How to get more value from Data Integration and Analytics for the Insurance business
Data integration and consolidation of traditional insurance transactional sources and systems built in-house is the first step towards building an enterprise Data platform for the “Single version of the truth”. This not only helps you fulfill regulatory requirements, it also becomes your starting point to enable “data-driven” decision making across the enterprise., But is this enough?
In today’s scenario, there is a constant need for integrating external sources of data like consumer demographics, telematics data, survey data, public records, social media, sentiment data and so on with the internal data set. This is required in order to provide more meaningful insights into customer relationship, risk, pricing, mergers and acquisitions to scale up your organization to be more data driven.
However, Data Integration comes with its own set of challenges because of a variety of reasons. To name a few, the industry itself is battling with several legacy systems across policy administration, claims and billing. Data in most of the insurance companies are in silos with each business unit looking into its own piece of data instead of viewing it holistically from an enterprise perspective. In most cases, this results in the quality of data getting compromised.
So what are the key aspects one has to keep in mind while carving out a Data Integration strategy ?
- Understand and apply process modelling technique to data integration – visual depiction of process flow diagrams,– Data flow diagram (DFD) and inter dependent process flows.
- Address Process Lineage first so that you can plan how to address Data Lineage next.
- Understand the source systems thoroughly, including the granularity of data across source systems.
- Standardize source file formats from an integration perspective.
- Analyze sample source data and undertake data profiling.
- Address Data Quality with the right stakeholders.
- Strategize to address Metadata Management, Master data and Reference Data Management.
- Have a Data Stewardship program and a Data Governance Council.
Once the integration is complete, the next step is Analytics. Having a well-defined Data Analytics Strategy and Roadmap is of crucial importance for the success of an enterprise.
The strategy also needs to be flexible, scalable and Agile so as to progress towards becoming a more data matured organization which in turn will help you monetize your data assets.
Possible Data and Analytics interventions to consider are as follows:
- Propensity and Response models to improve the targeted acquisition.
- Consumer behavior analysis, social media and sentiment analysis to understand consumer needs.
- Regulatory reporting and data management for streamlining the regulatory requirements and developing early warning signals.
- Channel usage and migration analysis to help improve operational costs.
- Financial consolidation and reporting for clear understanding of the portfolio risks and investments.
Mindtree has been partnering with several commercial insurance companies to define a Data Integration roadmap. Its portfolio of services comprises many prebuilt accelerators, framework and services across data integration and data management needs
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