Existing design methods for enterprise software systems rely upon system requirements being fully known and largely static. Model driven approach, courtesy separation of functional and technology concerns and automatic derivation of the desired implementation from high level specification, has found industry acceptance for developing such systems.

Globalization market forces, increased regulatory compliance, ever-increasing penetration of internet, and rapid advance of technology are some of the key drivers for increased business dynamics. As a result, enterprise systems need to be agile and adaptive. We explore how model-driven techniques can be used to impart these properties with certainty.

Business-critical nature of enterprise software systems means there is no room for error in identifying the right adaptation. We believe the key lies in ability to specify enterprises in terms of high level models that are amenable to analysis and simulation.

Need to deliver insights is making enterprise systems depend heavily on data analytics. We investigate how to reduce the essential complexity of developing analytics solutions.