Five-Step Methodology for Building a Data Middle Platform

The "Five-Step Methodology for Data Middle Platform Construction" is a practical framework distilled from numerous data middle platform projects, allowing teams to emphasize or de‑emphasize specific steps based on project needs.

Step 1: Data Resource Inventory and Planning – Establish a complete, accurate inventory of all enterprise data assets, which forms the foundation for any data‑driven initiative. The goal is to (1) catalog existing data, (2) plan which data the enterprise should own, and (3) build a reliable inventory system with appropriate tools.

Step 2: Data Application Planning – Based on current technical conditions, define a comprehensive plan for data‑driven applications. This includes identifying data needs across business lines and roles, designing the set of applications that address those needs, and establishing an evaluation model that considers feasibility, business value, and implementation cost to determine the implementation order.

Step 3: Data Asset Construction – Build the core data assets that underpin the middle platform. This involves product selection, technical architecture design, standards and data‑warehouse modeling, data extraction (ODS layer), data development (cleaning, calculation), task monitoring and operation, data quality validation, and providing development support for downstream applications.

Step 4: Detailed Design and Implementation of Data Applications – Follow traditional software design processes (waterfall or agile) to develop data applications, typically within a database or data‑warehouse environment. Applications are delivered via BI dashboards, custom UI, or API services. Key differences from classic IT projects include a focus on data source quality, iterative optimization of complex data pipelines, extensive validation effort, challenging operations, and the need for ongoing data product operation.

Step 5: Data‑Driven Organizational Planning – Establish a strategic, high‑level organization responsible for the enterprise’s data‑driven agenda. Whether the team originates from IT, a strategic department, or another unit, it must serve as the core driver ensuring the middle platform’s successful rollout and continuous data‑centric transformation.

Author Biography – Zhang Xu, partner at Kangaroo Cloud, senior VP, Alibaba Cloud MVP, former general manager of Yonyou’s Application Integration Business Unit, and expert in master data management, enterprise integration, and data middle platform solutions, with extensive experience leading data‑centric projects for major Chinese enterprises.

The content is excerpted from the book "Data Middle Platform Architecture – Best Practices for Enterprise Data‑Driven Transformation" by Zhang Xu, Dai Li, Yin Saihua, et al., published by Electronic Industry Press.