This is the ninth blog in a series on insurance transformation by Majesco and PwC. Today’s insurance blog is a continuation from the 7/7/2022 featured podcast between Majesco’s Denise Garth and PwC’s Kanchan Sukheja and Sudhakar Swaminathan. We will continue to discuss how transformation is a continuous initiative for future growth and how it will ultimately lead you to become a next-gen digital leader.
Before undergoing any transformation, carriers should consider their enterprise data strategy: is the organization’s data ready to support a new distribution strategy? We’ve seen some common data challenges across carriers. Below, we discuss those challenges, the impact to the organization, why these challenges can be so difficult to resolve, and dimensions carriers can use to measure their data quality.
Common Data Challenges Across Carrier
Data accuracy, completeness, and timeliness
We’ve seen carriers who struggle with their data at a very basic level. Some carriers struggle with a population of incorrect information, missing information in fields from the source system, and data that is not available at the time of business need. These challenges are often indicative of a legacy source system issue. Carriers can be resistant to updating source systems; such an undertaking can require significant investment. However, oftentimes a source system transformation is a prerequisite to future successful downstream transformations (e.g., a DM transformation).
Inconsistent Data Definition and Use Across the Enterprise
We see carriers who use the same field for multiple data points across products or lines of business. This is a quick solution for data storage issues. However, inconsistent data definition and reuse of data fields can add complexity downstream where systems must rely on separate pieces of logic to interpret a single field. In short, this quick, short-term fix can create complicated, long-term issues.
Duplicate Records in Various Data Repositories
Some carriers fail to establish a single source of the truth. This can result in carriers requiring multiple sources to pull information, and conjoining disparate pieces of data together to get a clear picture. This challenge can be a result of failure to establish enterprise data quality and storage standards; in some cases urgent data needs drive ‘quick data fixes’ that are ultimately costly in the long term.
Challenges to Resolving Data Quality Issues
Data challenges are common across carriers. What makes them so difficult to remediate? The root cause is often either a cultural or system issue, or some combination of the two.
Organizational Culture Issues
Information culture dictates the information management strategy. Carriers fail to establish an enterprise data strategy, or a designated resource to lead the strategy, and as a result, may see resources make disparate data quality and storage decisions across the organization.
Inaccessible Enterprise Strategy
Carriers may have a data strategy, but it may be unclear or unshared across the organization. In short, a data strategy exists, but it’s not well known or understood.
Carriers’ strategies, and supporting systems, are becoming increasingly complex. Quick data fixes are tempting to alleviate short-term, immediate challenges, but often end up contributing to technical debt and existing data challenges.
Challenging Data Quality Issues
Carriers may struggle to identify what data quality issues they have, and gauge how widespread those issues are. Once understood, data quality issues may be difficult to resolve, and, or, require significant investment of time and resources to remediate.
Steps for Data Quality Improvement
Carriers may consider the following steps in working to improve the quality of their data.
Define the Data Quality Scope and Approach: Establish what needs to be addressed and how it will be addressed.
Define the Data Quality Organization: Determine who will undertake the data improvement effort.
Assess, Remediate, Test: Determine the extent of the issue, resolve the issue, and test the fix.
Train and Sustain: Train resources in the go forward approach, monitor data quality over time, continue to uphold data quality standards over time.