Data Integration

Data Integration is the action of combining data to extract meaningful information and business value.

If we become more technical, data integration is about exchanging content among systems and analyzing it to design decision support systems for business requirements.

Terminology

· A Data Integration Layer is a layer which integrates multiple source systems with target systems.

· A Back end is something which shares data and sources called as source system).

· A Connector is the component which connects source and target so they can share data among each other.

· A Front-End is a target system which utilizes the data for making decisions. It can be OLAP or a digital application.

· An Orchestration is a process in which defines when data will travel from source, when it will be transformed in the shape that target can understand and when it will push to target system.

In the previous diagram:

· The Web Application, the Mobile Application, the Front-End A are Front-Ends.

· The ERP, Legacy Tool, and Back-End B serve as back-end systems.

· To connect to ERP, Legacy tool and Back-End B, those can be connected via C1, C2 and C3, respectively.

For example, orchestration can involve a step where the mobile initiates a call to the Data Integration Layer, which then a call to both the ERP and Back-End B to pull required data for the process.

Features

When selecting a Data Integration tool, it’s crucial to thoroughly examine its features.

The essential attributes to consider include:

· Integrating Disparate Systems

· Provide a Connectivity Layer via Connectors

· Provide a Business Process Layer via Orchestration

· Provide Common Integration Services

· Support Service Composition and Modular Architecture

· Support Conditional Transitions

· Support “Looping”

· Support Scheduling

· Support Wait/Sleep Controls

· Support Throttling

· Support Multicast

· Support Data Transformation (Basic and Complex)

· Manage Meta Data

Patterns:

Data Integration can follow multiple patterns. One of the most frequent examples is ETL (Extract Transform Load — Refer to the ETL topic for more information).

Example: Data Integration in Manufacturing

In the manufacturing sector, Data Integration is also widely utilized. It empowers the end-to-end supply chain as well as the manufacturer operations. It aids manufacturers in facilitating their global processes and operations by enabling real-time ordering and invoicing, parcel tracking, monitoring inventory levels, and tracking efficiency, along with reporting.

Suppose Smart Electronics operates as a manufacturer of electronic products. It sends electronic parts all over the globe.

Smart Electronics aims to predict delays within its global supply chain. Leveraging Data Integration, Smart Electronics can access real-time updates regarding the status of the latest capacitor shipment from their Japanese supplier.

In this example, Data Integration is providing Smart Electronics with the right information (Status of Shipment) at the right time (Up-to-date information) to support business operations.

Business Value:

Data integration empowers organizations to link and synchronize data from various origins, facilitating enhanced decision-making and a data-driven approach. To make sure their Digital Transformation Initiative is successful, organizations must implement data governance, data quality, data security and other best practices for data integration. Additionally, organizations ought to embrace data integration strategies tailored to their requirements. As organizations progress in their digital transformation efforts, data integration will remain a fundamental aspect of their journey.