This is the ninth blog in a series on insurance transformation by Majesco and PwC. Today’s insurance blog is a continuation of the 7/7/2022 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 generation 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 between carriers. Below we discuss these challenges, the impact on the organization, why these challenges can be so difficult to solve, and dimensions that carriers can use to measure their data quality.
Common data challenges for the operator
Data accuracy, completeness and timeliness
We̵7;ve seen carriers struggle with their data at a very basic level. Some operators struggle with a population of incorrect information, missing information in the field from the source system, and data that is not available at the time of business need. These challenges are often indicative of an older source system problem. Carriers may be resistant to updating source systems; such a business may require significant investment. However, often a source system transformation is a prerequisite for future successful downstream transformations (eg a DM transformation).
Inconsistent data definition and usage across the enterprise
We see carriers using the same field for multiple data points across products or industries. This is a quick fix 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 solution can create complicated, long-term problems.
Duplicate records in different data repositories
Some carriers fail to establish a single source of truth. This can result in carriers requiring multiple sources to pull information and piecing together different pieces of data 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 “data quick fixes” that are ultimately costly in the long term.
Challenges in Solving Data Quality Issues
Data challenges are common between operators. What makes them so difficult to fix? The root cause is often either a cultural or systemic issue, or some combination of the two.
Organizational culture issues
The 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, resources may make different decisions about data quality and storage within the organization.
Inaccessible corporate strategy
Carriers may have a data strategy, but it may be unclear or unshared across the organization. In short, there is a data strategy, but it is not well known or understood.
Carriers’ strategies and support systems are becoming increasingly complex. Data quick fixes are tempting to alleviate short-term, immediate challenges, but often contribute to technical debt and existing data challenges.
Challenging data quality issues
Carriers can struggle to identify what data quality problems they have and assess how widespread those problems are. Once understood, data quality issues can be difficult to resolve, and or require significant investment of time and resources to address.
Steps for improving data quality
Carriers can consider the following steps in improving the quality of their data.
Define data quality scope and approach: Determine what needs to be fixed and how it will be fixed.
Define the data quality organization: Decide who will perform the data improvement work.
Assess, fix, test: Determine the extent of the problem, resolve the problem, and test the fix.
Train and maintain: Train resources moving forward, monitor data quality over time, continue to maintain data quality standards over time.