Data Governance For AI And Digital Transformation

Data Governance for AI and Digital Transformation is becoming essential as businesses across Asia-Pacific accelerate digital initiatives. However, many companies still struggle with outdated and fragmented data governance strategies. In an interview with iTNews Asia, Dhiraj Goklani, Splunk’s Area Vice President for South Asia, explains why gaps in data maturity threaten the success of AI and Digital Transformation efforts.

Although 64% of organizations now manage over 1 petabyte of data, most rely on reactive data management. They address issues only after failures occur. This approach limits the value of data and slows innovation. In addition, fragmented infrastructures, siloed systems, and inconsistent standards make it difficult to use Data Governance for AI and Digital Transformation effectively. As a result, real-time insights suffer. Delayed or incomplete data prevents companies from responding quickly to change.

Several key challenges stand out:

• Only two-thirds of APAC firms have formal data strategies in place.

• 40% of data leaders cite data quality and trust as major barriers to progress.

• Nearly half of companies still move data manually each month, which increases security and compliance risks.

Moreover, the problem extends beyond technology. Governance is often viewed only as an IT or compliance responsibility. Consequently, it lacks business ownership and connection to strategic goals. Goklani emphasizes that Data Governance for AI and Digital Transformation must become a shared priority across the business. Clear ownership, cross-functional collaboration, and measurable outcomes are essential. For example, strong governance can improve incident response times, reduce downtime, and increase decision velocity.

Clean, reliable data is critical for AI success. Without well-governed data pipelines, AI models can produce flawed or harmful outputs. Therefore, companies with mature governance frameworks report higher confidence in their AI capabilities. In contrast, firms with poor observability struggle to operationalize AI and Digital Transformation effectively.

As AI adoption increases, Goklani recommends integrated governance frameworks that unify data, AI, security, and compliance. Furthermore, these frameworks should include real-time observability and proactive risk management. In an era where AI and data are inseparable, building a strong foundation of Data Governance for AI and Digital Transformation is no longer optional. It is now a competitive necessity for APAC enterprises.

Source: 

https://www.itnews.asia/news/strategies-for-companies-to-overcome-struggles-in-data-management-617960