Data Migration: Group Benefits Insurers’ Five Major Challenges
Migrating policy data is a significant challenge in the group benefits space. As group benefits insurance tends to feature greater flexibility and personalisation than other lines of business, it is more complex, harder to automate, and requires more transformation rules. Group benefits also tend to rely on a larger quantity of historical data. This makes data migration more costly and prone to failure.
According to Gartner, more than 50% of data initiatives in 2022 will exceed their budget and timeline – and potentially harm the business – because of flawed strategy and execution.1 So what challenges do group benefits insurers need to consider for successful data migrations? They need to be aware of and proactive about the five major challenges that can obstruct data migrations.
Focus on Data Quality
The fact that group benefits core systems are often built in-house makes data migrations more complex. These legacy systems often contain data that is essential to operations but is unstructured or the purpose of specific fields may have changed over time. This makes it more difficult to create transformation rules to migrate the data to the target system, which is likely to be less permissive and more demanding in terms of data integrity rules.
To address this issue, subject matter experts (SMEs) are needed to determine exactly what data is required for the new ecosystem and to cleanse it effectively. Making sure that sufficient time and resources are allocated to this task is vital for a smooth and successful migration. Gartner recommends devoting a minimum of 20% of overall effort to analysing data sources to build a clear understanding of the nuances and complexities of data structures,2 and how much it will cost to migrate them.
Control Project Scope
To maximise the chances of a successful data migration within budget, it is essential to minimise the scope of the project. While it is tempting to include as many of the target-related data sources as possible, the initial scope must be limited to data that adds genuine business value in the target system.
This is particularly true for group benefits organisations, which tend to deal with a much larger quantity of historical data than other lines of business. Scope creep during the project is also a risk that needs to be managed.
Find the Right Balance of Skills
Although data migration is considered a technical task, it needs more than just IT and technology expertise, and it can be challenging to combine these skills in a single team.
Expertise across the old system and the target system is required for a successful data migration project. Business domain SMEs are needed to understand the data’s significance in the old system and help with transformation logic and mapping in the new system. Data stewards are also needed to assess data quality.
Stakeholders from the business must be involved to make decisions about the data that needs to be migrated and what can be archived or decommissioned. Migrating data that the business doesn’t really need leads to unnecessary expense and failing to migrate data that is still required has an operational impact. Testing can help establish the extent of this impact.
Accommodate the Old and the New
Data migration not only requires the data to be converted and migrated from the current format to the target system format, but the functional rules must also change.
The pressure to go live with a new system that is designed to accommodate new areas of business can mean that not enough consideration is given to exceptions when the existing data is migrated. If this happens, functionality may be lost, which will require more change management, or unexpected time and money will be needed to fix the issue, potentially affecting the scope, timescale, and budget of the migration.
On the other hand, failing to transform the data and the functional rules to allow for new business can also have a long-lasting effect on the organisation’s growth.
Be Realistic About Multiple Data Sources
Group benefits organisations often have multiple legacy systems for different functions, such as policy administration and claims. Many insurers will also have grown through acquisition, and they may have more than one system with the same function.
Naturally, more data sources make extracting and transforming data for the target ecosystem more complicated, and it is important to be realistic about the complexity and cost of a migration under these circumstances.
For effective data migration, it is critical that insurers do not underestimate the importance of data quality and that decisions about which data should be migrated are justifiable in terms of business value. Along with the correct balance of skills and a strong grasp of the project scale, these principles will help ensure that data migration projects are on time, within budget, and successfully achieve their goals.
1 “Make Data Migration Boring: 10 Steps to Ensure On-Time, High-Quality Delivery,” Gartner, December 2019