The Achilles’ Heel of Decision-Enabled Architectures
Technology departments have become very adept ‒ when provided with detailed, unambiguous and verified requirements ‒ at producing working code delivered in sophisticated architectures. Converting bullet-proofed requirements into value is faster than ever with methodologies, such as Agile, and technology toolchains, including such capabilities as containerization and API gateways (and many others).
However, the Achilles’ heel of virtually all our clients is the cost of producing unambiguous, verified requirements, and particularly in terms of the effort required to close the gap between how the business and IT understand the same logic. This includes both new logic provided by the Business, and old logic in existing systems that causes IT to engage in archaeological digs, so they can explain this logic to the Business. The scarcity of Subject Matter Expert (SME) and technology implementer availability compounds the problem. It leads to long delays, many cycles of back-and-forth exchanges, and late discovery of incorrect logic in the downstream UAT testing cycle, where the tight time constraints often lead to de-scoping and increased operational incidents, raising the total cost of ownership (TCO).
The problem is further compounded when the driver for enacting the business logic is as diverse as user interactions with screens in a system, tasks in a process flow (online or in batch), or events identified in the business operational lifecycle. The diverse drivers may be implemented using heterogenous technologies, such as low-code Application Platform as a Service (APaaS) products, event listeners over big data or streaming analytics, or service- or micro-service-based (e.g. MuleSoft or Apigee) architectures, among others.
In many years of experience with various profiles of clients, we’ve learned that using a rigorous logic model, and appropriate tooling can effectively close these gaps. They enable business people to create validated and tested business logic that transforms into auto-generated executable code, that can be discoverable at run-time and easily or automatically integrated into various system architectures and DevOps best practices.
To learn more about this topic and Sapiens DECISION’s solutions, and to view some technical diagrams, please download my white paper here.Share this blog post