How Enterprise Data Sharing and Product Development Unlock Business Value

Data Sharing and Analysis

After data assets begin to show results, enterprises must implement data sharing mechanisms and supporting analysis tools so that various business units can access and analyze these resources. Platforms such as data marts, data middle‑platforms, and shared labs provide processed data, APIs, and analytical environments, while BI platforms handle statistical reporting and data‑science platforms enable machine‑learning workloads.

Platform Types and Flow

Data Marketplace : Business‑driven processed results are batch‑loaded daily and primarily used with BI tools for statistical analysis.

Data Sharing Platform/Model Lab : Full‑volume data is requested on demand; analysts apply for access, undergo approval, and then use data‑science tools for machine‑learning and advanced analytics.

Data Middle Platform : Technology teams create indicators, tags, and datasets as data products delivered via APIs or DaaS services; these are consumed by downstream applications.

Industry Example – Banking

Chinese banks initially built MPP‑based data warehouses and data marts, later evolving to big‑data platforms, data lakes, and dedicated data labs for risk, audit, and regulatory analytics. Recent years have seen cloud‑native, multi‑tenant architectures supporting comprehensive data sharing and analysis.

Emerging Data Federation

For ad‑hoc analyses that require data not yet loaded into lakes or warehouses, data‑federation engines allow analysts to query across heterogeneous sources (data lakes, databases, etc.) without handling underlying complexity, improving flexibility and speed.

Data Product Development and Internal Operations

Data products are processed, analyzed, or modeled outputs packaged as APIs, indicators, AI models, datasets, or databases, with dedicated development, release, and operational processes. Low‑code or no‑code platforms (e.g., Salesforce Einstein) enable rapid creation of data‑driven applications, while traditional micro‑service approaches embed data‑centric logic into enterprise systems.

External Value Creation – Data Capitalization

Data can be monetized through data‑as‑a‑service offerings, APIs, data packages, and solutions, contributing directly to revenue and even appearing on financial statements. Both domestic and foreign data marketplaces illustrate various models for data supply, compliance, and pricing.

Data Sandbox and Security

Data sandboxes provide isolated environments where data can be ingested but not exported, ensuring "data in, no data out" security. They support multi‑level isolation, resource quotas, on‑demand scaling, and built‑in SQL, programming, and visualization capabilities.

Security and Compliance Requirements

Database security: authentication, authorization, static/dynamic masking, SQL audit, access control, periodic data cleanup.

Infrastructure security: network‑level whitelisting for component access.

Multi‑tenant isolation: tenant, sandbox, and space isolation with controllable resource quotas.

Lightweight, on‑demand resources: sandbox can start/stop quickly and reuse existing analysis tools.

Extensible analytics: default SQL development, programmatic modeling, visual analytics; customizable toolsets.

Support for varied security classifications and deployment models.

Compliance Operations

Enterprises must regularly classify and grade data assets, assess security risks, implement technical and organizational controls, and maintain continuous security operations including monitoring, incident response, and iterative improvement.

Conclusion

The article covered five stages of enterprise data platform construction: foundational storage and compute, data assetization, data sharing and analysis, internal operational enablement, and external value creation, setting the stage for deeper technical discussions in upcoming parts.