- Posted by Marketing Daitan
- On November 9, 2017
- Architecture, Digital Transformation, Event-Driven Architecture
How to Prepare, Migrate and Scale an Event-Driven Architecture
In the twenty-first century, the transition to Digital Business changes everything. Organizations are switching from inward-facing enterprise-centric models to outward-facing ecosystem-centric models. With new ecosystem focus comes a transition from data-centric thinking to event-centric thinking.
The concept of event-centric thinking emerged in the early 2000’s initially around Messaging and eventually became known as Event-Driven Architecture (EDA). This idea would enable companies to leverage events into real-time actionable information. By definition, “Event-driven architecture (EDA), is a software architecture pattern promoting the production, detection, consumption of, and reaction to events.” 
As examples, Waze and Uber are two prominent companies demonstrating how Event-Driven Architecture is key to delivering disruption in ways that today’s Digital Businesses compete in an on-demand economy.
Event-Driven Architecture Enables Digital Transformation
Situational awareness, real-time responsiveness and informed decision making are critical to managing ecosystems that include suppliers, business partners and ultimately customers. External requirements including conformance and environment protection are additional drivers toward event-centric ecosystem thinking. Event-Driven Architecture (EDA) supports and enables critical trends in the transformation to Digital Business. According to Gartner, some examples include:
- Responsive Customer Engagement: Events with added situational awareness enable context enriched customer experiences.
- Capitalizing on Business Moments: Coalesced groups of events indicate significant business moments. Companies can respond in (near) real time.
- Internet of Things: Events from sensors are the single most important foundation of IoT. Event-Driven Architecture is the best option.
- Artificial Intelligence and Machine Learning: Many of the applications involve loops of event collection, analysis and response.
 K. Mani Chandy Event-Driven Applications: Costs, Benefits and Design Approaches, California Institute of Technology, 2006