Data integration as a critical competitive capability in insurance

Carriers have always been data-driven, using data to assess risk and set premiums. In recent years, however, data has become even more important as insurers strive to improve customer experience and operational efficiency.

[.emph]The average insurance organization generates 2.5PB of data per year. This data is growing at a rate of 30% per year.[.emph]

Insurers are struggling to keep pace with the rapid pace of data growth. Only half of this data is usable. The remaining data is either duplicative, irrelevant or of poor quality.

Data silos are a major obstacle to data integration

Despite the clear benefits of data integration, many insurers still struggle with data silos. Poor-quality data costs the US economy around $3.1 trillion annually – and one of the biggest causes of poor-quality data in insurance organizations is data silos.

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[.figure]$3.1 trillion annually[.figure]

[.emph]cost of poor-quality data in the United States[.emph]

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Data silos occur when data is scattered across the organization in different formats and platforms. This makes it difficult to get a complete picture of the customer or the business.

To overcome these challenges, insurers need to invest in data integration solutions that can help break down data silos and ensure the free flow of data within the organization. By doing so, insurers can not only improve customer experience and operational efficiency, but also create a more data-driven culture within the organization – one that is better able to compete in the digital age.

Data silos negatively affect business outcomes

The lack of a single source of truth with complete data visibility can lead to data duplication, inaccuracies, and inconsistencies. This, in turn, leads to poor decision-making, sub-optimal customer experiences, and increased operational costs.

Data silos affect organizations in multiple ways:

• Make it difficult to get a complete view of the customer. This can lead to poor decision-making and sub-optimal customer experiences.
• Increase operational costs by requiring manual data entry and data cleansing.
• Impede employee productivity by making it difficult to find the data they need.
• Make it difficult to comply with data privacy and security regulations.

How to overcome data silos in 8 steps

Data integration is critical to overcoming data silos and ensuring the free flow of data within the organization.

Data integration is the process of combining data from multiple sources into a single view. This allows businesses to get a complete picture of their customers, operations, and performance.

By integrating data from disparate sources, insurers can get a complete view of the customer and make more informed decisions. Data integration also enables insurers to automate repetitive tasks and improve employee productivity.

There are a few key steps that insurers can take to overcome data silos and achieve data integration:

1. Define the business need for data integration: Without a clear business need, data integration can quickly become a costly and time-consuming exercise with little return on investment.
2. Assess data quality: In order to overcome data silos, insurers need to have clean, accurate, and complete data. This data quality assessment will help identify any gaps in data quality so they can be addressed.
3. Identify data sources: Once the business need for data integration has been defined and data quality has been assessed, insurers need to identify all of the data sources that will be required.
4. Streamline data collection:  This includes internal data sources, such as policy administration systems, and external data sources, such as credit bureaus and social media data. This also includes customer data collection methods, such as data entry forms, surveys, and customer data captured through web analytics. Data can be collected from various sources:

• Data collected directly from customers: data entry methods include data entry forms, PDF forms, or in a call center.
• Data captured through web analytics: data can be collected through web and mobile analytics tools.
• Social media data: data can be collected from social media platforms, such as Twitter or Facebook.
• Data from partners and third-party data providers: data sources include credit bureaus, social media data, and other data sources.

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5. Eliminate manual customer data intake methods: One of the biggest causes of data silos is manual data collection and entry. Transforming the way insurers collect data from customers will be critical to achieving data integration.
6. Monitor data quality: Data quality needs to be monitored on an ongoing basis to ensure that data silos do not reform. This can be done through data profiling and data cleansing techniques.
7. Implement data governance: Data governance is critical to ensuring data quality and preventing data silos. Without data governance in place, data can quickly become fragmented and difficult to manage. By establishing processes and policies for data management, insurers can ensure that data is accurate, complete, and consistent across the organization.
8. Implement data integration solutions: There are several data integration solutions available on the market, ranging from simple data import/export tools to sophisticated data management platforms. The right solution will depend on the specific needs of the organization. However, to work well, all those solutions rely on high quality digital data, which means that data collection methods will play a major role in data integration success.

Digital data is essential for data integration

Data integration is a critical capability for insurers looking to improve customer experience, operational efficiency, and employee productivity. To achieve data integration, insurers need to have high-quality digital data.