Banner image

Overcoming the Challenges of Data Modernization in Insurance

Get Analyst Report

World economies are rapidly moving toward connected ecosystems organized around 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 data to drive intelligent decision making on-the- fly. In an increasingly digital world, insurers can no longer depend on outdated legacy systems, processes, and approaches to drive meaningful value to their business. The winners, then, will be the insurers who can move past legacy challenges and successfully adopt the data platforms necessary to stay competitive in today’s rapidly evolving digital landscape.

However, truly achieving data modernization in insurance is arduous and costly, requiring significant investment across the enterprise and posing a number of challenges. For that reason, it is critical to not only acknowledge, but also prepare for the various obstacles that may arise during a data modernization journey.

Legacy Data Challenges: What Today’s Insurers Need to Overcome

To fully understand the challenges posed by data modernization, it’s helpful to recognize the current state of data and data modernization within the insurance industry. To be brief, the insurance industry has historically used data for tasks such as determining pricing strategies. It’s only in recent years that insurers have started to consider data an asset rather than a cost of doing business. “That’s the paradigm shift driving today’s wave of data transformations,” explained Jeff Goldberg, Novarica’s SVP of Research and Consulting during a webinar examining data modernization trends in insurance.

Insurers, in turn, are starting to embrace data as key to achieving competitive success in the digital era. However, their core systems, processes, and approaches—across not only technology, but also business and culture—weren’t built to accommodate the vast quantities of data now available across dozens of new channels. In the insurance industry, some of the most common obstacles—and a few of their countermeasures—are as follows:

Limited Company-Wide Data Integration.
In insurance, this typically occurs for two reasons: 1) Many insurers have multiple core systems, whether due to acquisitions over time or deploying different systems for different business lines and 2) Most insurers haven’t developed internal agreement on data definitions, which leads to inconsistencies across the enterprise that prevent enterprise-wide usage. If insurers wish to fully tap into the power of data, it is critical to develop frameworks and standards for resolving these inconsistencies and reconciling their different core systems.

Taking a Policy View Rather Than a Customer View.
As a natural consequence of the traditional business model of selling through agents, many insurers have fallen into the habit of delegating policyholder relationships to agents. This has prevented insurers from gaining a true 360-degree view of customers—a must in our current marketplace, where customers reward the providers that best deliver the ultra-personalized, seamless experiences they expect. Having a customer-centric approach that captures more information via more touchpoints will allow insurers to maximize the quantity and quality of data-driven insights they have to personalize the products and services they offer their customers.

Low Data Quality and Completeness.
A long-standing struggle for insurers, low data quality and completeness arises from historical problems such as changing data collection needs, poor documentation, and improper data entry. As intelligent, data-driven decision making becomes the norm, insurers must find ways to leverage the full value of their data to derive meaningful business value from analytics. Resolving this may include data testing and validation as a bottom-up component, establishing a data testing environment, and automating various processes. Insurers should also develop a data governance framework, which will be necessary for effective decision-making and strategy execution.

Insufficient Predictive Modeling and Analytics Support.
A forward-looking modernization strategy requires realizing the promise of higher-order predictive analytics, as predictive analytics offer increased complexity and sophistication of modeling techniques, as well as a far greater breadth and depth in ability to incorporate internal and external data sources. Heading in, be prepared to answer questions such as: 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 do we integrate it?

Lack of alignment between People, Process, and Technology.
Many data modernization hurdles emerge from an overemphasis on only implementing the right technologies for identifying, extracting and storing data. Especially in enterprise-wide modernization initiatives, identifying and remediating people and process gaps are equally important. It is paramount that investments, resources, processes, and technologies across the enterprise are aligned with the insurer’s data strategy. Alignment up and across all levels of the organization are crucial to ensuring the success of data modernization. Insurers can achieve this by establishing the role of Chief Data Officer (CDO), or similar title, who reports directly to the CEO or COO.

Getting Started: Embracing the Inevitability of Data

No matter what an insurer’s business and IT strategy entails, the future is all about data. The sooner insurers accept this new reality and act to overcome the challenges of data modernization , the faster they’ll begin to benefit from it, as almost every innovative technology emerging today has data at its core. Through the Internet of Things (IoT), insurers can capture billions of new data points, whether via drones, wearables or smart property sensors. Big Data is being leveraged to store, manipulate, and visualize data in previously unfounded ways. With AI and Machine Learning, insurers can gain insights from massive quantities of data that will soon surpass any human’s individual 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. Nonetheless, no matter the specifics of any particular data modernization journey, there’s at least one thing they share in common: they all begin by embracing the inevitability of data.

For more on Data Modernization, read 5 Steps to a Winning Data Strategy to understand the key considerations for establishing a comprehensive and flexible technology approach for gaining fast and actionable insights.

You can also view the live discussion on this topic in the webinar: A P/C Insurance Data Modernization Journey: Learn from Pekin Insurance’s Success.