The traditional paradigm for provisioning data for software testing is evolving. What the industry currently refers to as Test Data Management (TDM) is changing with the times. Everything associated with the software release pipeline is being automated and integrated, except, that is, for the traditional and monolithic TDM model. With the help of synthetic data and Test Data Automation (TDA), software development and testing teams can unlock new levels of quality and efficiency.
Some analysts believe that the market for machine learning may surpass $9 Billon USD this year. The surge in growth in the ML market is exponential as new avenues for software development and applications emerge. With this surge in new applications comes the need for massive volumes of data to train ML models to perform at a high level of accuracy and consistency. Here, we present an overview of the role synthetic data can play in training machine learning algorithms. And we’ll identify the best applications for GenRocket’s Synthetic Test Data Automation platform in this rapidly growing industry.
Software performance testing is a form of non-functional testing that’s critical to providing a great user experience and ensuring the stability and scalability of any software application under heavy load conditions. To fully exercise your code, you’ll need a high volume of test data to simulate the most demanding real-world operating conditions.
We are often asked if GenRocket can perform data masking. The answer is, “Yes, of course – and with ease.” But like many questions asked about synthetic test data, the simple answer belies a complex explanation of the advantages of synthetic test data over production test data. Here, we explain five issues with masked production data and how Synthetic Data Masking overcomes them.
The GenRocket Test Data Automation platform has been in development for more than 10 years with a vision of accelerating and automating the delivery of accurate, controlled test data into any testing environment.
The meaningful use of electronic health records (EHR) has been at the forefront of the healthcare IT conversation since the 2014 American Recovery and Reinvestment Act. As of 2019, the CDC reports that 89.9% of healthcare practices such as physicians’ offices are actively using at least some or all of an EHR management system.
The healthcare sector is on the verge of a revolution in data and analytics, but the advancement of data-driven decision making has been hampered by difficulties in updating legacy systems, as well as challenges stemming from disparate data sources. With the growing push to digitize patient and claims information, the evolution of healthcare data exchange standards has come a long way to address some of these problems, but not all of them.
Enterprise Metadata Management is technology used to centrally manage and deliver high quality data and trusted information for business analysis and decision-making. Metadata is often referred to as “data about data” and describes the content, governance, and structure of enterprise information. Metadata is often used to create data catalogs that aggregate, group, and sort multiple data sources to make them accessible for a wide variety of use cases.