Ash Ali

Co-Founder & Growth Hacker

Uhubs

At Uhubs, we empower sales teams to unlock their full potential by transforming skill development into a precise, data-driven unfair advantage. 

By industrialising the monitoring and tracking of essential sales skills, we provide leaders with rapid insights to identify growth opportunities that genuinely drive revenue impact. 

Uhubs streamlines skill development, enabling modern sales teams to achieve measurable success faster and more effectively.

Co-Founder

The Unfair Advantage

The new Book "The Unfair Advantage" uncovers the secret behind why some startups make it and others don’t. Available in Jan 2020- Pre-order book from Amazon, WHSmiths, Foyles & Waterstones.

Wrapping up my experience as the first UK Marketing Director of £3.5 billion company JUST EAT into a practical narrative, I break apart the different types of unfair advantages swimming just under the surface of your favourite success stories. Every single one of them has an unfair advantage within the MILES structure (Money, Intelligence & Insight, Location & Luck, Education & Expertise, Status).  Out on January 9th 2020!

I've done keynotes on "Startup Marketing", "How to find your unfair advantage", "My personal/JUST EAT success story", "The first 3 years in startups" and have been part of several panel debates, pitch judging and discussions.

Author, Keynote Speaker & Co-Founder

Private angel Investments in seed stage startups across the globe. Mentorship and advice to startup entrepreneurs in various sectors and markets with a specific focus on online marketplaces, mobile app's and interesting social & community projects.

If you have startup requiring seed capital, an experienced startup advisor or non-executive role get in touch. 

- Idea assessment, including market sizing, competitive analysis, go to market strategy
- Applying lean methodology techniques to launch MVPs and find product market fit
- Growth Hacking
- Product and business development
- Digital marketing strategy
- Capital raising including marketing materials and business introductions
- Entrepreneur Mentorship & Advisor
- Lean startup
- EIR or non exec roles

Mentor @PiLabs @turn8 @istarter @TeamPK

Startup Investor, Advisor & Mentor

University of Warwick - Warwick Business School

Guest Lecturer

Spinks

Advising the senior Spinks team on continual growth for being the talent partner to the Start-Up / Scale Up sector.

Spinks Advisory Board Member

Royal Holloway, University of London

UCL School of Management

Loughborough University

Imperial College Business School

ESCP Business School

Washplus - Dubai's Laundry App

Acquired by new player in GCC with great ambitions. 

Seed investor and marketing advisor to Dubai's leading on-demand laundry and dry cleaning app, washplus. 

Founded in Dubai Silicon Oasis as part of Dubai's smart city drive we have an awesome vision and a very strong founding team and set of angel investors. 

Have been featured in 7 days, Time Out Dubai, Dubai Chronicle and Ahlan!

Washplus - On demand Laundry App

Co-Founder of Uhubs | Bestselling Author: 'The Unfair Advantage' | Empowering SaaS Sales Leaders to Exceed Targets

Hey there! I’m Ash Ali, an entrepreneurial spirit who thrives on turning ideas into reality. 

Best known as the first marketing director at Just Eat, I helped scale it to Series A which then went onto become UK’s first tech unicorns.

But my journey didn't start in the usual boardroom. Without a formal degree, I carved my path through sheer grit, instinct, and a knack for seeing opportunities where others see obstacles. 

Today, I leverage my 20 plus years experience to mentor, invest in, and fuel the next generation of startups.

I co-authored "The Unfair Advantage: How You Already Have What It Takes to Succeed" which won Business Book of the Year.

Our book dives into leveraging your unique traits for success — something I live by and encourage others to recognise in themselves.

I'm currently leading Uhubs, a cutting-edge B2B sales performance startup that empowers sales leaders to define and measure excellence in their teams. 

Our unique Pulse technology provides real-time insights and metrics, helping businesses understand "what good looks like" in sales performance. 

It's about bringing clarity and precision to skills for better sales strategies, enabling teams to thrive in today's competitive markets.

