Every enterprise needs to share the data between their applications to achieve efficiency, and no application in the modern world can operate in silos. An end-to-end business transaction will have to pass through multiple applications with defined service level agreements. In such enterprise scenarios, integrations become crucial from the business operations perspective. Let us go through the important factors to be considered in enterprise integration design.
Understand from the business view the activities or support functions in the business value chain the integration enables and the necessity of each supporting data element that is considered in the integration. If you ask the question, why there is a need for the integration, it will provide ground-level clarity and real business requirements for the integration. This will help to focus on the absolute need rather than integrating every available data.
Work OrderSource of Truth: This is an essential part of the integration design. No matter the latest technologies and toolsets used, there is no success without proper data management. Enterprise data architecture should identify a proper system of records for the data sets, for example, customer data, asset data, work data, etc.; integration design should consider the system of records defined by the data architecture.
Work OrderMetadata Management: By 2025, the amount of data generated each day is expected to reach 463 exabytes globally. With enterprises collecting data at an accelerated pace, managing it becomes vital. It's critical to track its metadata (5W - What the data means, Where it is sourced and managed, When it is created, How it is modified, and Who owns it.) Otherwise, you will be searching for a needle in the haystack. Data Sensitivity: With growing regulations around PII (Personally identifiable information) such as GDPR and many other regulatory and business-critical data, the metadata management must include attributes to maintain the criticality of the data. Transferring data related to customer information is not as easy as sending public information.
Work OrderData Structure: Be thoughtful and meticulous to craft all the data elements required for the integration. The data structure neither includes every available attribute nor a minimal list of attributes. Arrive at a sweet spot to confirm the required data for integration. Be considerate about the type of data- integrated map data, IoT sensor data, Asset Modelling data, P&ID or network diagrams, etc. In these cases, it will be prudent to contextualize the required data for integration as it can be tricker and complex to manage unnecessary data in the integration.
In-Service Oriented Architecture, service is often defined as a logical representation of a repeatable activity with a specified outcome. Typically, service is self-contained. While defining the integration services, apply the same approach as above; services should be neither too big nor too small to break down the data structure into services. Typically, it can be grouped based on business functions in combination with other application parameters. A Proper definition of services will increase the re-usability and easy future expansion.
Any unsuccessful attempt to reach the target during the integration data flow is generally referred to as exceptions. They typically fall into two major categories, Business validations and Technical Errors. Apply the 80/20 rule during the integration design, i.e., 80% of your efforts should focus on the exception scenarios you may encounter and ways to handle them. Any integration exceptions will cost the enterprises in many ways. It will affect the business KPIs, data synchronization between major systems, extensive sustenance efforts for identification and reprocessing, etc. Design should include ways to identify the exceptions during processing, notification to a respective group, reprocessing (automated/manual), exception reporting with criticality, and funnel the enhancements to integration design sustenance.