What Do Lewis Hamilton and Your Chief Data Officer Have in Common?

Daniel Rolles
Nov 4
6 min read

By Daniel Rolles, CEO & Founder, BearingNode

Spoiler alert: They both rely on telemetry to prevent disasters. The difference? Only one of them looks good doing it.

Picture this: You're sat in a Monaco café, watching Lewis Hamilton scream past at 200mph in a £15 million carbon-fibre masterpiece, while simultaneously reviewing your organisation's data governance strategy.

Here's the uncomfortable truth: That F1 car costs about the same as what most organisations claim their data estate is worth.

Except the F1 car gets 200 sensors monitoring every parameter in real-time, predictive analytics preventing failures before they happen, and a team of engineers obsessively optimising performance.

Your multi-billion-dollar data estate? It gets a quarterly audit and a PowerPoint presentation titled "Q3 Data Quality Metrics Review."

The contrast is... well, let's call it embarrassing.

On one side: champagne sprays, global TV audiences of 400 million, and technology so advanced it makes NASA jealous.

On the other side: grey spreadsheets, dashboards nobody trusts, and data lineage documentation that's "scheduled for next quarter."

But here's the thing that'll blow your mind...

The Ridiculous Truth About F1 and Data Observability

Both industries are literally obsessed with the exact same things:

Real-time monitoring
Predictive analytics
Performance optimisation
Preventing catastrophic failures
Knowing exactly what's happening, when it's happening

The only difference? One makes it look sexy, the other makes it look like... well, like a data governance committee meeting.

F1 Telemetry vs Data Observability: A Painfully Accurate Comparison

Look, we know what you're thinking: "Are these consultants seriously comparing Monaco Grand Prix glamour to my data platform?" Yes. Yes, we are. And we're not sorry.

The £15 Million Question

F1 teams spend £15 million on a car, then spend millions MORE on telemetry to gain mere milliseconds of competitive advantage. They monitor everything from tyre temperature to driver heart rate, all in real-time, all to prevent disaster and win races.

Meanwhile, organisations with data estates worth billions spend their budget on... storage? More Snowflake credits? Another data catalogue nobody uses?

What if we applied F1-level sophistication to data observability?

Imagine if your data lineage was as precise as Mercedes' pit stop strategy. Picture data quality monitoring that's as real-time as Hamilton's steering wheel display. Think about anomaly detection that's as predictive as Red Bull's race simulations.

Sounds impossible?

That's what they said about a bloke called Lewis winning seven world championships.

When Data Management Meets F1-Grade Intelligence

Here's where it gets interesting (and slightly less ridiculous). Both F1 and data management are actually dealing with mission-critical situations:

F1: Telemetry prevents crashes that could kill drivers
Data Platforms: Observability prevents failures that destroy business decisions, regulatory compliance, and customer trust

The stakes are equally massive. The sophistication gap? Well, that's where things get embarrassing for data teams.

What F1 Does Right (That Data Teams Get Wrong)

1. Real-Time Everything

F1 cars transmit 100GB of data per race. Your data platform probably transmits 1.5GB of lineage metadata per quarter. And most of it's in a Confluence page nobody's updated since 2022.

2. Predictive, Not Reactive

F1 teams predict tyre degradation 20 laps ahead. Your data quality alerts spot problems 20 days too late—usually when a C-suite executive asks why the numbers are wrong.

3. Continuous Optimisation

Every F1 practice session improves performance. Your data pipelines get reviewed annually (if someone remembers to schedule the meeting).

4. Obsessive Measurement

F1 measures everything that matters. Data teams measure everything that's easy to measure (which usually means counting tables and calling it "data governance").

Data Observability: The Three Lines of Defence... F1 Style

Let's reimagine the traditional approach to data management with some F1 flair:

First Line: The Engineers (Data Producers)

Traditional View: Teams building pipelines and hoping they work
F1 Version: Engineers with 300 sensors providing instant feedback on every component
What This Really Means: Your data engineers should have real-time visibility into pipeline health, data quality, and lineage—not error logs they check when things break

Second Line: The Strategists (Data Governance)

Traditional View: Data governance creating policies nobody reads
F1 Version: Strategists with live data feeds making split-second calls on tyre strategy, fuel load, and race position
What This Really Means: Data governance should be strategic partners with real-time insights, not policy police with quarterly compliance checklists

Third Line: The Race Directors (Data Leadership)

Traditional View: CDOs presenting "data as an asset" slides to the board
F1 Version: Race control with comprehensive telemetry ensuring fair play and optimal performance
What This Really Means: Data leaders should have executive dashboards showing actual data health, not vanity metrics about "data maturity scores"

The Data Telemetry Revolution

Here's where we stop being silly and start being serious (but only slightly).