My personal philosophy is to:

"Embrace your unique journey, your most significant 'unfair advantage' might just be something you already possess."

If you’re an entrepreneur at heart, a sales leader looking to elevate your team, or just want to chat about the startup world's highs and lows, feel free to connect! 

I'm always open to sharing ideas, speaking at events and conferences and specialise in GTM, Marketing, Sales and everything to do with starting tech startups.

Co-founder - We're hiring!

Uhubs helps busy sales leaders at top SaaS companies optimise their sales enablement to develop reps and grow revenue, by uncovering blind spots on performance, motivation and skills.

The Uhubs Pulse platform visualises each team’s development needs for personalised upskilling & provides data-driven coaching recommendations that are saving Sales Managers 40+ hrs per quarter and helping reps ramp up 30% faster.

Supercharge your sales team by booking a demo with us today.

I help organizations unlock the full value of their data, turning raw information into strategic insights that drive measurable business impact. Whether it's optimising operations, enhancing customer experiences, or uncovering new revenue opportunities, I specialise in transforming data from a passive asset into a powerful decision-making tool. My expertise spans data strategy, analytics, AI, and data culture, ensuring that businesses don’t just collect data, but actually use it to create competitive advantage.

I have spent most of my career helping organisations turn data into business value. With a Ph.D. in Psychology specialising in modelling and predicting human behaviour, I began as a lecturer before transitioning into the industry, where I have worked with some of the world’s largest brands, including Unilever, Shell, Coca-Cola, Microsoft, JLR, Ford, VW, GSK, ITV and Novartis, to drive measurable outcomes through data.

Currently, I run two data companies, Be Data Solutions and Subatomic Analytics, with offices in the UK and Bangladesh. I also serve as a Data & AI advisor for OutThink, FitForWork, and Data Ravens, alongside other start-ups operating in stealth mode.

Previously, I was Chief Data Scientist at Tribal Worldwide, leading the data strategy across its London and Singapore agencies. As Managing Director at Stream Intelligence, a data agency I co-founded, I worked with clients across Europe, the UAE, and Indonesia. At Wunderman (a WPP company), I was Managing Partner for Data & Insights, where I led a global team of data scientists, analysts, strategists, and technical specialists.

In leadership roles at Adam&Eve/DDB and ITV, I built and strengthened data functions that continue to deliver value today. At ITV, I established core data, digital marketing, social media, and CRM teams, integrating data into key business decisions.

Beyond my corporate work, I am passionate about fostering a strong data culture. I authored Data Culture: Developing an Effective Data-Driven Organisation to help businesses maximize the impact of their data.

You can buy the book at -  https://amzn.to/3ywnlU2

Dr Shorful Islam

Thinking about starting a Data Analytics Agency?

Stop right there.

Here's the 3-point framework that separates a successful agency owner from a burnt-out freelancer.

A. The 80% Technical Rule ⚙️ 
You can't run an analytics agency if you can't deliver the core product.

I watched a new agency try to 'manage' a $60K project. They didn't know how to build the data pipeline themselves. The contractor they hired bailed, the project failed, and they had to subcontract the project to another agency, costing them more than they made.

The cycle goes:

Define Business Problem → Data Access → Data Ingestion → Processing, Transforming & Cleaning →  Reporting & Analysis → Presentation

Can you personally handle 80% of this?

If not, you're not ready. Go get experience in the missing pieces.

If yes, you're good. You need this for two reasons:
You can't manage what you don't understand.

In year one, you ARE the team. 

You can't afford to outsource the core work.

B. Market Yourself Before You Leave 📢 
When you sell analytics, clients buy you, not your "agency."
Right now, you have your company's name backing you. 
Use it.

I've seen people spend six months networking while employed. Their current company's logo got them in the room. By the time they left, they had three warm leads and one signed contract.

Once you're out, you're just another consultant. Getting meetings becomes 10x harder.

