Organisations implement data governance for different reasons, which can broadly be categorised as reactive or proactive.

While reactive governance acts after problems occur, proactive governance seeks to prevent future challenges and use data as a strategic asset. It can be divided into two fronts: preventive and strategic.

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Reactive governance: fixing problems after they happen

Reactive governance arises as a response to incidents that have already caused damage. Companies following this approach typically adopt corrective measures after crises, such as data breaches, compliance failures, or operational inefficiencies.

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Example 1: data breach in a retail company

A large global retail chain suffered a breach that exposed millions of customer records. The incident resulted in regulatory fines, lawsuits, and reputational damage. To mitigate the impact, the company implemented:

• Strict access control to sensitive data
• Enhanced security measures, including encryption and monitoring
• Compliance policies aligned with data protection regulations

Although effective, these measures only came into play after the damage had occurred.

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Example 2: compliance violation in a financial institution

After an audit revealed missing and inaccurate transaction data, a financial institution was fined US$5 million. To restore compliance, it implemented:

• Data quality controls to detect and correct errors
• Internal reporting mechanisms for transparency
• Clear roles and responsibilities for data owners

Again, the actions restored compliance, but the issues could have been avoided with a proactive strategy.

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Proactive governance: preventing problems and creating value

Proactive governance goes beyond risk prevention. It turns data into a competitive advantage, supporting strategic decisions and more efficient operations. This approach can be divided into preventive and strategic.

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1) Preventive: anticipating future challenges

Preventive governance identifies potential risks and acts before they become real problems.

Example: preparing for new data privacy laws‍

A healthcare provider anticipated stricter regulations and adopted proactive measures:

• Classified patient data by sensitivity level
• Strengthened security protocols
• Conducted regular compliance audits

When the new regulations took effect, the organisation was already compliant.

Example: data integration after a merger‍

A bank acquired a regional institution and faced the challenge of integrating different systems. To ensure a smooth transition, it:

• Created a centralised data warehouse
• Established a standardised governance framework
• Implemented Master Data Management (MDM) solutions

This prevented operational bottlenecks and data conflicts.

2) Strategic: using data to improve operations

Strategic governance turns data into a business asset, improving decisions, operational efficiency, and customer experience.

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Example: improving decision-making in a tech company‍

A cloud software company identified unreliable data as a barrier to decision-making. It:

• Defined clear data owner roles
• Implemented secure access controls
• Created a metadata catalogue

This led to faster, data-driven decisions and better customer insights.

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Example: optimising inventory in a coffee chain

‍A coffee chain faced supply waste due to uncoordinated orders. It:

• Implemented governance to monitor sales trends
• Used real-time analytics to align stock with demand
• Automated orders using predictive analytics

The result was reduced waste, lower costs, and improved efficiency.

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Conclusion

Data governance can emerge as a reaction to crises or as a proactive initiative. While reactive governance fixes problems, proactive governance prevents risks and turns data into strategic value.

Companies investing in preventive and strategic governance stay ahead of risks and unlock the full potential of their data, achieving competitive advantage, efficiency, and better business decisions.

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