Digital transformation is really Data Transformation. The bedrock foundation of any company’s digital future is being built on the data that resides in its core systems. It’s that client and policy data that will inform increasingly personalized customer experiences, drive product design, enhance decision making and accelerate process innovation.
But getting data out of the ‘bedrock’ can be harder than drilling for oil. Legacy systems often aren’t API (Application Programming Interface) capable and don’t connect easily to other systems. Data has to be moved in batch processes making real-time access very difficult for engagement platforms or service portals.
Digital transformation journeys are going to held up by data roadblocks if insurers are unable to free their siloed legacy data for use in all coming digital applications.
There are several approaches to freeing up that data for use in everything from new PAS to digital sales, service and engagement solutions to AI.
3 techniques for leveraging legacy data for digital transformation #
Making the data housed on your legacy PAS available and usable for all the applications you’re building as part of your overall digital transformation can be done in a variety of different ways.
Three very effective approaches are:
- Data migration
- Data integration
- Data modernization
Let's take a look at each of the three in more detail:
Approach #1: Data migration #
Data migration describes the process of moving data from a legacy source system to a new Policy Administration System (PAS) with the goal of consolidating your data, retiring aging core systems and the business processes/processing associated with these systems, reducing costs and resource load, while increasing your digital capabilities.
Benefits of data migration #
Data consolidation and elimination of data silos
Data migration consolidates information from multiple legacy systems into a single, unified platform. This consolidation eliminates data silos, making it easier to manage the business, as well as access and manage the data. With all information centralized, insurance companies can gain a comprehensive view of their operations and customers, leading to more informed decision-making and streamlined processes.
Retirement of aging legacy systems and reduced IT burden
Legacy systems often require significant maintenance and can be difficult to integrate with new technologies. By migrating data to modern platforms, insurance companies can retire these outdated systems, reducing the IT burden. This shift allows IT departments to focus on more strategic initiatives rather than spending time and resources on maintaining obsolete infrastructure.
Cost savings through streamlined infrastructure and data management
Migrating to a modern data platform can result in substantial cost savings. Modern systems typically offer more efficient data management capabilities, reducing the need for expensive legacy hardware and software maintenance. Additionally, the streamlined infrastructure can lead to lower operational costs and improved resource allocation.
Considerations for data migration projects
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Data mapping and transformation to ensure compatibility
One of the primary challenges in data migration is ensuring that data from legacy systems is compatible with the new platform. This process involves data mapping and transformation, which can be complex and time-consuming. Accurate mapping is crucial to ensure that all data fields align correctly, and that the integrity of the data is maintained throughout the migration. Working with a data migration vendor who has experience on hundreds of projects on every type of PAS will greatly increase the speed and accuracy of the migration.
Downtime and potential disruption during migration
Data migration projects can cause downtime and disrupt business operations if not managed properly. It is essential to plan the migration carefully, considering the timing and scope of the project to minimize disruptions. Developing a detailed migration plan and conducting thorough testing before the actual migration can help mitigate these risks. The right vendor can offer strategic insights, highlight potential risks in the project plan and suggest mitigations to ensure success.
Data migration use case: streamlining operations and reducing costs #
Data mapping and transformation to ensure compatibility
One of the primary challenges in data migration is ensuring that data from legacy systems is compatible with the new platform. This process involves data mapping and transformation, which can be complex and time-consuming. Accurate mapping is crucial to ensure that all data fields align correctly, and that the integrity of the data is maintained throughout the migration. Working with a data migration vendor who has experience on hundreds of projects on every type of PAS will greatly increase the speed and accuracy of the migration.
Downtime and potential disruption during migration
Data migration projects can cause downtime and disrupt business operations if not managed properly. It is essential to plan the migration carefully, considering the timing and scope of the project to minimize disruptions. Developing a detailed migration plan and conducting thorough testing before the actual migration can help mitigate these risks. The right vendor can offer strategic insights, highlight potential risks in the project plan and suggest mitigations to ensure success.
Data migration use case: streamlining operations and reducing costs #
The problem
An insurance company uses multiple PAS, some of which are decades old, and struggles to handle the demands of modern insurance products and digital services. How can they make data available in real time to digital front-end solutions and create a foundation for faster product innovation, while reducing manual steps and lowering costs?
The solution
The insurer decided to migrate their entire data portfolio, including policyholder information, claims history, and financial data, to a new, cloud-based PAS. Since all data will reside in a single system, they eliminate the need to access and manage data from multiple sources. And they will benefit from the advanced functionalities for underwriting, claims processing, and policy management in the new PAS—all at a reduced cost once the legacy system is retired.
The modern approach to migration
- Use a data migration toolset that puts the business experts in the driver’s seat
- Evolve the data migration solution using an agile-like methodology
- Run the conversion on the secure platforms where the source and target systems live.
- Create target data in the most usable format
- Find out more about how a state-of-the-art data migration methodology works.