Try this: 

Line up your first client before you quit. Go back to your old company as a contractor for the first few months. It bridges the gap until you find your second client.

C. Kill the "Conveyor Belt" Client Model 🔄 
The biggest mistake is treating clients like one-and-done projects.
You'll burn out chasing new business every month.

I've seen a founder get hired to build a dashboard for $15K. 

Instead of moving on, they spent time understanding the client's quarterly planning process. That turned into an $80K annual retainer.
Your goal isn't just delivering the first project. It's building the relationship that leads to the second.

In short, you need three things:
- Technical mastery.
- The ability to sell.
- The skill to keep clients happy.

If you only have one or two, you're setting yourself up to fail

CEO & Co-Founder | Data & AI Advisor to CEOs | Author of "Data Culture"

The AI era has a simple test for your job security.

I call it the "Can you codify your job?" test.

If you can write down your entire workflow in a document and hand it to someone else without a face-to-face conversation, your role will be automated (perhaps it already is).

The reality is that repeatable tasks will not exist for much longer.

This means that if you are doing a job that can be codified, it's gone.
This is why your unique value is no longer in technical execution. AI is taking that speed advantage.

Your value is now in human translation and contextualisation.

The human bit is the part that cannot be written in a document. 

Sometimes you need to grab coffee with a stakeholder instead of scheduling a meeting. In the meeting, they give you surface-level answers. Over coffee, they tell you what they actually mean.

Understanding people, navigating politics, and connecting your work to business outcomes. That's what makes you irreplaceable.

Your most powerful tool is no longer a technical one.

It is your human interaction.

Technical execution is no longer a competitive advantage.

AI has made the mechanics of data work accessible to everyone. 

Anyone can now get help to:
- Write SQL queries that run.
- Debug Python scripts that were breaking.
- Build Tableau dashboards that look professional.

The barrier to generating output has collapsed.

I've spent 25+ years building data teams at companies like ITV and Wunderman, scaling departments from 2 people to over 100. And I'm watching two paths emerge.

Path 1: Analysts who use AI to execute faster, but stop thinking critically about what they're building. They become glorified copy-paste operators.

Path 2: Analysts who use AI to handle the mechanics, then apply their judgment to validate, refine, and ensure the output actually solves the business problem.

The first group becomes commoditised.
The second group becomes indispensable.

The problem isn't using AI (I use it daily, and so should you). 

The problem is outsourcing your judgment to a tool that doesn't understand your business context. When you blindly accept AI-generated output:
- You miss the errors in logic that skew your analysis.
- You fail to validate whether the data actually represents reality.
- You ship a dashboard that looks beautiful but doesn’t answer the business question.

So how do you become indispensable when everyone has access to the same tools?

You master what AI can't do: understanding whether the output is right.

The market is flooded with people who can generate a script. 

But there's a massive shortage of people who can look at AI-generated output and instantly spot:
1. The Context Gap: Does this solve the question the business meant to ask?
2. The Accuracy Gap: Does the data actually match what the analysis claims?
3. The Logic Gap: Does this approach make sense for our specific business rules?

This is your leverage point. AI handles execution speed. You handle context.

Don't compete on speed. The robot will always win.

Compete on fundamentals.

Use AI to do the heavy lifting, but never let it do the thinking.

P.S. If you're building your data career in the AI era, what skill are you doubling down on?

There's a common misconception that a great data culture means everyone is in alignment.

In my experience, the opposite is often true. 

A great data culture doesn't eliminate arguments; it makes them productive.

We all instinctively seek alignment because organisational conflict feels inefficient and messy. This leads us to chase the wrong "single version of the truth", which is a single, correct interpretation that everyone must agree on.

This is a flawed and unrealistic goal.

The reality is that people make decisions, and people will always have different viewpoints and biases. The true purpose of a data-driven culture is not to force a single opinion.

It is to ensure a single, shared evidence base.

In medicine or academia, researchers don't all reach the same conclusions from the same study. But they all start with the same data. 

