BANGALORE, INDIA: Data is all pervasive it begins much earlier than the initial stages of client understanding and diligence, and extends far beyond helping revenue generation, encompassing cross and up-selling products or services. It also helps to understand the business risks and verify whether the regulatory compliance needs are met.

The insurance industry  depends on promises made on paper, which are eventually converted into supporting databases and document repositories. This article elaborates on the types of data, modes of data acquisition, data checks and usage, and the prevalent techniques for data management.

Data sources
Insurance industry's data can broadly be classified as employee-related, distribution-related, customer-related, product-related, operations- related and accounting-related. Of these categories, employee-related data is required purely for internal workforce operations management and the rest have a direct impact on the cost and revenue of the insurance company.

All data is collected and stored in databases, data warehouses and as documents or images.

1. Data acquisition results from new business management, internal operations (HR, accounting, distribution and product & policy management systems). These are made available in unique respective data structures, in an integrated way. One step up, they can be consolidated into data warehouses and document management systems, jointly referred to as the universe of the insurance enterprise data.

2. Data exploitation could be done to cater to different needs like planning or analyzing growth of revenue, cost control, improving efficiency of operations, planning and executing business expansions, conceptualizing new products, and to provide data-related services to customers, distribution networks and employees.

3. Data Quality Management:?Most of the big insurance enterprises have been operational for several decades and hence the data available with them may not be 100% accurate. Many such insurance enterprises still use green screens for systems support and policy administration. Data quality could be maintained and ensured, by continuously checking, correcting and preventing data errors, thereby making data ready for exploitation.

The link between data acquisition, data quality management and data utilization could be described in the ICO (Input-Check-Output) model.