Why data migration is critical to digital transformation #
Life insurance carriers are some of the most data rich environments on the planet. They house troves of data, made up of millions of policies and client documents accumulated over decades. Advances in areas such as machine learning, analytics and AI are shining a new light on those data caches. New tech demands that companies approach data differently. The new mindset will require the industry to shift its view. Data NEEDS TO BECOME INFORMATION.
This data transformation (which may not previously been anticipated) will enable insurers to turn their existing legacy data into a strategic asset that then is leveraged to provide new insights into the business, streamline processes and deliver the real-time, hyper-personalized digital experiences advisors and customers expect.
Unfortunately, for most companies, that data transformation has not yet been undertaken.
For many insurers, data can’t be easily harnessed for these modern use cases because it remains trapped in legacy silos (even if it is extracted and aggregated into warehouses, lakes, etc...). It is not available for real-time access by event driven/on-demand architectures that connect customers to high quality digital experiences. And legacy data may not be easily accessible/usable because of changes to data structure or formatting over time. All of this makes it very difficult for companies to build true 360-degree views of their clients’ policies, activities and needs.
How to solve data migration challenges and reduce data risk #
In order to capitalize on the wealth of data on hand, as well as eliminate the cost of running multiple core systems and achieve the expected ROI on digital transformation projects, carriers know that PAS modernization is necessary.
But true PAS replacement requires data migrations that aggregate, transform (cleansing, normalization, standardization) and integrate data on the target PAS (and other places in their data architecture) ready for real-time use in sales and service portals, BI and analytics apps, AI solutions and more. And not many insurers have the in-house experience, skills and tools to effectively perform such a migration in a low-risk manner.
So, how do insurers ensure that the data migration that will enable them to take full advantage of their modern PAS is handled effectively? The first step is to engage a partner in conducting a comprehensive data migration study that will assess the state of the carrier’s data and includes a detailed plan for the migration.
What are the elements of a good data migration plan? #
In order to ensure the success of complex data migrations a robust data migration plan should:
- Assess the current state of the data in the company’s legacy systems.
- Tailor a business appropriate approach for migrating data from legacy to target systems or data stores—from planning to execution to testing.
- Identify potential risks and provide solid mitigation approaches.
- Provide clarity on the data outcomes that will help propel the data strategy forward.
- Define reliable budgets and timelines.
- Clearly define roles and responsibilities within the project.
Typically, data migration studies can be completed in 2-4 weeks. The final report gives the carrier everything they need to know to successfully approach the transformation and migrate their legacy data.
Data migration project risks and solutions #
A data migration study evaluates and provides solutions for a number of situations that can put a data migration project at risk. These challenges include:
Data migration challenge: Lack of a comprehensive data migration plan #
One of the most fundamental oversights insurers make during modernization journeys is failing to consider their strategic end state policy administration environment. The cost and business implications of dealing with their legacy systems and data once the new PAS is launched. Too often, carriers don’t consider or include migration initiatives in their program of activities. They assume they can “deal with migration from the legacy system later,” treating it as a secondary consideration when measured against the “value-grabbing” new capabilities offered by today’s modern architectures and systems. There is an undercurrent of thought that portrays migration as something that can be done in-house or with their new vendor when they get around to it.
Unfortunately, in many cases, this lack of planning means carriers have no defined strategy for consolidating the cost out of their operational/administrative backends and have not ear-marked resources or budgets to complete this important strategic milestone which can actually flip the ROI on their new tech investment, if put in its proper view. Without adequate examination of the state of their data and the process required to move it to the new PAS, carriers can end up discovering, too late, that existing ETL tools/methods are an expensive and suboptimal solution to completing the task of a complex conversion. These “once in a career or generation” projects are then pushed to an in-house staff that lack experience in identifying and working through the unique challenges and potential risks of these efforts. They are left defining the best migration process they can and thus unknowingly introduce a significant amount of risk to their organizations.
Solution: complete an assessment of the overall end state policy administration environment as part your transformation initiative
A robust, independent data migration assessment outlines a comprehensive plan for the complete migration, including costs, steps, timing, responsibilities, possible risks, pitfalls and contingencies. This kind of assessment can be handled by a specialized insurance data company that has vast experience migrating data from legacy systems to modern systems and/or data architectures. Partnering with an expert firm ensures that you get the benefit of their experience.
