Building an Intelligent Enterprise Integration
For the past few decades, organizations have been focused on traditional integration SOA [Service-oriented architecture]services, which keep data flow between distributed systems less focused on business integration – a move that would otherwise bring real-time data visibility and integrity to enable more informed data-driven decisions. Most organizations have multiple complex integration platforms that delay the availability of data for analysis in real-time. Despite significant investments, organizations are still working on vague and unclear offline reports instead of using real-time, multi-dimensional analyses before executing customer transactions.
Organizations continue to have multiple applications across functions to manage their businesses, and key decision makers often lack the comprehensive information and data insights they need to make effective decisions. For example, a local business may experience low revenue for a certain product and make a decision to discontinue the product. However, with the right presentation of data, decision makers could have seen that the same product may have produced another outcome through a different channel or market or by sourcing the product from a different supplier. Another example is when a company may be impacted by high production costs. Again, with the right presentation of information, other avenues for cost-saving could be explored such as identifying bottlenecks in the production lines, changing product attributes or analyzing the product's flow in the supply chain.
But unfortunately, in many cases, the data points do not reside in a single transactional system. They are stored in different systems. Transactions are not captured on a real-time basis and limited data points flow between systems just for the sake of completing the transaction.
IT leaders should also transform the traditional service-based integration platform into an event-driven enterprise platform with APIs [Application Programming Interface] and microservice architecture
The digitization of transaction and integration layers are important in collecting data and passing the information immediately into the analytical layer. To initiate a digital transformation, the following are the key steps to success for IT leaders with an understanding of the business model.
Enterprise integration designs should start with business focus
1. Business objectives such as speed to market, supply chain efficiency, customer service, productivity, and cash management should be of the same priority as the execution of the transaction itself. To complete a customer or vendor purchase order, few data points are enough. Additional real-time data points are needed to generate useful information about the productivity, supply chain efficiency, and customer satisfaction of the product.
2. Understanding the value chain is important in order to realize the value proposition. In addition to data integration initiatives, leaders should adopt digital technologies into the business process, which will increase automation and improve the value chain.
3. As mentioned earlier, business integration should have the same priority (if not more!) than system, process and data integration. Businesses should be able to measure and monitor the service levels at each stage of the operation than just the simple data exchange between applications. IT leaders should also transform the traditional service-based integration platform into an event-driven enterprise platform with APIs and microservice architecture.
Position Digital Integration as a Transformational Service
Build your integration platform as a service to break down data silos by integrating data sources, devices, and on-prem and cloud applications in order to provide seamlessly connected environments across organizational functions and partner ecosystems. These four are key technology initiatives for building a digital platform to manage a transformation.
Integration Platform Strategy: As more and more companies expand globally through mergers and acquisitions, they accumulate many internal integration platforms. Data flow through multiple integration platforms limits end-to-end visibility for businesses, and as a result, managers may fail to measure service levels to meet customer expectations. IT organizations should plan to integrate multiple integration platforms by adopting innovative technologies to ensure both inbound and outbound functional processes run through an intelligent platform to monitor and achieve organizational efficiency. IT management should consider experimenting with and deploy RPA [Robotic Process Automation] that includes AI and ML as microservices to collect more data points from various applications in order to monitor the business process and make more informed decisions. Intelligent integration platform should lead to next generation RPA platform that includes workflow orchestration, business rules management, mobile capture, analytics and digital messenger.
Lean Data Management: Lean data management plays a key role in Industry 4.0. Efficient data movement provides insights to measure operational efficiency on a broader level. Today's companies are aiming to reduce wastage in the production process by implementing leaner and more efficient systems. Integration platforms should be designed to capture additional data points from applications and IoT [Internet of Things] devices to monitor data quality and intelligence at every stage to aid in the identification of process delays and bottlenecks.
Leverage Microservices: Microservices will not only decrease development cycle times but will also reduce data flow cycle times between applications and machines. This is critical for Industry 4.0 to enable information flow between people, systems, machines and external partners and achieve a connected environment. Microservices architecture mobilizes data between systems in real-time with the use of APIs and assist managers with a predictive analytical view. For a period of time, both cloud and on-prem platforms will continue to exist until organizations fully migrate into cloud integration.
Security and compliance: To achieve the highest level of data integrity, the integration platform should be designed in such a way that data is secured across all services for businesses to have reliability in their governance, risks, and compliance. To prevent data theft/tampering, policies should be established to detect access violations and implement SOD [Segregation of Duties] controls so that no single individual can control the end-to-end data flow.