S5 | E6 | Business Process; Data Management’s Moment to Shine with Aldwin Tito, Chief Data Officer at Shell
Jan 7, 2025
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Aldwin Tito, Chief Data Officer at Shell, shares insights on the significance of data management for safe, eco-friendly operations. He emphasizes that effective management doesn't always require expensive tools, highlighting the value of a strong foundation. The discussion dives into navigating tech hype cycles, the transformative impact of Generative AI compared to past technologies, and the evolving role of CDOs in a data-driven world. Tito encourages a close alignment of data processes with business operations and the importance of promoting data savvy within organizations.
Effective data management can thrive without expensive tools by focusing on mastering data processes and local accountability.
AI holds transformative potential for data management, necessitating a balance between innovation, data quality, and governance for successful integration.
The future of data leadership lies in aligning AI strategies with business processes, ensuring that data governance underpins AI initiatives for operational effectiveness.
Deep dives
Aldwin Tito's Journey in Data Management
Aldwin Tito shares his extensive experience within the data management field, having spent over 11 years at Shell, including six years as Chief Data Officer. His journey began in IT in 1994, gradually shifting focus towards data in the early 2000s after witnessing the inefficiencies and high costs associated with data warehousing and business intelligence projects. His transition from technology to process-oriented roles highlights the importance of understanding business processes in achieving effective data management. Aldwin emphasizes the necessity of investing time and effort in mastering data processes, arguing that sound data management doesn't always require expensive tools.
The Role of Data in Asset Management
Aldwin defines the central role of the Chief Data Office in Asset Management at Shell, which supports numerous global assets involved in diverse operations like oil extraction and chemical production. Each of Shell's 38 assets possesses unique challenges and histories, necessitating a tailored approach to data management rather than a one-size-fits-all solution. The primary focus is on utilizing data to enhance the safety and environmental sustainability of operations, revealing the intersection between data strategy and real-world impact. This necessity for localized accountability ensures that assets each take responsibility for their own data management practices.
AI's Evolution and Its Implications
In discussing artificial intelligence (AI), Aldwin observes that while hype cycles often bring challenges, they also stimulate innovation. He recognizes that AI holds significant potential for driving efficiencies in various processes but warns against the dangers of relying on faulty data quality. The critical issue lies in the middle ground of data quality, where assumptions are made about accuracy without verification, leading to potential operational failures. Aldwin argues for a cautious approach, balancing AI's promise with the realities of data governance, stressing the need for a rich understanding of both technology and processes to leverage AI effectively.
Data Management's New Paradigm
Aldwin proposes that the advent of generative AI will fundamentally transform data management roles, shifting focus from traditional quality checks to proactive quality control through AI's capabilities. He envisions a future where AI can actively monitor and rectify data input errors during processes, enhancing overall productivity and decision-making. However, he cautions that as AI becomes more integrated into processes, the complexity of data governance will also increase, requiring organizations to mature their data management practices. The need for data lineage and accountability in AI-generated outputs will grow, necessitating a joint effort from both business and data professionals to ensure effectiveness.
Navigating the Ownership of AI and Data
Aldwin discusses the evolving landscape surrounding data and AI leadership roles, emphasizing that successful management should ideally be closely aligned with business processes. While current trends suggest a desire to own AI initiatives, he stresses that data governance must remain central to ensure quality and relevance. As organizations explore the balance between technology and process, there's a risk of delegating too much AI ownership to IT without sufficient understanding of operational needs. Ultimately, Aldwin believes that effective AI strategies require business-led ownership, fostering collaboration between data and process management to unlock AI's true potential.
In Episode 6, of Season 5 of Driven by Data: The Podcast. Kyle Winterbottom is joined by Aldwin Tito, Chief Data Officer at Shell where they talk about Business Processes and Data Management, in which they discuss:
How data management can be effective without relying on expensive tools.
The critical role of asset management within Shell.
How navigating hype cycles helps you get to where you are today.
Taking control of what you have today ensures a reliable foundation for the future.
How does the GenAI era compare to past hypes like ERP systems?
Unlocking data-savvy thinking and why it’s crucial.
How tools like ChatGPT are becoming more accessible in everyday contexts.
How AI enables the creation of decision models based on past data.
How GenAI approaches data management challenges.
Building yourself up by taking deliberate, well-thought-out steps.
How history tends to repeat itself in pursuing the latest tech trends.
AI is an enabler of technology, rather than just a technological advancement.
How business analysis helps teams understand the impact of change.
Balancing risks and potential: reaching the maturity required to apply AI for tangible benefits.
Exploring the three key states of data quality.
Placing ownership close to the point of data generation can drive better outcomes.
Aligning data management processes closely with business operations.
How to determine the rightful owner of the GenAI strategy.
Speculating on the potential obsolescence of the Chief Data Officer role in the future.
Thanks to our sponsor, Data Literacy Academy.
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At Data Literacy Academy, we don't just teach data skills. We empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. We're bridging the gap between business and data teams, so they can all work towards aligned outcomes.
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