That's the real "one version of the truth"—not a final, unarguable conclusion, but the raw, uninterpreted data itself: the agreed-upon set of facts from which a debate must begin.

The goal isn't to stop people from debating. 

It's to stop them from debating in two different realities, using two different sets of facts.

A strong data culture is the framework that allows for vigorous, healthy, and evidence-based disagreement. It's how you turn data conflict into a productive business conversation.

Have you ever tried to help someone, but accidentally made things worse? This is a huge risk for data teams who deliver logical "solutions" without understanding the human context.

In my work as Data and AI Advisor for a Health Startup, we designed methodologies to understand and improve resilience.

We focus on "micro-stressors": those constant, nagging worries about things like finances that build up over time.

Using machine learning, we identified two groups of people with low resilience, both worried about money.

The logical advice? → "Save money".
It worked for Group 1.

But for Group 2, their resilience dropped, and stress levels increased.
We had triggered a "contra-effect."

Why?

We had to look closer at the human context.

"Saving money" meant cutting their daily coffee. That daily coffee, it turned out, was their only social support system. It’s their one moment of connection with friends.

Our logical solution had inadvertently prescribed social isolation.

The real solution was more human: "You can still see your friends, but maybe go for a walk instead of the coffee shop".

The data showed us the problem, but the context held the actual solution.

It’s a clear reminder that at the end of every data point, there is a human.

P.S. 
Not every organisation can uncover this level of insight. 

We find that working with companies that have a strong data culture, we are able to move beyond generic reporting and analytics and uncover insights that drive real business results. 

If you are ready to build that strong data culture, we should talk!

You’ve been asked to set up an analytics team. 

But you don’t have a background in data science.

This is the moment where most leaders fail.

The temptation is to hire a Senior Analyst with a perfect CV.

You see the credentials, the years of experience, and the specialised toolset. 

You think, "They know what they are doing."

That is usually a mistake.

After building data teams for two decades, I've seen Senior Analysts fall into the trap of 'Waiting for Perfection': building data warehouses and governance frameworks before answering burning business questions.

These are important. But you don't have time for that. 

You need to prove value in weeks, not months.

If your first hire takes three months to deliver their first insight because the data wasn't “clean enough," you lose momentum. 
Stakeholders lose interest. 

The budget gets cut.
Instead, hire a "Doer."

This is someone who can come in on Day 1 and get their hands dirty. You need a generalist who can handle the entire lifecycle of a business question:

- Import the messy data from the sales CRM.
- Transform and manipulate it without waiting for IT.
- Analyse the numbers to find the pattern.
- Present the findings directly to the stakeholder.

No handoffs. 
No waiting for a data engineer. 
No "black box" analytics.

The goal isn't to do analysis. The goal is to show value to the business.

When you answer a burning business question in Week 2, you buy yourself the credibility to build the "perfect" infrastructure later.
Start with the Doer. 

The specialisation comes after you survive.

When a stakeholder seems unimpressed with your work, it stings. Most of the time, it happens because of one missed conversation. 

That’s what led me to create my 5-question SMART framework.

My friend Min Bhogaita, who used to be Director of Analytics at Deloitte, once told me a story that captures this perfectly.

When he set up the Analytics Lab, his team got their first request: a CD of data and a vague ask for “insights.”

Eager to prove themselves, they dove straight into analysis. When the stakeholder returned for the results, they weren’t impressed.

The work wasn’t bad. 

It just missed the point, because no one had asked the key question: “What business problem are we trying to solve?” After leading data teams for over two decades across the NHS and ITV, I’ve seen this pattern over and over again. 

Strong analysis gets ignored because it doesn’t connect data to decisions.

That’s why I built the 5-question SMART Framework. It helps analysts become translators, not just technicians.

Here’s how it works:

S=Solve
Ask, “What specific business problem does this solve?” If you can’t explain the challenge clearly, you’ve already lost your audience.

