In a sector like insurance that is marked by its inherent complexity and diversified structure, development of new features and maintenance of the insurance application is a challenge. But a bigger challenge is test data management as the data in such applications tend to be sensitive, interdependent and often show high redundancy.<\/p>\n

Let us look at some of the reasons that make test data identification a challenge in the insurance sector.<\/p>\n

[Tweet “Top 5 reasons that make test data identification a challenge in the insurance sector.”]<\/p>\n

Regulatory Compliance:<\/strong><\/h4>\n

Similar to banking, insurance is a sector that is heavily regulated by federal and state laws. Frequent changes in regulations require making changes to the application for compliance. These recurrent releases make test data management an arduous task. Also, legal obligations to protect customer information forces insurance organizations to have some mechanism to mask or obfuscate production data before making it available for testing.<\/p>\n

Complex workflow:<\/strong><\/h4>\n

The insurance sector is characterized by a variety of insurance services like life insurance, property insurance, vehicle insurance etc., each of which have a varied range of products and business segments. All transactions like new business, contract management, claims, policy renewal have their distinct workflows. Such diversity and complexity implies there will be a variety of business scenarios that need to be tested which makes test data management a huge task.<\/p>\n

Lack of sufficient automation:<\/strong><\/h4>\n

Insurance is one sector which is yet to take advantage of automation possibilities on a large scale. Data across various business segments is mostly non-standardized which again makes automation a difficult proposition. Employing data mining and test data management tools can make the test data identification and set up process less tedious.<\/p>\n

Domain knowledge: <\/strong><\/h4>\n

Developers often lack sufficient domain knowledge with respect to the many insurance types, each with their own business rules and workflows. Workflow, in insurance, may again vary depending on age and financial status. Without domain knowledge, identifying and preparing test data that meets your test criteria is a challenge.><\/p>\n

Lesser time to market:<\/strong><\/h4>\n

Insurance is a highly competitive sector with new products and services being announced regularly. At any time, there may be multiple IT releases going on to support them. To maintain a competitive edge, companies are forced to release new products in short time. This puts immense pressure on developers and testers who have to come up with a high quality, winning product in a limited window of time. This makes it challenging for testers to devote adequate time on activities like test data identification, test environment set up etc.<\/p>\n

Better Testing, Better Quality through Cigniti<\/strong><\/h3>\n

Is lack of skilled QA resources holding you behind? It might be time for you to partner with a leading testing services organization like Cigniti.<\/p>\n

[Tweet “lack of skilled QA resources holding you behind? partner with leading testing organization @Cigniti.”]<\/p>\n

Cigniti is the world’s third largest independent testing services organization. We specialize in providing testing solutions for a range of industries including the insurance sector. Our testers bring you the advantage of technical expertise and unmatched domain knowledge. We are proud to say we have been cited by leading research firms like Gartner<\/a>, Forrester<\/a> and Nelson Hall<\/a> for our commitment to quality.<\/p>\n

For information on our testing and QA staffing services, write to us at info@cigniti.com<\/a> today!<\/p>\n","protected":false},"excerpt":{"rendered":"

In a sector like insurance that is marked by its inherent complexity and diversified structure, development of new features and maintenance of the insurance application is a challenge. But a bigger challenge is test data management as the data in such applications tend to be sensitive, interdependent and often show high redundancy. Let us look […]<\/p>\n","protected":false},"author":2,"featured_media":403,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3069,199],"tags":[125,151,368,146,147,149,150,148,145,144],"ppma_author":[3736],"yoast_head":"\n