Approach #2: Data integration #
In a data Integration process, data is not migrated off the legacy system, but modern tools and methods can be employed to access data and make it available to modern digital point-of-sale solutions. In a two-way process, data that is altered in the digital solution can be returned to its source system for updating the system of record.
Benefits of data integration #
Personalized customer experiences and product recommendations
Data integration allows insurance companies to access and utilize legacy data in real-time, enabling them to offer personalized customer experiences. With integrated data, companies can analyze customer behavior, preferences, and history to provide tailored product recommendations and services. This level of personalization enhances customer satisfaction and loyalty.
Improved sales efficiency with real-time policy and customer data
Sales teams benefit significantly from real-time access to policy and customer data. Data integration ensures that agents have the most up-to-date information at their fingertips, allowing them to monitor the status of pending applications, respond quickly to carrier or customer inquiries, generate accurate quotes, and close cases faster. This improved efficiency can lead to increased sales and revenue.
Enhanced customer service through access to comprehensive customer history
Customer service representatives can provide better support when they have access to a comprehensive view of a customer's history. Data integration consolidates information from various legacy systems, giving service teams a complete picture of each customer's interactions, policies, and claims. This comprehensive access enables representatives to resolve issues more effectively and provide higher levels of service. Data integration can also enable insurers to provide true omni-channel service.
The importance of APIs in data integration #
APIs are ubiquitous in modern ecosystems—these sets of rules and protocols that allow different software applications to communicate with each other make integrations between disparate systems possible—even including older legacy platforms.
API integration offers numerous benefits, including real-time data access, improved operational efficiency, and enhanced customer experiences. By enabling seamless data exchange, APIs reduce the need for manual data entry and minimize errors. This integration allows insurers to automate processes, streamline workflows, and respond quickly to market changes.
Data integration use case: enhanced sales and service #
The problem
The insurer has customer information housed on multiple legacy PAS for different products. They want to implement a client and advisor engagement portal that will enable them to do self-service on a web portal. They aren’t ready to migrate their data and consolidate their PAS. How can a carrier present client policy data for each product owned by a client from all the various PAS’ or data stores where it resides?
Solution
The insurer chose not to migrate their data, but instead integrate the data from their legacy PAS with the self-service portal using APIs. This integration involved using a vendor’s integration solution for sync processing between web and legacy source systems. The API connectors and microservice integration opened doors that were previously closed—exposing data that had previously been siloed, without the need for a large data migration.
Approach #3: Data modernization for improved analytics and AI #
Data modernization describes the process of cleansing and optimizing legacy data for use in digital solutions. It is a way to adapt your data to suit a specific business need/context.
How data modernization prepares legacy data for advanced analytics and AI applications #
Data modernization techniques include:
- Data cleansing to address inconsistencies, missing values, and inaccuracies.
- Data standardization to ensure consistent formatting and definitions across datasets.
- Data enrichment with additional information to improve data analysis capabilities.
Benefits of data modernization in digital transformation #
Data modernization makes more data, more usable, by more applications. Performing cleansing, enriching and standardization procedures on legacy data will improve data quality resulting in more reliable analytics and AI results. The increased access to more usable and more accurate data increases the effectiveness of analytics and AI, delivering an enhanced ability to identify trends, patterns, and customer insights. These types of benefits are especially useful in developing data-driven solutions for risk assessment, fraud detection, and underwriting.
In general, data that has been cleansed, formatted and structured features:
- Improved data quality: Clean and consistent data leads to more reliable results from analytics and AI models.
- Enhanced insights: Standardized data allows for easier identification of patterns, trends, and customer behaviors.
- Data-driven decisions: The company can develop data-driven solutions for underwriting, fraud detection, and risk mitigation strategies.
Data modernization use cases #
Problem
An insurance company wants to leverage advanced analytics and AI to improve risk assessment and fraud detection. However, their legacy data contains inconsistencies, missing values, and outdated formats. They need to implement data modernization techniques to cleanse, standardize, and enrich their data.
Solution
In this case the insurer worked with a insurance data service partner who was able to cleanse, consolidate and aggregate disparate data. They used work around processes to augment missing data and also translated old code values to "real business language." At the end of the data modernization process the insurer had enriched, properly structured data for use in their AI fraud detection initiative.
Conclusion #
Strategic data management is a critical capability for successful digital transformation. Depending on the business needs data migration, data integration and/or data migration approaches can free legacy data from core system silos, making it available for modern applications. Using these techniques can help insurers achieve true data readiness for automation, analytics and AI solutions that will enable them to leverage the full potential of all their data.
How to start preparing your data for digital transformation #
Assess your data and create a plan
Before choosing an approach, insurers should complete a comprehensive data assessment that examines their business needs and what data they have, where it is, how it is structured, its quality and what needs to be done with it.
From that assessment a detailed plan can be developed that includes timelines, budget, contingencies and responsibilities. A good insurance data services company can conduct an independent assessment and create a full plan in 2-3 weeks.
