How to Overcome Data Governance Challenges and Unlock Business Value

Challenges and Pain Points in Data Governance and Utilization

Enterprises face many challenges in data governance and data utilization. First, data governance is difficult because it requires both technology and business participation, with clear responsibilities for each role. Second, user departments focus on business development rather than data management, lacking motivation to implement governance measures. Additionally, balancing standardization with flexibility for business development and system development adds difficulty. Finally, a lack of data lifecycle control leads to data quality issues, hindering regulatory statistics and mining analysis applications.

On the other hand, enterprises encounter many problems in data integration. Previously, unplanned IT architecture and siloed system construction created information islands, making data integration difficult. Moreover, the absence of a unified data model and standard specifications further complicates integration. Insufficient control over data lifecycle stages also results in data quality problems. These issues impede the building of data application capabilities and the cultivation of a data-driven culture.

Therefore, to address the above issues, enterprises need to formulate a reasonable strategy to implement data governance and utilization. First, establish clear data management rules and processes, including data standard management, quality management implementation, and metadata and data dictionary management. Second, strengthen user department awareness and motivation for data governance to ensure smooth implementation. Finally, devise a strategy that balances standardization and flexibility to ensure data management can adapt to business development needs.

Organic Integration of Data Governance and Utilization, Complementary and Mutually Reinforcing

To achieve an organic combination of data “govern” and “use” so that data truly becomes “governed and useful”, we propose a data asset management system oriented toward data applications and driven by business value. This system aims to manage data assets throughout their lifecycle, establishing mechanisms, processes, and methods for data asset admission, updates, etc., promoting effective data integration and supporting various data applications such as marketing, risk control, and operations, thereby ensuring the realization of development strategy and business value.

1. Leverage applications to solidify governance foundation

● Use to promote governance, with clear methods

Facing the complexity and long‑term nature of data governance and control work, many financial institutions adopt a “small‑step, fast‑run, urgent‑use‑first” data‑driven strategy. This means governance tasks are implemented in steps and phases, aligned with the priority order of data‑use requirements.

Using data‑use needs and business development as a catalyst, financial institutions first carry out data standardization and quality improvement in key business areas. This addresses the issue of poor basic customer data quality despite a large proportion of retail and micro‑business. By rebuilding the customer master data management system, implementing basic data standards, cleaning, supplementing, and integrating existing data, and planning customer data tags and 360‑view applications, they iteratively improve customer data standardization and quality.

● Use brings governance, shared results

Data governance work should originate from business and return value to business; its effectiveness emerges through continuous data integration by technology teams and data‑driven analysis by business units. Cultivating a data culture across the organization, stimulating innovative application scenarios, and supporting the design, implementation, improvement, and maintenance of data applications enable participants in governance to experience the value of their work. On this basis, establishing assessment and incentive mechanisms that combine “govern” and “use” becomes more effective.

Data governance requires scientific methods and long‑term perseverance, but enterprises with solid governance foundations will achieve greater efficiency in supporting decisions and realizing data‑asset value. “Use” drives “govern”, and results are shared.

2. Ride the wind of governance, boost data applications

Enterprise data value evolution begins with data governance. By advancing data planning and governance—inventorying, sorting, and planning various thematic data assets, and conducting targeted governance and quality improvement of existing data—organizations achieve unified business terminology and standards across the enterprise. Simultaneously, they build a comprehensive technical platform, including source‑level data lakes, enterprise‑level data warehouses, and domain‑specific data marts, facilitating cross‑domain data asset integration and providing a solid foundation for data‑driven applications.

● Data enlightens, insight shines

Benefiting from high‑quality integrated data assets and supported by various algorithm models and data processing capabilities, banks can plan and build data applications for diverse needs.

Management cockpit is a typical application that, based on an understanding of management requirements, provides a “one‑stop” decision‑support platform for executives. It presents multi‑view, comprehensive indicators of bank performance, risk analysis, and key business metrics, relying on an enterprise‑wide integrated data platform and real‑time or near‑real‑time computing, as well as well‑defined data domains, dimensions, attributes, and statistical standards.

For example, a credit asset portfolio analysis model uses existing internal data to comprehensively analyze the credit asset portfolio, designs related analysis models according to bank needs, evaluates risk‑return relationships, and, combined with external environment factors, optimizes model parameters to produce credit asset position adjustment plans for increased returns.

Non‑on‑site risk monitoring platforms start from risk scenarios, build compliance risk monitoring models based on massive transaction and operation log data, assess and identify risk conditions, and achieve intelligent risk early warning and automatic risk report generation through monitoring indicator systems and rule libraries.

In summary, financial institutions progressively improve data governance and digital transformation by organically combining data “govern” and “use”. Data management lays a solid foundation for data applications, helping realize data‑asset value, while data applications guide data management toward maturity. Both depend on each other. As the importance of data assets grows, institutions should strengthen the practice of “govern” and “use”, align with regulatory requirements, standardize data order, and unlock data value.