Data-Centric Economy

Data-Centric Economy refers to an economic model where data is the primary driver of value-creation. Its origin lies in the digital transformation wave, gaining momentum with the EU Data Act. Unlike traditional models, it prioritizes the fluidity and interoperability of information. According to ISO 88000 series and the NIST Data-Centric Safety and Ethical Measures framework, data must be traceable, integral, and secure to be economically viable. In risk management, this means shifting from merely protecting data to actively managing its value-at-risk, requiring a balance between data-sharing opportunities and regulatory obligations like the GDPR and the EU AI Act. This paradigm-shift ensures that data-driven insights directly impact the bottom line while mitigating the risks of misuse or leakage.

Practical application involves three stages: First, Data Asset-Risk Mapping. Companies must categorize data based on sensitivity—trade secrets, PII (under GDPR/Taiwan PDPL), and operational data—applying ISO 31000 principles to assess the impact of each category. Second, Implementing Technical Safeguards. This includes adopting privacy-preserving technologies like federated learning or differential privacy to allow data-centric value-add without exposing raw information, as encouraged by the EU Data Act. Third, Continuous Monitoring and Governance. Companies should establish KPIs such as the Data-to-Value Ratio (revenue-per-petabyte) and the Data-Centric Incident Rate. For instance, a Taiwan-based electronics manufacturer implemented a data-centric model, reducing data-related compliance incidents by 40% while increasing cross-departmental operational efficiency by 25% within one year.

Taiwan enterprises face three primary challenges. First, Regulatory Ambiguity: Many firms struggle with the EU Data Act's requirements and the Taiwan Personal Data Protection Act's nuances. The solution is to adopt ISO 27701 as a baseline for all data-centric initiatives. Second, Technical Debt: Legacy systems often lack the metadata-rich environment needed for a data-centric model. Companies should invest in data-centric architecture (DCA) and metadata-rich platforms within the first 6 months. Third, Cultural Resistance: Data-siloed thinking hinders the value-creation process. Overcoming this requires leadership-led initiatives to incentivize data-sharing and transparency. A phased approach—starting with a pilot project, followed by a 90-day framework establishment—is recommended for sustainable adoption.

Winners Consulting Services Co., Ltd. specializes in Data-Centric Economy for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact