EDITOR ’ S QUESTION
and resources required to maintain AI systems . This issue is heightened in organisations that still rely on legacy systems and manual data processes .
APAC ’ s regulatory landscape is also ever evolving , with stricter data privacy laws and AI guardrails emerging in markets such as Australia and Singapore . AI systems that are not built on transparent and well-governed data could become vulnerable to compliance risks and legal penalties .
Ultimately , a robust data strategy should precede any AI strategy . Without a clear data governance framework and a strong foundation for reliable data , APAC organisations risk investing in AI technologies that fall short of their potential . The priority should be about getting data AI-ready .
How do organizations get their data AI-ready ?
Preparing your data for AI is foundational for any organisation in APAC aiming to leverage advanced analytics and machine learning effectively . Here are some practical strategies to transition your data into AI-readiness :
• Evaluate and cleanse your data : Start by conducting a comprehensive evaluation of existing datasets to identify and correct inconsistencies , missing values , duplicates and inaccuracies . Clean , quality data is essential for the success of AI models .
• Build a robust data governance framework : Establish a strong data governance framework
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