filmov
tv
Data Governance and AI
Показать описание
Explore the dynamic intersection of data governance and AI with Tiankai Feng, Data Strategy and Data Governance Lead at Thoughtworks Europe. In this insightful interview, Tiankai discusses the essentials of trustworthy data, the critical role of AI oversight, and the importance of a human-centric approach to data governance.
© 2024, NinjaCat
● Introducing Tiankai Feng and the Importance of Data Governance (0:00-5:00)
This chapter introduces Tiankai Feng as the data strategy and data governance lead at ThoughtWorks Europe.
It highlights his passion for the human side of data and how he works with clients to improve communication, collaboration, and creativity in their data practices.
The chapter emphasizes the importance of data governance in making data trustworthy.
Tiankai defines data governance as a practice that ensures data is created, processed, and used in a meaningful and secure way.
He stresses the subjective nature of trustworthiness and the need for organizations to agree on what constitutes trustworthy data.
● Data Strategy and its Relationship to Data Governance (5:00-10:00)
Tiankai defines data strategy as the process of linking data efforts in terms of people, processes, and technology to the overall business strategy.
He emphasizes that data governance is a crucial component of a comprehensive data strategy.
The chapter also touches upon other elements of data strategy, such as data culture, platform strategy, and process management.
● The Role of Data Governance in AI and AI Governance (10:00-15:00)
Tiankai highlights the dependence of AI on high-quality data, emphasizing the "garbage in, garbage out" principle.
He argues that effective data governance is essential for ensuring the quality of data used to train AI models, thus preventing the amplification of existing errors.
The chapter introduces the concept of AI governance, which addresses the need for human oversight and evaluation of AI models.
Tiankai stresses the importance of understanding AI models' decision-making processes and the risks associated with their deployment.
● Exploring the Causes of Generative AI Failures (15:00-20:00)
Tiankai attributes these failures to the inability of AI systems to anticipate and account for all possible scenarios, leading to unexpected and undesirable outcomes.
He connects this lack of foresight to the principles of data governance, arguing that a structured approach to risk assessment and mitigation is crucial for preventing AI failures.
The chapter also touches upon the philosophical limitations of AI, highlighting its inability to grasp human concepts of truth, right, and wrong.
Tiankai emphasizes the absence of a moral compass in AI, which prevents it from intuitively making ethical decisions.
● The Human Element in Data Governance: Intrinsic Motivation and Championing the Cause (20:00-25:00)
Tiankai argues that the often negative perception of data governance stems from its perceived restrictive nature.
He advocates for reframing data governance as an empowering and enabling force that enhances data quality, facilitates compliance, and ultimately promotes greater data utilization.
The chapter stresses the importance of motivating individuals intrinsically to embrace data governance principles.
Tiankai suggests framing the value of data governance in terms of personal benefits, such as reduced manual workload and increased efficiency.
He also highlights the need for champions, particularly from business functions, to advocate for data governance and its importance.
● Data Governance Success Stories and the Importance of Collaboration (25:00-30:00)
Tiankai shares a case study where data governance helped bridge the gap between product managers and sales teams in a product company.
He illustrates how aligning data structures and processes between these teams led to significant efficiency gains and eliminated redundant manual work.
The chapter reinforces the importance of collaboration and communication in data governance, emphasizing the need to bring together stakeholders with diverse perspectives.
● Overcoming Challenges and the Future of Data Governance: Starting Small, Thinking Big (30:00-35:00)
Tiankai addresses the common tendency to approach data governance with an overly broad scope, leading to analysis paralysis and inaction.
He advocates for a more pragmatic approach, recommending starting small with specific use cases and gradually expanding the scope over time.
This iterative approach, according to Tiankai, allows for demonstrating tangible impact and building momentum, ultimately converting skeptics into believers in data governance
© 2024, NinjaCat
● Introducing Tiankai Feng and the Importance of Data Governance (0:00-5:00)
This chapter introduces Tiankai Feng as the data strategy and data governance lead at ThoughtWorks Europe.
It highlights his passion for the human side of data and how he works with clients to improve communication, collaboration, and creativity in their data practices.
The chapter emphasizes the importance of data governance in making data trustworthy.
Tiankai defines data governance as a practice that ensures data is created, processed, and used in a meaningful and secure way.
He stresses the subjective nature of trustworthiness and the need for organizations to agree on what constitutes trustworthy data.
● Data Strategy and its Relationship to Data Governance (5:00-10:00)
Tiankai defines data strategy as the process of linking data efforts in terms of people, processes, and technology to the overall business strategy.
He emphasizes that data governance is a crucial component of a comprehensive data strategy.
The chapter also touches upon other elements of data strategy, such as data culture, platform strategy, and process management.
● The Role of Data Governance in AI and AI Governance (10:00-15:00)
Tiankai highlights the dependence of AI on high-quality data, emphasizing the "garbage in, garbage out" principle.
He argues that effective data governance is essential for ensuring the quality of data used to train AI models, thus preventing the amplification of existing errors.
The chapter introduces the concept of AI governance, which addresses the need for human oversight and evaluation of AI models.
Tiankai stresses the importance of understanding AI models' decision-making processes and the risks associated with their deployment.
● Exploring the Causes of Generative AI Failures (15:00-20:00)
Tiankai attributes these failures to the inability of AI systems to anticipate and account for all possible scenarios, leading to unexpected and undesirable outcomes.
He connects this lack of foresight to the principles of data governance, arguing that a structured approach to risk assessment and mitigation is crucial for preventing AI failures.
The chapter also touches upon the philosophical limitations of AI, highlighting its inability to grasp human concepts of truth, right, and wrong.
Tiankai emphasizes the absence of a moral compass in AI, which prevents it from intuitively making ethical decisions.
● The Human Element in Data Governance: Intrinsic Motivation and Championing the Cause (20:00-25:00)
Tiankai argues that the often negative perception of data governance stems from its perceived restrictive nature.
He advocates for reframing data governance as an empowering and enabling force that enhances data quality, facilitates compliance, and ultimately promotes greater data utilization.
The chapter stresses the importance of motivating individuals intrinsically to embrace data governance principles.
Tiankai suggests framing the value of data governance in terms of personal benefits, such as reduced manual workload and increased efficiency.
He also highlights the need for champions, particularly from business functions, to advocate for data governance and its importance.
● Data Governance Success Stories and the Importance of Collaboration (25:00-30:00)
Tiankai shares a case study where data governance helped bridge the gap between product managers and sales teams in a product company.
He illustrates how aligning data structures and processes between these teams led to significant efficiency gains and eliminated redundant manual work.
The chapter reinforces the importance of collaboration and communication in data governance, emphasizing the need to bring together stakeholders with diverse perspectives.
● Overcoming Challenges and the Future of Data Governance: Starting Small, Thinking Big (30:00-35:00)
Tiankai addresses the common tendency to approach data governance with an overly broad scope, leading to analysis paralysis and inaction.
He advocates for a more pragmatic approach, recommending starting small with specific use cases and gradually expanding the scope over time.
This iterative approach, according to Tiankai, allows for demonstrating tangible impact and building momentum, ultimately converting skeptics into believers in data governance
0:45:31
0:05:22
0:09:11
0:04:16
0:43:35
1:01:01
0:11:36
0:31:18
0:50:30
0:01:33
0:38:49
0:02:59
0:29:43
0:07:47
0:31:11
0:04:51
0:19:55
0:03:27
0:17:24
0:06:34
0:00:57
0:34:23
0:07:41
0:26:51