How to Profit from Big Data in the Post‑Privacy‑Law Era

Regulatory backdrop and industry shock

Four months after the enforcement of the Cybersecurity Law and related criminal interpretations, personal data protection has become a top priority, leading to investigations of dozens of big‑data firms and the shutdown of many data‑access APIs, prompting a debate on the future of the industry.

Strategic choice: platform vs. application

1. Building a platform to earn big money – Massive multidimensional data analysis can reveal insights unavailable elsewhere; the more data a platform holds, the higher its commercial value. However, merely scaling data volume is insufficient: modern analytics require granular user‑behavior logs, web content, signaling, routing, financial, and HR data, dramatically increasing storage and processing costs.

Data integration from diverse IT systems demands complex ETL pipelines, and maintaining data completeness and quality requires continuous resource investment, making platform development a capital‑intensive endeavor suitable mainly for large enterprises.

2. Building applications to earn small money – Concrete use cases—such as personalized customer care, precise marketing, or city‑level safety management—demonstrate tangible value and are more attractive to decision‑makers. Yet, when accounting for all costs—including data acquisition, trial‑and‑error, model training, and the need to rebuild models for new scenarios—the profitability of small‑scale applications can be overstated.

Future profit models

With direct data resale curtailed, platforms can monetize through industry reports and custom “competitor analysis” services, which aggregate data at a macro level to avoid legal risks. Indirectly, platforms add value by supplying data services to application developers; without thriving applications, platform revenues remain unsustainable.

Some firms may adopt a “small‑and‑beautiful” model, leveraging niche expertise in algorithms or specific business domains to deliver low‑cost, high‑impact applications, but they must balance resource constraints against growth potential.

Enablement vs. self‑sufficiency

Platforms act as “enablers,” providing foundational data infrastructure, while application‑centric companies pursue an “I can” approach, building end‑to‑end solutions. Both paths involve trade‑offs: infrastructure requires heavy upfront investment, whereas application development demands deep business integration to avoid misaligned, costly solutions.

Strategic decisions must consider whether to focus on data enablement, direct application delivery, or a hybrid model, recognizing that attempting both simultaneously often dilutes resources and reduces the likelihood of success.