iDXC

industrial Digital Transformation Consulting

From Strategy to Execution: Powering Your Digital Industrial Journey

iDXC Introduction

iDXC

Data-Driven Industrial Digital Transformation Consulting

Comprehensive analysis and value mining of existing enterprise data to accurately understand current digital transformation status and issues.
Based on the iDOP × iIAM × iDMP collaborative architecture, provide enterprises with an end-to-end upgrade path from data governance to operational implementation.
Plan a “growable” industrial digital system and architecture, helping enterprises achieve a data-driven, closed-loop process, agile-responsive intelligent operations system.

iDXC Features

Full-Chain Service: Data-Driven → Closed-Loop Construction → Value Mining

Data First: Organize enterprise data assets, build unified data catalog & models, establish metric standards & trusted data supply chain.
Process Collaboration: Model core business processes & value delivery channels, achieve cross-role/cross-system task closure, driving convergence of data flow, task flow, decision flow.
Application Empowerment: Based on digital twin concepts, design flexible, composable application modules by business scenario, plan easy-to-implement, scalable software systems.
Value Mining: Form a field-aligned, business-driven digital system, empowering business optimization and intelligent decision-making.

Why Choose Data-Driven?

Data-Driven Equals Business-Driven

Correctly collect, process, and aggregate frontline business data. Extract knowledge and patterns from vast data, significantly enhancing decision comprehensiveness and scientific rigor.

Personal experience and intuition take a back seat, avoiding low-level errors from human input, significantly improving process automation and execution efficiency.

Data-driven transformation doesn't rely on specific implementers; it's embedded as a model throughout the organization. The process isn't just about providing solutions, but building problem-solving capabilities.

Use urgent problems as examples. Help the organization learn, establish, and become familiar with the data-driven problem-solving model through the implementation process.