We ‘deep dive’ with Russell Willis, Head of Data & Delivery Intelligence, BCS – who tells us about life inside and outside of the office.
What would you describe as your most memorable achievement in the data centre industry?
In 2015, almost by accident, I found myself leading the rollout of multiple hyperscale data centre campuses for global tech giants. By harnessing data-driven reports and automating 80% of routine admin tasks, my teams shifted from chasing spreadsheets to solving problems. The payoff? Projects went live with greater confidence, engineers spent more time innovating and we set a new standard for efficiency.
What made you first think of a career in technology/ data centres?
I didn’t chase a career in data centres – I stumbled into it!
Following success in electrical project management in 2010, I was asked to manage structured cabling and fibre runs. Next thing I knew, a contractor asked me to oversee the electrical project management on a data centre build. After an eight-year detour on global infrastructure programmes, I returned in 2024 as Head of Data & Delivery Intelligence at BCS – proving sometimes the best careers find you, not the other way around.
What style of management philosophy do you employ in your current position?
A key mantra I keep in mind for Management is: “Poor is the Teacher, whose student does not surpass them.”
I believe in:
· Growth-first: I invest in my team so they can outpace my own successes – celebrating every leap beyond my experience.
· Be water leadership: Stress is a river – sometimes calm, sometimes rapid. I adapt with empathy and compassion: flow around obstacles or crash through them when needed.
· Open communication: Weekly check-ins and live reports create zero surprises – only shared victories.
· Balance as a superpower: I champion work/life harmony because well-rested minds innovate faster.
What do you think is the current hot talking point within the data centre space?
· Liquid Cooling: From direct-to-chip to full immersion, we’re engineering racks and floors strong enough to handle litres of coolant at scale.
· Chip Architecture: As Moore’s Law bends, every watt saved at the processor cuts downstream cooling costs – making next-gen chips our best sustainability tool.
· Quantum Cooling Tease: Immersive setups need new warranties and materials—exciting hurdles that push us forward.
How do you deal with stress and unwind outside the office?
I don’t see stress as an enemy – it’s a river with calm pools and roaring rapids. By being water – formless, adaptable, unstoppable – I navigate every challenge with focus and flow. My response to the river can either aid or inhibit my navigation of it, should I choose to fight the current, or be distracted by the white water.
To further the water related analogies, I am a great fan of some of Bruce Lee’s philosophies, one the most famous being “Be Water.”
“Empty your mind.
Be formless, shapeless, like water.
You put water into a cup; it becomes the cup.
You put water into a bottle; it becomes the bottle.
You put it into a teapot; it becomes the teapot.
Now water can flow, or it can crash.
Be water, my friend.”
– Bruce Lee
What do you currently identify as the major areas of investment in your industry?
· 200 kW/Rack Density: We’re designing for it now – because in five years, anything less will feel underpowered.
· Micro-Local AI: Small-scale AI solutions that live at the edge, keep data sovereign, and avoid GIGO pitfalls.
· Rock-Solid Data Foundations: No flashy LLM is meaningful without clean, structured inputs.
All three items are inextricably linked and major areas of investment to support the exploding requirement for data centres, not only to facilitate the growing Cloud, but more significantly the rapidly growing AI market, for which every Generative Model, LLM or other AI requirement for compute, huge amounts of rack space is required.
We foresee Rack density exceeding 200kW per rack within the next two to five years and AI will be a huge area of investment, not only to facilitate the use of AI to make tasks more efficient, but to do so safely, whilst avoiding the fallibilities that are present in many current AI platforms and solutions. Micro Local AI will also likely become a major area of investment, either developing smaller solutions from scratch, or developing a solution that will safely utilise the power of another existing platform, whilst ring fencing sensitive data.
What are the region-specific challenges you encounter in your role?
The list is full and varied as every region is different. However, it generally includes; local building regulations, local contract forms, power availability, planning consents and public perception.
In addition, data sovereignty, where any entity cannot allow their data to leave the constraints of their country, which then has a direct effect on the data centres that can be used and the redundancy protocols (ensuring that copied data is not sent out of area) is a challenge. The same applies to Data Protection Regulations (GDPR or equivalent) where the categories of data must be considered and no protected category data used in contravention of the requisite regulations.
At BCS we have experienced and navigated all of these region-specific challenges in many countries, helping many of our international clients navigate the transition from building a data centre in one country, to another.
What changes to your job role have you seen in the last year and how do you see these developing in the coming months?
The biggest change I have seen in the last year, by far, is the arrival of AI.
This has been accelerating rapidly, mostly in the form of Gen AI and LLMs, as organisations race to jump on the AI train.
Whilst I support and applaud digital transformations, which AI accelerates, there are some significant fallibilities that should not be ignored in the fervent race to board this train. All users should understand that when using default interfaces, the data entered will be held by the learning model, to improve its base of data. This can lead to unintended invalidation of confidentiality agreements, or otherwise lost control of sensitive data, or IP.
The quality of any AI output is wholly dependent upon the quality of the data that they are trained on and have access to. If data foundations are not sound, one cannot expect even the best AI systems in the world to produce accurate and meaningful outputs.
The Mantra “Garbage in, garbage out” (GIGO), succinctly captures this.
I see the use of AI developed by the leading users, to safely configure applications, or develop their own AI models, to protect against inadvertent loss of data.
