Design Clear Data Middle Platforms: Solving ‘Can’t See’, ‘Can’t Understand’, ‘Can’t Find’

01 Data Middle Platform Overview

Data middle platforms integrate data resources with production systems to support diverse front‑end business scenarios, providing capabilities such as data tables, indicators, reports, and tasks for the entire organization.

Typical problems in middle‑platform design include unclear information lists, weak brand awareness, and poor scenario continuity.

02 Data Visualization Cases

Data Middle Platform "Can’t See"

Unclear lists arise from stacked fields and repetitive information, increasing cognitive load. The solution splits lists into execution‑type (full field display) and dashboard‑type (key information only).

Example: the Experience Cockpit project, a one‑stop data monitoring platform, structures its dashboard into three layers: key information filtering, visual carrier design, and visual mapping.

Key information filtering involves prioritizing data based on importance.

Next, select visual carriers that match dashboard functions, using keywords like "view", "time", "status" to choose appropriate metaphors such as calendars.

Finally, build a marketing calendar carrier, mapping its structure to the dashboard layout and populating activities, time, and status.

Data Middle Platform "Can’t Understand"

Complex configuration items increase cognitive load, leading to inefficiency. Replacing business attributes with graphic symbols enhances brand perception and clarifies abstract concepts.

Case: Data Ark project homepage redesign. The original design was dull, with unclear logic and weak branding.

The redesign adopts a light‑tech design language, redefining brand symbols as the main visual, improving brand recognition and streamlining interaction flow.

03 Data Middle Platform "Can’t See" (Front‑Back Gap)

Many middle‑platform scenarios are closely related to C‑end interactions, yet communication between front and back ends is lacking, making the system appear as an iceberg where the front is only the tip and the massive back end remains invisible.

04 Conclusion

The article summarizes a set of visualization design methods for data middle platforms: analyze business scenarios, identify typical problems, devise design strategies, and apply appropriate mapping rules. Designers should extract multi‑dimensional keywords for each scenario, find suitable visual carriers, and create effective visual designs.