The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.

How to combine your enterprise data with generative AI.

With more emerging players entering the insurance market and insurance companies recognizing the importance of the digital experience, consumer demands for a connected insurance experience have grown to new heights. For insurance customers, the claims process is a critical moment of truth, making it essential that insurers deliver a hassle-free, seamless experience with faster claims processing and excellent customer service.

With all the recent discussion about the use of artificial intelligence (AI) and large language models (LLMS) like ChatGPT, you may think that AI is a new phenomenon. But in fact, the US Department of Defense (DoD) has been investing in AI for more than 60 years.

What determines the success of an API? A significant part lies in mastering the API lifecycle—from planning to retiring, every step impacts your API’s performance and adoption. The API lifecycle involves several critical stages—planning, designing, developing, deploying, consuming, monitoring, and retiring. Each stage dictates the successful implementation of APIs, including governance models, transparency, and security being crucial throughout.

The term ‘data persistence’ refers to data that remains available, even when the program that created it is idle, sleeping or unable to open. In many cases, our iOS apps need to provide support around the clock, so we need our data to be ‘always on’ – even when the apps themselves are not.