Endy Lambey, Group Head of Enterprise Data Management and a 25-year veteran of data transformation, talks to The Ortus Club about the high-stakes world of telco mergers and cloud migrations. Having led some of Malaysia and Indonesia’s largest data warehouse integrations, Endy shares a “war-room” perspective on why data quality is the ultimate safeguard against AI hallucinations. He argues that even for the most seasoned technical leaders, the most valuable growth comes from peer-level exchange, stepping outside the engineering silo to refine the business translation skills that secure board-level confidence.
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Executive Summary: Key Takeaways
- The Merger DNA: In rapid-growth environments, a “lift and shift, stabilise, transform, and optimise” strategy is essential to keep pace with constant acquisitions and shifts.
- The Hallucination Guardrail: AI readiness is a myth without data integrity. Dumping unmodeled data into a platform leads to AI hallucinations and suspect outputs.
- MVP Velocity: To build executive confidence, leaders must deliver Minimum Viable Products (MVPs), like top-line revenue data, on day one, even while deep-detail teams work in the background.
- Operating Model over Technology: Operating model alignment is the primary friction point in data modernisation. Without a central model, cloud costs skyrocket due to data redundancy and silos.
- The Translator Skill: Data leaders must be able to simplify technical complexity for CEOs and business strategy for engineers, adapting their language to the specific audience.
With over a quarter-century of experience, Endy Lambey has spent much of his career in what he calls “war-room” environments. From leading massive bank mergers in Malaysia to orchestrating migration from on-premise to the cloud during a major telco merger, Endy has developed a leadership style defined by velocity and creative, out-of-the-box thinking. He is a successful architect who understands that data isn’t just a technical asset; it’s a business deliverable. He believes that to stay relevant, leaders must move past the technology hype and return to the fundamentals of data modeling, all while actively seeking the high-level dialogue that helps translate technical “war stories” into board-level strategy.
Why is merger DNA essential for modern data transformation?
Endy reflects on the high-pressure integrations that taught him to prioritise creative problem-solving over standard templates.
“There were a few defining moments, but leading a technical team for a large bank in Malaysia during a merger stands out. We finished one integration, only to be acquired again. That experience taught me creative, out-of-the-box thinking to integrate data within tight timeframes. At XL, we had to adopt a ‘lift and shift, stabilise, transform, and optimise’ strategy to keep pace with the business.
The mindset has to be: know your team. You are going to a war where there is no fallback; you must move forward and win. You deliver the Minimum Viable Product (MVP) first, like revenue and network data, to build executive confidence while your team works on the deeper details. If you dive too deep into the details immediately, you will never deliver at the necessary velocity.”
What do leaders underestimate about the journey to AI Readiness?
Focusing on the fundamentals, Endy warns that a lack of data quality will inevitably lead to unreliable AI outcomes.
“I see many companies focusing entirely on the AI itself while neglecting the data quality behind it. If you don’t take care of the fundamentals, you get AI hallucinations. Enterprises often dump every single piece of data into a big platform and assume the AI can process it. But if you cannot ensure the integrity and quality of that data, the output will always be suspect. The fundamentals have to be correct; otherwise, all the creative work on top of it will not give you the correct result. You need to go back to the fundamentals of data modelling. If you don’t understand your data lineage and have a single business definition, you become a victim of the technology hype.”
Why is the Operating Model the biggest point of friction?
Endy explains how a lack of central alignment leads to expensive data silos and cloud redundancy.
“Operating model alignment is the most important factor. If you agree on the alignment, synergy between business and data follows. Without a central operating model, you get data silos. Every division builds its own system because they have the budget. This becomes extremely costly, especially in the cloud where every query costs money. Governance must be pushed down from the top, but the work and optimisation must come from the bottom. Friction itself isn’t bad. It creates movement but it must be manageable.”
How do you bridge the Strategy-Execution gap as a translator?
Sharing his leadership philosophy, Endy discusses the art of adapting technical language for different enterprise stakeholders.
“It is like being a translator. With my team, I am technical and result-oriented. With executives, I talk about business outputs and SLAs. I try to follow the principle of explaining things as if the audience is five years old. When I talk business to a technical team, they are the ‘five-year-olds’. When I talk technical details to a CEO, they are the ‘five-year-olds’. You must adapt your language to the point of view of your audience to be effective. This is how you bridge the gap between high-level strategy and technical execution on the ground.”
What hard question should every executive ask their data team today?
In a final challenge to leadership, Endy encourages a move away from the hype toward real-world use cases.
“They should ask: ‘How can AI, using the data we actually have, help us execute our business?’ Many buy into the hype without a real use case. They want data delivered the second it happens, but when you drill down, they often don’t need that speed. Leaders need to figure out how to use AI and data to optimise strategy. This requires staying curious and engaging in industry discussions. No matter how senior you are, you must understand your data lineage and have a single definition for success across the board.”
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Across Endy’s insights on “merger DNA,” data modeling, and the translator skill, one pattern is clear: these challenges aren’t solved in isolation. They require a peer-level perspective and the kind of high-trust dialogue that transcends the engineering silo.
His vision of the data leader as a “Translator” reflects a broader reality: today’s enterprise data heads cannot rely on internal technical teams alone. The most effective executives, especially those navigating complex mergers and cloud migrations, actively seek out peer dialogue as a strategic necessity to refine their business language and strategic foresight.
At The Ortus Club, we host curated executive roundtables that bring together senior leaders facing these exact challenges. Step away from generic digital transformation talk and engage in the kind of open, high-value conversations that return the focus to data fundamentals and business outcomes.
Frequently Asked Questions
Q: What is “Lift and Shift, Stabilise, Transform, and Optimise”?
A: It is a phased cloud migration strategy. You first move data as-is, ensure it runs reliably, update the architecture for cloud-native features, and then fine-tune for cost and performance.
Q: Why does Data Quality prevent AI hallucinations?
A: AI models learn from provided data. If the underlying data is inaccurate, duplicated, or lacks context, the AI will generate confident but false answers (hallucinations).
Q: What is a Data Catalog?
A: It is an organised inventory of data assets in the organisation. It uses metadata to help data citizens collect, evaluate, and manage data, ensuring everyone uses the same business definitions.
Q: How does Data Lineage improve governance?
A: Data lineage tracks the path data takes from its origin to its destination, including transformations. This provides transparency and trust in the accuracy of the final report.
Q: Why is the “Translator” role critical for CIOs?
A: Because boards care about business outcomes while engineers care about technical specs. A leader must bridge this gap to ensure technical work aligns with corporate strategy.
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