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Why data integration and analytics is important to the insurance industry?

Published on 18 January 19

With the aim of building an enterprise data platform, data integration and the assimilation of traditional insurance transaction data is the first step. Along with fulfilling regulatory requirements, this acts as the initial point of your decisions becoming data-driven throughout the organization.

However, this alone does not solve the entire issue at hand.

Today, integration of external sources of data like survey data, demographics, public records, sentiment data etc. with the internal dataset must happen constantly. In order to obtain meaningful insights into pricing, mergers and acquisitions, risk and customer relationships integration is key. This will in turn scale up your organization to be data-driven.

Challenges Faced

When we talk about data integration in the insurance industry, it comes with its own set of challenges. These data challenges pose as an obstacle to digital transformation. A few data hurdles in the insurance industry are as below:

  • When Data is in Silo’s
  • Data stores being on cloud/on premise
  • Data definitions are inconsistent
  • Data ownership is not clear
  • Regulation
  • When change management is complex
  • When organizational structures are complex
  • Data governance being incomplete
  • Challenges with respect to Data latency
  • Legacy systems
  • Data standards are not maintained
  • Complexity of data
  • Issues in data culture
  • Business models being complex
  • Data security requirements
  • Data sharing is lacking
  • Introduction of new technology
  • Data value understanding is lacking
  • Business / IT integration
  • Data being of mixed quality

What are the major aspects you need to keep in mind while devising a data integration strategy?

  • Process modeling techniques should be understood and applied to data integration – process flow diagrams to be depicted visually, inter dependent process flows and data flow diagrams.
  • Before planning on addressing data lineage, address process lineage first.
  • Source systems should be understood perfectly. This should also include granularity of data.
  • From an integration perspective, source file formats should be standardized.
  • The sample source data should be analyzed and data profiling should be undertaken.
  • Data quality should be addressed with the right stakeholders.
  • A strategy should be in place to work on maser and reference data management and metadata management.
  • A data governance council and data stewardship program should be in place.

After the completion of data integration, data analytics is the next step. A solid, well-defined big data analytics roadmap cum strategy must be in place as it is the very crucial for the success of the organization.

Flexibility, scalability and agility are necessary components in the strategy for the organization to be data matured and for the monetization of data assets.

A few Data and Analytics mediations to consider are as follows:

  • With the aim of improving targeted acquisition, response and propensity models to be in place.
  • Analyze consumer behavior, sentiment and text, social media to further understand customer needs.
  • Streamline regulatory requirements and develop trigger warning signals by having regulatory reporting and data management in place.
  • Improve operational costs with channel usage and migration analysis.
  • Understand portfolio risks and investments using financial consolidation and reporting.


The dependence upon data in the insurance industry is extremely high. The need for data integration and analytics is evident and insurance firms are making use of them to drive their digital transformation initiatives. There are challenges that need to be overcome and strategies that need to be set. Having the ideal strategy in place will help insurance firms big time.

This blog is listed under Data & Information Management Community

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