AVGEEK
Using aviation data for routes, operations and sustainability
Cirium’s Mike Malik discusses how on-time performance data and analytics inform airline and airport planning, including regional connectivity, legacy IT constraints and sustainability measurement.
The conversation
Episode number 47 of The Indian AvGeek, Vishal sits down with Mike Malik—veteran airline sales leader turned marketing chief at Cirium, to trace a remarkable journey through aviation. From sales at American Airlines and leading Aloha Air Cargo to co-founding Maxjet, Mike shares stories of entrepreneurial grit and strategic pivots that shaped his career.
We then dive into Cirium’s mission: how this data powerhouse powers on-time performance reports, underpins airport and airline planning, and supports emerging markets like India with analytics on regional connectivity, network optimization, and sustainability. Along the way, Mike reflects on rebranding FlightGlobal into a unified Cirium, the challenges of old IT systems (remember only 26 fare buckets?), and the future of aviation tech—from AI assistants baked into operations tools to new products tailor-made for Asia-Pacific growth.
Key topics covered include:
• Mike’s early days in airline sales and the genesis of Maxjet’s low-fare, all-business-class model • Cirium’s customers, core offerings, and impact of its On-Time Performance rankings • The role of data and analytics in optimizing routes, regional connectivity, and profitability in India • Challenges of digital transformation in airlines and how to overcome legacy IT constraints • Leveraging data for sustainability, environmental tracking, and new green aviation solutions • Insights on Asia-Pacific expansion, Cirium’s brand vision, and products beyond Emerald Sky • Advice for young professionals entering aviation analytics or airline management • Leadership lessons in driving innovation, transformation, and buy-in across people and processes
Whether you’re an aviation insider, data geek, or simply curious about how numbers shape every takeoff and landing, this episode delivers fresh perspectives on the present and future of global and Indian aviation.
Vishal Mehra - @vishal1mehra
Key Takeaways
- Data becomes valuable at the point of decision, not ingestion.
- The same flight-data layer serves airlines, airports, lessors, financiers and passenger applications, but those users have different incentives.
- Maxjet illustrates the risk of coupling a differentiated premium proposition to a narrow demand pool and heavy aircraft economics.
- Airport and airline performance are interdependent; a route schedule is only as reliable as its operating environment.
- Emissions comparisons require an explicit denominator and an aircraft/engine/route methodology; estimates are not direct per-flight fuel readings.
- An AI assistant over a bounded performance dataset is a stronger testable use case than open-ended transformation rhetoric.
- Aviation analysts need operational empathy as well as technical competence; Malik uses his atypical airline responsibilities to make that point.
Additional subjects
Editorial analysis
Aviation data is useful only when it changes decisions
Mike Malik has worked across airlines, distribution technology, cargo and analytics. That career makes the distinction at the centre of this conversation unusually clear: collecting aviation information is not the same as using it. Malik argues that the competitive gain comes when an airline, airport, lessor or other decision-maker can turn schedule, aircraft, performance and emissions data into action quickly. Cirium, in his account, supplies a largely invisible layer behind public-facing travel information and institutional decisions. That is the argument worth preserving, rather than the promotional claim that data by itself will transform aviation.
Maxjet and the economics of a narrow proposition
His account of co-founding Maxjet is a useful counterweight to technology optimism. The business-class-only Boeing 767 proposition delivered a distinct product and initially attracted customers, but the airline was vulnerable to fuel-price shocks, the economic downturn and the difficulty of filling a high-yield-only cabin consistently. Malik cites the 2008 financial crisis and fuel as causes of failure; the chronology and causal weight should be treated as his recollection rather than a complete independent history of the airline. The episode therefore connects elegant market segmentation with the unforgiving fixed-cost economics of aircraft.
What emissions estimates do—and do not—measure
The most technically substantial material concerns Cirium’s EmeraldSky flight-emissions work. Malik distinguishes measuring flight emissions from solving aviation sustainability. He describes incorporating actual flight time, taxiing, tail-specific fleet and engine characteristics, and cabin space instead of imposing a distance-only proxy. A common methodology can make estimates more comparable; it cannot convert a modelled estimate into direct measurement of the fuel burnt by every individual flight.
The realistic promise of AI for aviation analytics
The discussion of AI is more grounded when the example is specific: Malik cites an assistant over on-time-performance data, capable of investigating the consequence of changing a block-time assumption. That is a constrained analytical task with a meaningful business user, not evidence that generative AI will resolve industry-wide operational constraints. Forecasts for India and specific Cirium market-share figures in the recording are dated supplier claims requiring external checking; the transcript also appears to misstate a forecast year.
Questions & answers
Why did the all-business-class experiment matter?
It tested a differentiated transatlantic value proposition; Malik recalls that getting a full premium cabin consistently was difficult and fuel/economic shocks proved severe.
Who actually uses Cirium’s information?
Malik names airlines, airports, lessors, manufacturers, financial institutions, government and technology providers.
Why not estimate emissions from great-circle distance alone?
Malik says actual flight times, taxi times, aircraft/engine characteristics and cabin allocation produce a more discriminating estimate.
What is the practical AI example?
An assistant querying OTP data to test effects of schedule assumptions, including small changes to block time.
What skills matter for aviation analytics?
Curiosity, adaptability, technical analysis and the ability to understand the human and operational consequences of decisions.
Sources & attribution
Original conversation: The Indian AvGeek — Episode 47. Originally published 10 August 2025.
Historical context: This editorial analysis reports and interprets positions from the original 2025 conversation. Estimates, forecasts, commercial relationships and policy statements must not be mistaken for current conditions or independent verification as of October 2026.
Editorial Q&A: Questions and answers are explanatory editorial paraphrases, not verbatim dialogue.