Data Quality: A Structured Approach

Implementing a structured data quality framework involves assessing, profiling, cleansing, standardizing, validating, and governing data. This approach ensures accurate, consistent, and reliable data, leading to improved decision-making, operational efficiency, and overall business success.

Key Takeaways

• Explain structure of Data Quality
• Defining Data Quality Approach
• Explain key data quality metrics
• Explain how Machine Learning help data quality

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Bio

Melvin Widodo- Data Platform Lead | Endeavour Group| Australia

Melvin is a seasoned data analytics leader with over two decades of experience in delivering results-oriented solutions. His expertise lies in implementing data strategy into actionable insights that address complex business challenges. With a deep understanding of data product, techniques and tools, Melvin has a proven track record of developing and implementing data-driven solutions that drive efficiency, profitability, and customer satisfaction.

His ability to translate data into meaningful actions has made him a trusted advisor to organizations seeking to leverage the power of data. He has a proven track record of successfully delivering insights and recommendations that have led to significant improvements in business outcomes. Melvin’s passion for data and his commitment to delivering value.