Why Data Middle Platforms Are the New Production Lines for Data Products

1. Origin of the Data Middle Platform

In 2011, Alibaba’s founder announced a vision to create a 21st‑century data‑sharing platform for society, and in 2015 introduced the "big middle platform, small front‑ends" strategy that included a data middle platform. Subsequent marketing positioned the term as a flagship of digital transformation, prompting many enterprises to chase the concept.

2. Data Products

Data products are not new; they are essentially processed data offered as services, such as weather forecasts derived from sensor data. Companies like Dun & Bradstreet have sold processed commercial information for decades. The data middle platform extracts raw data from various business systems, processes it, and delivers value‑added services (e.g., product recommendations) to front‑end business units.

3. Essence of the Data Middle Platform

Data is likened to "the new oil," but raw data is a "muddy pool" without processing. Traditional IT systems create data silos, making data fragmented and difficult to reuse. Enterprises often store millions of tables and tens of millions of fields, yet only a tiny fraction (<0.1%) holds actionable insight. The data supply chain—raw data generation → acquisition → storage → value‑adding processing → consumption—mirrors a physical supply chain, with the data middle platform responsible for the processing stage.

4. Data Product Production Process

Data processing is divided into two stages:

Initial processing : Integrate fragmented data to reconstruct the original business reality, turning raw data into information.

Deep processing : Apply statistical aggregation, user profiling, machine‑learning, data‑mining, and other techniques to uncover hidden patterns and generate high‑value insights.

Examples include aggregating sales figures, building employee performance profiles, and leveraging algorithms for automated tagging.

5. Core Capabilities of the Data Middle Platform

The most critical capability is building an integrated team that combines business expertise, data engineering, and IT development to create data products. Business experts must define rules and provide domain knowledge, while engineers implement and iterate models.

Technical challenges include the growing variety and complexity of data, the need for robust development tools to support evolving processing rules, and talent scarcity. Success requires a mindset shift toward continuous improvement of data production technology.

In summary, a data middle platform is essentially a production line for data products, turning fragmented enterprise data into strategic assets through systematic processing and cross‑functional collaboration.