Universal Conversion Technologies (UCT) has migrated data from and to most commercial systems in North America with more than 99% accuracy in over 300 conversion projects. They bring all of that experience, expertise, proven methodology and propriety tools to each data migration study, which results in the creation of specific plan for mitigating business risk during the migration project or program.
Data migration challenge: Clear Understanding and assessment of impacts between multiple PAS modernization vendors can result in project delays #
It’s not unusual for policy administration system (PAS) modernization projects to have more than one vendor working on different aspects of the initiative. A successful migration needs to take into account the activities and requirements of each vendor as they install, integrate, configure and test the new system. To synchronize development and delivery, the plan should detail the major dependencies of each workstream of activity and they are linked with the complexities and timing of each workflow.
Starting a migration too soon can lead to less efficient progress, whereas starting too late can create the risk of running out of time to iterate through complex legacy considerations for the migration (especially if the migrated business brings requirements to be met in the new system). If multiple vendors are involved in migrating, configuring the new system, and potentially building integration then greater potential challenges arise. In order to avoid delays, overruns or mistakes that can arise from poor coordination of different vendor efforts the plan needs to define:
- Dependencies between vendor activities
- Roles and accountabilities
- Communication framework and process
Solution: UCT’s data migration framework enhances communication among all modernization parties--ensuring smooth collaboration, removing roadblocks and reducing risk. The study includes the development of a strategy to manage dependencies between vendor activities, ensuring a seamless data migration.
Data migration challenge: Too many unknowns when it comes to source data #
Part of the problem many companies face when it comes to converting their legacy data is the proliferation of data sources. Multiple legacy PAS, ERMs, CRMs, and Data Lakes make locating and accessing all historical data difficult.
And, even when data sources have been identified the quality of the information can be questionable. The data is in old or incompatible formats or structured in a way that is not usable by new, modern systems.
Solution: A comprehensive data modernization study will identify all data sources that need to be migrated. Data quality can be assessed through additional technical research in a data audit which identifies the quality of the data in each repository and details the steps required to transform it. The plan defines what data access needs to be provided to complete the migration.
Data migration challenge: Data validation and testing responsibilities need to be clearly defined #
Conversion project success is defined by the converted business being properly supported in the new system. The only way to ensure quality is through a comprehensive and complete validation and testing process.
The end results are significant. More than simply a nuance in terminology, validation of the data is the process of ensuring that the data was correctly converted from the source(s) and testing of that same migrated data ensures that the processing of migrated business produces the expected results when compared with/to the original source.
Testing with migrated data must prove that all necessary information has been extracted and transformed correctly and that the new system handles the migrated business accurately and completely, which ensures minimal impact on policyholders and agents. The testing responsibilities should be clearly assigned and timelines to perform these tests (along with the fixes associated with the QA cycles) must be protected to have a solid migration result.
The validation and testing processes should be conceived in an iterative fashion that flags issues/fixes early on and enables refinements in the process to be made quickly, ensuring faster migrations and greater accuracy.
In some cases, carriers do not adequately map out the process and clearly identify who is responsible for each step. If assumptions are made about testing responsibilities and this phase of the project is not properly planned it can lead to breakdowns in the schedule and increases the risk of data inaccuracy.
Solution: UCT’s data migration study creates a plan that details what, how and when and by whom testing will be conducted. This comprehensive testing plan for the target data ensures accuracy and data integrity. The plan specifies testing methodologies, timelines, and responsibilities. This ensures that the migrated data meets all requirements and functions as intended in the new system.
The conversion testing plan includes mapping out the tasks required to complete:
- Unit Testing—used to debug the conversion loading process.
- Functional Testing—can be used by the team for new business and conversion testing. Converted policies will be loaded into the functional test region after successful unit testing.
- Integrated Testing—for testing of downstream systems and interfaces with converted data.
Conclusion: A Strategic Approach to Data Migration #
For insurers modernizing their PAS, conducting a data migration study is a strategic step. It turns a data migration project from an afterthought to a part of a more valuable holistic plan for digital transformation by contributing to significant modernization benefits/results/ROI of new modern PAS.
The study identifies the specific requirements and challenges unique to the carrier’s operations. It outputs a detailed plan for the migration, including testing and implementation, acknowledges the inherent risks and provides targeted solutions. This approach ensures a migration process that is not only efficient but also aligns with the insurer's long-term digital transformation goals.
Investing some time working with data migration experts to ensure you have a comprehensive conversion plan will ensure you avoid the pitfalls that plague so many modernization projects. It will provide you with the best possible foundation for achieving true transformation and realizing the full potential of your data.
