development risk falling foul of the classic‘ garbage in, garbage out’ scenario that was a feature of early computer programming and still remains valid to this day.
Secondly, AI and automation themselves play a key role in optimising data management by enabling businesses to implement automated rules around data access, retention and movement in line with compliance and security policies. AI-powered data governance also reduces the risk of manual human errors, and instead ensures consistency and compliance across all data operations.
3. Implement robust data governance and compliance policies
Businesses tend to view compliance in one of three ways. Some consider it as little more than a box-ticking exercise to avoid penalties, others view it as a risk management strategy to protect data and reduce security threats, while some utilise compliance to build a competitive advantage that fosters trust and drives innovation.
But whatever the perspective, regulatory compliance is now non-negotiable. Organisations must maintain structured policies to align with various domestic and international rules, with authorities better armed than ever to impose painful sanctions when breaches occur.
From ensuring proper data documentation and access controls to retention practices, best practice also hinges on continuous monitoring and auditing of stored data. Businesses should also be in a position to categorise data based on value, risk and relevance, ensuring that critical data is stored securely while obsolete data is archived or deleted.
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