Modern businesses have complex structures, with the global workforce distributed across offices. Multiple decision-makers are often involved in important decisions, which need to be taken quickly. Even a small oversight can cause a major setback, impacting many stakeholders and reducing options for future decisions.
Large enterprises usually adopt an organizational structure that is designed to facilitate ease of management and control. However, this may cause undesirable side-effects such as scattered and fractured knowledge—about goals, operational processes, IT systems, design rationale, IT infrastructure, and best practices. As a result, the response to a change may at best be locally optimal and not translate to global optimality. Moreover, many change responses ripple across the enterprise—from strategy to operational processes and IT systems to IT infrastructure—all of which need to be kept in sync before, during, and after the transformation.
Today, this concern is usually addressed by relying on experts who track the various influencing factors, the decisions these factors influence, and the interdependence of these decisions. The size and complexity of modern enterprises makes this process challenging.
Take a look at how single modeling language can be used to specify all the relevant aspects that an enterprise needs for agile decision-making.