M= Message
Ask, “Can you explain your main finding in one sentence?” Simplicity earns attention.

A= Action
Ask, “What decision will this analysis help make?” Without a clear next step, analysis becomes trivia.

R= Risk
Ask, “What happens if we ignore this?” Quantify the cost of inaction to show why it matters.

T= Target
Ask, “Who will act on this?” A CEO needs the strategy; a manager needs the tactic. They’re not the same.

Miss any of these five, and you risk ending up where Min’s team did: solid work, no impact.

Even good analysis gets overlooked when it isn’t tied to a clear business question

When you Stakeholder Looks Unimpressed 

Big data strategies often promise too much and deliver too late. That’s why the smartest way to execute any data strategy is to start small and show quick wins. 

But there’s a catch. 
Quick wins alone aren’t enough.

I’ve seen data leaders struggle because these wins often become scattered tactical projects that don’t build toward anything meaningful.
That’s where the Executional Road Map comes in. 

It runs alongside your long-term strategy but focuses on small, independent projects you can do right now with your existing data and tools. No big upfront investment in technology or people is required. 

Each project delivers real value, so the business sees benefits quickly without waiting for a massive rollout.

The results speak for themselves. 

For instance, I worked with a broadcaster in the Middle East on their video-on-demand service. Their goal was to unlock value from the data they already had, without pouring money into new tech or hiring more people.

Using the executional road map, we identified small initiatives that could be executed immediately. By analysing the data they were already collecting, we produced insights that improved the service. 

Meanwhile, larger projects, like centralising data from multiple systems and rolling out a reporting tool, continued in parallel.

The beauty of an executional road map is that each initiative is independent. There’s minimal dependency between projects, and every single one demonstrates real value. 

Quick wins don’t just show progress. 
They build confidence, momentum, and proof that your data strategy actually works.

If you’re struggling to make your data strategy stick, start thinking like this: not just long-term plans, but small, impactful projects you can execute today.

Founders obsess over their proposition, pricing, marketing, and team.  But if you don't manage this one area, your business will die.

That silent killer is Cash Flow. 

It's the biggest threat because the problem is invisible until it's too late. You can be wildly profitable on paper but still go bankrupt because the bank account is empty.

This problem almost always starts with the 60-Day Cash Gap.

As a service business, you get paid after the work is done. This creates a simple, deadly problem.

You deliver work in January. 
You sent the invoice on February 1st. 
The client has 30 days to pay, so you get paid in March.

In the meantime, you're covering January and February salaries, office expenses, subscriptions, accountants, and insurance.

Two full months of costs before you see a single dollar from your first project.

And that's the best case. 

What happens when clients pay late? 

They delay 60 or 90 days because they have their own cash flow problems, or your invoice missed their payment cycle.

You can skip your own salary as a founder. 

But you can't skip your team's. Miss payroll a few times, and your best people leave. Your company is dead.

Here's how to survive it.
1/ Forecast the scenarios. Use your accounting software or a spreadsheet to map what happens if clients pay late.
2/ Know your real costs. Factor in VAT, Employer National Insurance, and pension contributions. Your true payroll cost is always higher than your base salary.
3/ Run the stress test. What if 20%, 50%, or 70% of clients pay 30 days late? Can you still cover payroll? For how many months? That's your runway.

Aim for three months. Enough cash in the bank to cover three full months of expenses, even if zero client income arrives.
And don't overcommit your cash. 

Avoid hiring people you don't need or signing annual contracts for software you're testing.

Choose a monthly payment for systems like Google Workspace or Jira so you can ramp users up or down. If you have an annual bill like insurance, ask to pay monthly. For other suppliers, see if you can delay payments to line up with when client invoices get paid.

If you're starting an agency, you must manage cash flow. 

This isn't just a day-one problem. 

It becomes even more critical as your payroll and expenses grow.

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The Battle

Ash Ali

Dr Shorful Islam

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The Battle

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Dr Shorful Islam

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