Modern F1 cars generate telemetry data that tracks:

300+ parameters per second
Predictive maintenance alerts before components fail
Performance optimisation recommendations in real-time
Instant anomaly detection across all systems
Complete lineage from fuel tank to finish line

Your data platform probably tracks:

Number of tables created last quarter
Percentage of datasets with "proper documentation" (whatever that means)
Data quality rule compliance rates
That one metric the CDO likes to show the board

What if your data had F1-grade observability?

Imagine knowing:

Which pipelines are degrading in performance (before they fail at 3am)
When data quality is dropping (before it reaches your ML models)
Where sensitive data is flowing (before regulators come knocking)
How your data is actually being used (not how you documented it should be used)
The complete lineage of every critical business metric (in seconds, not days)

Making Data Observability Sexy (We Can't Believe We Wrote That)

Look, we're not suggesting your next data governance meeting needs grid girls and champagne (though it couldn't hurt). But we are suggesting that your data observability infrastructure could be as sophisticated as McLaren's telemetry platform.

Because here's the uncomfortable truth: Your data is moving at F1 speed, but your observability is stuck in the pit lane.

The BearingNode Difference: F1-Grade Data Intelligence

We've spent years building what we call the Data/Information Observability (D/I O11y) Framework - basically, F1-level telemetry for your data estate.

It includes:

Discover: Real-time asset discovery (not quarterly data inventories that are outdated before publication)
Track: Continuous lineage monitoring (not static diagrams in SharePoint)
Comply: Automated privacy and compliance controls (not manual attestations and spreadsheets)
Govern: Intelligent policy enforcement (not policy documents nobody reads)
Value: Performance analytics showing actual data ROI (not "data is an asset" platitudes)

Think of it as putting 200 sensors on every piece of data in your organisation. Suddenly, your Chief Data Officer might start looking as cool as Toto Wolff.

(Okay, maybe not quite. But definitely cooler than spreadsheets.)

The Checkered Flag

So yes, we've spent 1,500 words comparing champagne-soaked racing drivers to your data platform architecture. And yes, we're completely serious about it.

Because in a world where data moves at digital speed, your observability can't afford to move at quarterly-report speed.

Here's the real kicker: Most organisations have data estates worth billions. F1 cars cost £15 million. Yet the F1 car gets better monitoring, better analytics, and better real-time intelligence than your entire data platform.

Let that sink in for a moment.

The question isn't whether you can afford F1-grade data observability.

The question is whether you can afford NOT to have it.

When Lewis Hamilton is making split-second decisions at 200mph, he has more real-time data about his car than most CDOs have about their billion-dollar data estates.

That's not a technology problem. That's a priority problem.

Ready to upgrade from quarterly reviews to real-time intelligence? Let's talk about bringing some F1 sophistication to your data observability.

Watch: F1-Grade Data Intelligence in Action

About the Author

Daniel Rolles is the CEO and Founder of BearingNode, where he leads the firm's mission to help organisations unlock the commercial value of their data whilst enhancing their risk management capabilities.

As CEO, Daniel drives BearingNode's strategic vision and thought leadership in data transformation, analytics strategy, and the evolving regulatory landscape. He regularly shares insights through industry publications and speaking engagements, focusing on practical approaches to data governance, AI implementation, and performance transformation in regulated environments. He is one of the key authors of BearingNode's Data and Information Observability Framework.

With over 30 years of experience in Data, Analytics and AI, Daniel has successfully built and led D&A teams across multiple industries including Financial Services (investment, commercial and retail banking, investment management and insurance), Healthcare, and Real Estate. His expertise spans consulting, commercial leadership, and delivery management, with a particular focus on data governance and regulatory compliance.

Daniel holds a Bachelor of Economics (University of Sydney), Masters of Science (Birkbeck College, University of London), and Executive MBA (London Business School).

Based in London, Daniel is passionate about financial inclusion and social impact. He serves as a Trustee for Crosslight Advice, a debt advisory and financial literacy charity based in West London that provides vital support to individuals facing financial vulnerability.

About BearingNode

BearingNode simplifies the complexity of decision-making through data, analytics, and AI. Our Data & Information Observability framework helps organisations discover, track, comply, govern, and create value from their data assets. We specialise in building F1-grade data intelligence platforms that deliver both regulatory compliance and competitive advantage.