Business Intelligence & Analytics Solutions
Critical to business performance is intelligent, informed decision making. Fundamental to business intelligence systems is the numerical code which undertakes the complex computation, analytics, interpretation and result reporting. The NAG Library provides highly accurate and expertly supported mathematical and statistical code for use in business intelligence and analytics.
An unrivalled collection of reliable, portable and rigorous mathematical and statistical algorithms used in thousands of applications world-wide.
A specialist optimization consultancy service that offers expert advice on the extensive mathematical area of optimization.
The Software Modernization Service solves the porting and performance challenges faced by customers wishing to use the capabilities of modern computing systems.
NAG’s expert Training Courses are delivered by highly skilled and experienced practitioners in their fields. Material can be tailored to an organisation’s need or given to students and staff onsite.
Enhance software applications and speed up development time with tried and tested NAG algorithms and expertise.
Using NAG’s products and services can give you access to our highly expert technical support team. Every day they’re helping developers like you find solutions to complex algorithmic problems.
Collaborate with NAG
NAG is a not-for-profit organisation with roots firmly placed in academic and industrial collaborations. Learn more about how we engage and collaborate.
Nielsen has collaborated with Intel to migrate important pieces of HPC technology into Nielsen’s big-data analytic workflows including MPI, NAG Numerical Libraries, as well as custom C++ analytic codes. This complementary hybrid approach integrates the benefits of Hadoop data management and workflow scheduling with an extensive pool of HPC tools and C/C++ capabilities for analytic applications. In particular, the use of MPI reduces latency, permits reuse of the Hadoop servers, and co-locates the MPI applications close to the data. Learn more.