Designed to Scale: Enterprise Data Hub with webMethods and AWS
A large media company was facing very traditional enterprise integration challenges. The company had significant amounts of data, but access across the company was inconsistent. Data was spread out and housed within new cloud-based solutions and older legacy applications.
The existing solution was a file-based ERP related function that was brittle, slow, expensive to scale, and not reusable. The complexity of the ineffective solution resulted in individual business units implementing their own integration processes and resulting in a data warehouse becoming a high-traffic resource.
In contrast to the existing situation, the customer wanted a future-proof integration solution that could scale quickly and easily. Also, the solution needed to allow and support IT governance over one-off, shadow IT solutions. Focused on an event-driven design, the system needed to promote re-use in a pub-sub model, democratizing data availability by allowing business units ad-hoc discovery of objects via an API.
Big Compass worked side-by-side with the client to develop a design for an Enterprise Data Hub (EDH). The EDH would offer a centralized, unified data source where diverse business units could rapidly access the data needed for their specific functions. Through multiple phases, Big Compass provided an ESB architecture design, an API strategy, and initial use cases.
The ESB design included analysis and documentation for the creation of a webMethods environment, including server infrastructure, networking dependencies, and software configuration requirements.
The API strategy focused on the integration with existing API taxonomy and services.
Additionally, Big Compass helped the client with documented coding standards and best practices so the client’s support and development teams could own the applications and processes after the engagement.
Once completed, the client had an Enterprise Data Hub design that met the needs of reusability and scalability while promoting data transparency to the broader organization. The “shovel-ready” design not only enabled increased and continued IT governance, but reduced IT involvement and lowered costs.
ADOPTION & EXPANSION
+ Number of APIs
+ Business coverage
+ Number of contracted apps
+ API usage
+ API reuse
EFFICIENCY & COST SAVINGS
+ Number of APIs in each SDLC stage
+ Time spent in each SDLC stage
+ Cost and time to build an API
+ App development velocity
+ Number of launches per year
+ Number of defects
SECURITY & VULNERABILITIES
+ Security violation
+ Policy enforcement
+ Time since the last version was published
+ Number of throttling issues
+ Time to onboard
+ Number of deployments
+ Number of incidents
+ Percentage of customers impacted. per incident
+ Time to resolve incidents