How Data Management & Data Modeling Can Benefit Insurance Processes

The sheer volume of data that insurance companies process and analyze on a daily basis is astonishing. And this data is what helps inform decisions from top to bottom, from premium pricing to new product development and everything in between. However, an insurer can only act on their most valuable asset — data — if it is stored, organized, and managed properly.

That is why so many insurers are investing large sums into their data management systems. According to SNS Telecom & IT, insurance companies have already invested $2.4 billion in big data technologies, and are expected increase to that investment to $3.6 billion by 2021. These technologies make it possible to collect, track, analyze, store, and access all the valuable data insurance companies need to work as efficiently and accurately as possible.

In fact, data management and accessibility is a primary driver of many automated functions in the industry, such as claims, rating, billing, and more. Ultimately, the way an organization manages and uses its data to derive meaningful business insights that are accurate and timely will be critical to its long-term success and growth.

Here are some tips about transitioning to a more robust data management system, and the benefits insurers can expect from cloud-based data modeling platforms.

Data Management and Data Modeling

Today, insurance companies can address challenges around their information infrastructure by investing in master data management systems that enhance their data integration and automation. Legacy systems with siloed data repositories just won’t cut it anymore — insurers need scalable and flexible platforms that allow them to turn complex, unstructured data from multiple sources into actionable, efficient processes.

This typically correlates to cloud-based data management platforms and insurance data models. Insurance data models (cloud-based) allow scalable technology to deliver on-demand services over the internet.

How Insurance Data Management Improves Customer 360

According to TechTarget, customer 360 is “the idea that companies can get a complete view of customers by aggregating data from the various touch points that a customer may use to contact a company to purchase products and receive service and support.” For digital insurance providers, this means that the omnichannel experience increases the number of customer touchpoints, thereby collecting more data to paint a more holistic customer picture.

The largest category of insurance data is customer data, and customer data is what informs nearly all facets of insurance operations. It helps improve policies, informs claims processes, and even helps insurers develop new products. It also is helping insurers provide more personalized service to their customers by building what is known as a 360-degree view.

So what exactly does all of this data mean for insurers? Using robust data management and data models, organizations can better collect and leverage customer data to gain a 360-degree view of their behavior. Insights gained from enhanced data management practices include:

• Complete customer profile
• Comprehensive billing details and premium info
• All claims reported, both paid and outstanding
• Customer profitability and lifetime value
• Channels used by the customer
• Suggestions for cross-sell and upsell opportunities

Benefits of Insurance Data Models for Data Management

Insurance data models are best utilized to deliver on-demand services to users from anywhere, at any time. Many cloud users report significant cost benefits, increased productivity and collaboration across teams, and increased speed to month-end activities and reporting.

Additional benefits include:

1. Allowing for the delivery of exceptional customer experiences: Personalization drives customer experiences. By centralizing all the necessary data points for understanding and segmenting customers, insurers can tailor workflows based on the needs or actions of the customer.
2. Getting accurate and timely data/insights: Data may show untapped markets or trending customer behaviors, and the faster insurers can get that info and make informed decisions, the more potential they’ll have to create differentiation, gain market share, and attract new buyers.
3. Building scalable solutions: SaaS solutions allow for an environment that can scale up performance or capacity to meet increasing demands. This ultimately will support a large influx of data or database queries that will accumulate as businesses grow over time.
4. Supporting automation: Data drives automation. Automation can be done using rules-based workflows or by employing predictive analytics or artificial intelligence, which can lead to streamlined processes and optimal and consistent results. With data-backed automation, insurers are more likely to select the right risks, not overpay on claims, and more.
5. Leveraging partner integrations to pull in even more data: Bringing in external data improves customer experiences, as data is automatically input, and quotes can be generated faster. Also, third party data sources are likely more reliable than customers, as individuals may be forgetful or try to hide information they perceive as negative.
6. Visualizing data modes and architecture: Visualization helps tell the story in a better way than simply showing numbers — with data visualization, insurers can see the data lineage (how the data flows from source to database to report) and have more trust in the data as a result.

How Strong Data Management Can Improve Processes and Accelerate Automation

Insurers are more actively seeking out data management systems for myriad reasons, such as:

1. To identify good data and bad data
2. To identify fraud
3. To reach faster settlements
4. To improve claims forecasting
5. To improve data mining techniques

But for most, it is all about acquiring data and measuring results to more accurately run analytical models. With these results incorporated into day-to-day workflows, insurers can see better outcomes and create a virtuous cycle of capturing data, making recommendations, and learning from the outcomes of those recommendations. Ultimately, as those recommendations get better, carriers can shift from reviewing recommendations before taking action to fully automating workflows, leading to better experiences and happier customers.