically occurs for two reasons: 1. Many insurers have multiple core systems, whether due to acquisitions over time or from deploying different systems for different business lines. 2. Most insurers lack internal agreement on data definitions, which leads to inconsistencies across departments and systems that prevent integrating data for enterprise-wide usage. 

Lack of Data Quality and Completeness, a long-standing struggle within the insurance industry. This arises from historical problems such as data collection needs that change over time, poor documentation, and improper data entry. To neutralize this challenge, enterprises need to keep in mind that it’s mostly impossible to completely fix historical quality and insufficiencies. Hence, insurers’ goal becomes understanding strengths and weaknesses related to data, which allows for adjusting decisions accordingly. 

Taking a Policy View Rather Than Customer View, which is a natural consequence of the way core systems are oriented, and the long-standing practice of delegating policyholder relationships to agents. This challenge has prevented insurers from gaining a true 360-degree view of customers that must now be built to compete with new market entrants, whether insurtechs or other non-traditional players like Amazon or Google. Insurers must overcome this challenge to become customer centric. 

What to Do: Establish and Execute a Robust Data Strategy 

Developing a strong data modernization strategy will assist insurance carriers in resolving the above-mentioned challenges. Successful data modernization strategies typically include three key elements: 

Predictive Modeling and Analytics Support. Today’s predictive analytics are distinct from BI and analytics. Predictive Analytics offer increased complexity and sophistication of modeling techniques as well as a far greater breadth and depth of capability to incorporate internal and external data sources. To realize the promise of higher-order predictive analytics requires devising a modernization strategy that addresses questions like: Where is our data and how do we extract it? How much internal historical data do we need? What types of external data does our strategy require and how will we integrate it? 

Aligning People, Process, and Technology. Data modernization is all about implementing the right technologies for identifying, extracting and storing data. However, identifying and remediating people and process gaps are equally important. This requires establishing the role of Chief Data Officer (CDO), or similar title, who reports directly to the CEO or COO. Effective CDOs interact closely with their peers such as Chief Underwriter, Chief Actuary, Chief Claims Officer, and CIO to ensure investments, resources, processes, and technologies across the enterprise are aligned with the insurer’s data strategy.  

Resolving Data Quality (Accuracy) & Completeness Gaps. Although some aspects of a successful data strategy are top-down, it’s critical to include data testing and validation as a bottom-up component. In addition to establishing a data testing environment and automating various processes, developing a data governance framework is also necessary for effective decision-making and strategy execution. 

How to Start: Embrace the Inevitability of Data 

No matter what an insurer’s business & IT strategy entails, the future is all about data. The sooner insurers embrace this concept, the faster they’ll begin to benefit from it, as almost every innovative technology emerging today has data at its core: 

  • IoT - represents the ability to capture billions of new data points, whether via drones, wearables or smart property sensors. 
  • Big Data - represents the ability to store, manipulate, and visualize data. It starts with technologies like Hadoop and MongoDB at the foundation and then layers on various modeling and visualization tools. 
  • AI/Machine Learning - represents the ability to gain insights from the data that has been gathered and stored, as the massive quantities of data required to stay competitive will soon go beyond human capability to analyze effectively.  

It’s unreasonable to expect an insurance enterprise to become fully transformed overnight. With data technologies and methodologies still emerging and evolving, data modernization initiatives should be viewed as a continuous, iterative journey rather than having a specific endpoint.  

Many insurers are taking their first steps by using a combination of their existing internal resources, hiring new talent and partnering with others who have developed substantial data expertise. 

To learn more about Data Modernization, see our next blog in the series 5 Steps to a Winning Data Strategy. It discusses the key considerations for establishing a comprehensive and flexible technology approach for gaining fast and actionable insights. 

You can also view the webinar here: A P/C Insurance Data Modernization Journey: Learn from Pekin Insurance's Success.  

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