About the Webinar
This presentation challenges the common assumption that data quality is a problem to be solved with tools alone. It argues that successful data quality is not a project, but a continuous program that must be engineered by design, ensuring trustworthy data across the enterprise. This approach is more critical than ever in the age of AI, where the principle of “Garbage In, Garbage Out” is a fundamental reality.
To address these challenges, organizations need to move beyond reactive, fire-fighting solutions and adopt a proactive, programmatic approach rooted in systems thinking and the Deming Cycle (Plan-Do-Check-Act). The presentation will clarify that data quality challenges come in two forms:
- Practice-related: Issues that are closer to the user and their processes.
- Structure-related: Imperfections in how data is arranged and stored, which require architectural solutions.
Through real-world case studies, you will learn how to make the business case for data quality by focusing on tangible ROI and reframing technical conversations around business outcomes. You will be able to demonstrate how a dedicated, skilled team can develop the repeatable capabilities necessary to ensure reliable data inputs, allowing your organization to confidently deploy AI systems and achieve strategic success.
Program learning objectives:
- Help you understand foundational data quality concepts for improving data quality at your organization
- Demonstrate how chronic business challenges for organizations are often rooted in poor data quality
- Shared case studies illustrating the hallmarks and benefits of data quality success
About the Speakers
Peter Aiken, Ph.D. is an acknowledged Data Management authority, an associate professor at Virginia Commonwealth University, president of DAMA International, and associate director of the MIT International Society of Chief Data Officers. For more than 40 years, Peter has learned from working with hundreds of Data Management practices in more than 30 countries. Among his 13 books are the first on making the case for data leadership (CDOs), the first focusing on data monetization and modern strategic data thinking, and the first to objectively specify what it means to be data-literate. International recognition has resulted from these and a (pre-Covid-19) intensive worldwide events schedule. Peter also hosts the longest-running Data Management webinar series on dataversity.net. Before Google, before data was big, and before Data Science, Peter founded several organizations that have helped more than 200 businesses leverage data – specific savings have been measured at more than $1.5 billion. His latest venture is Anything Awesome.
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