Sinovatio Partners with Jikeng Xinyang on Data Governance | Trusted Data Foundation for Manufacturing AI

The kickoff meeting for Jikeng Xinyang Group's data governance project was recently held at the group's headquarters. After a three-month competitive solution review process, Sinovatio was officially appointed to deliver the group's data governance framework and platform. Both sides aligned on the overarching goal of making enterprise data "trusted, usable, controllable, and secure," and on the implementation roadmap—marking the group's transition from solution selection to foundation building.

Who Is Jikeng Xinyang—and Why Sinovatio for Group-Wide Data Governance?

Founded in Hong Kong in 1993, Jikeng Xinyang built its first factory in Dongguan, Guangdong, in 1994, and has since established manufacturing bases across Dongguan, Ganzhou, Chongqing, Shanghai, as well as in Japan and Vietnam. Its operations span industrial equipment, home appliances, automotive electronics, office equipment, and new-energy storage, providing dozens of Fortune 500 clients with one-stop intelligent manufacturing services from R&D design and substrate manufacturing to complete assembly. The company positions itself as a "global full-value-chain intelligent manufacturing solution provider."

As a multi-site, multi-business-group manufacturer, Jikeng Xinyang's digital transformation in recent years has centered on end-to-end coordination across R&D, production, supply, sales, and service. Yet siloed systems, inconsistent standards, and poor interoperability had made it hard to aggregate data and make coordination truly effective. This is precisely why it chose to prioritize data governance first—captured by a leader's remark at the kickoff: "Act early and stay proactive; act late and stay reactive." Here, data governance is more than a system rollout—it turns the "indescribable" into the "visible."

How Will It Be Built? Three Tracks in Parallel, Phased—not Big-Bang

Both sides shared the same objective: make enterprise data "trusted, usable, controllable, and secure," ensuring a single source of truth, full visibility, and cross-group sharing—laying the foundation for future digital operations and AI use cases. Three workstreams run simultaneously:

Consulting First — Establish the Rules Map the group's data governance blueprint, build the data management system, and cultivate the client's own data talent—so governance capability does not remain trapped in the project team.

Platform Foundation — Build the Bedrock Deploy a unified data governance platform, consolidating master data, metrics, and data assets, and establishing a single controlled gateway for enterprise-wide data.

Application — Put Data into Operations Build metric systems and business cockpits around key business domains, moving data from "after-the-fact reporting" to "day-to-day decision-making."

Implementation follows a phased approach—pilot first, then scale, then institutionalize:

• Pilot first: Start from management's top-priority operational view, running through one complete line of business to validate methodology and platform.

• Scale gradually: Expand business domain by domain, turning the proven path into replicable methods and templates.

• Institutionalize operations: Move to trial operation and knowledge transfer after acceptance, embedding governance capability inside the client's organization.

Results will not materialize all at once. The typical path is three steps: first unify definitions so the same metric reconciles across layers and business units; then control quality so data issues are detected early and traced to source; finally accelerate response so operational analysis shifts from manual aggregation to platform support. Only then does data become a dependable enterprise asset.

Why Now? Because Trustworthy Data Is the Prerequisite for AI

At the kickoff, both sides underscored that data governance answers one core question: "Can AI trust the data it receives?" Data must be trustworthy before AI can be deployed at scale. And to make data flow across the organization, a human-and-agent collaborative enterprise AI system is needed—exactly the positioning of Sinovatio's AI WorkForce platform:

• OceanMind data governance module: ensures data can be invoked by AI with trust;

• OntoBase semantic foundation: lets all agents work under one shared business semantic layer;

• Agent development & operations platform: orchestrates cross-department, cross-system agent tasks;

• AI OmniSight (AI governance gateway): centrally manages agent identity, cost, and risk.

The Value: From "Indescribable" to "Visible," Supporting Long-Term Growth

For Jikeng Xinyang, the value extends far beyond go-live day. Once definitions are unified, quality is controlled, and response speeds improve, business leadership can spot problems earlier and diagnose causes faster—giving concrete support to its vision of "enduring success."

For Sinovatio, this project represents another benchmark deployment of its data governance capability within a complex manufacturing group, paving the way for industry replication and further group clients. Bring order to messy data, put the right data to work, and turn data into an asset—and the ranks of organizations walking this path together are growing.