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What I Learned From Being a Broke, Unemployed Graduate.

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A programming language is for thinking about programs, not for expressing programs you’ve already thought of. It should be a pencil, not a pen.

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Why Africa’s Data Moment Is Now

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First, solve the problem. Then write the code.

Necessary, making this the first true generator on the Internet. It re are many variations of passages of Lo rem Ipsum available, but the majority have suffered alteration in some form, by injectedeed eedhumour, or randomised words which don’t look even slightly believable.

A programming language is for thinking about programs, not for expressing programs you’ve already thought of. It should be a pencil, not a pen.

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There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some form, by injected humour, or randomised words which don’t look even slightly believable. If you are going to use a passage of Lorem Ipsum. You need to be sure there isn’t anything embarrassing hidden in the middle of text. All the Lorem Ipsum generators on the Internet tend toitrrepeat predefined chunks. Necessary, making this the first true generator on the Internet. It re are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some form, by injectedeed eedhumour, or randomised words which don’t look even slightly believable.

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Lessons Learned from Designing a Data Sharing Framework

A few months ago, I was tasked with designing a Data Sharing Framework for a complex project that brought together government officials, private sector players, and humanitarian stakeholders.

At first, it felt overwhelming—so many actors, so much data, and so many expectations. But as I went through the process, I began to see patterns and lessons that made the work not just possible, but impactful..

Here are the biggest lessons I learned that I believe anyone can apply when building a framework for data collaboration:

🔹 1. Begin with clarity of purpose
I learned that you can’t design a framework in a vacuum. Start by asking: What is the project trying to achieve? What data is truly needed? Once the objectives are clear, everything else starts to align.

🔹 2. Stakeholder mapping is non-negotiable
Before the first meeting, I created a detailed map of all stakeholders—who they are, what they bring to the table, and where their influence lies.

🔹 3. Data quality and security come first
In an era where data privacy is a hot topic, I quickly realized that trust hinges on how well you handle data quality and security. Without strong safeguards, no framework will survive stakeholder scrutiny.

🔹 4. Understand what data really means
It wasn’t enough to say “we’ll share data.” I had to break it down: Are we talking about personal identifiable information? Aggregated stats? Operational data? Knowing who holds what and how it has been shared before was key.

🔹 5. Capture stakeholder needs early
I made it a priority to listen—really listen—to what stakeholders needed from the framework. Some wanted visibility, others wanted safeguards, and a few wanted both.

🔹 6. Always bring a draft to the table
One mistake I avoided: calling a meeting with no draft. By sharing an initial framework early, I gave stakeholders something concrete to react to.

🔹 7. Co-create, don’t dictate
During workshops, I shifted from “presenting” to “co-creating.” I asked guiding questions, facilitated group discussions, and made sure every voice was heard. That’s when the framework started to feel like ours, not mine.

🔹 8. Transparency builds trust
The more I explained every element of the framework, the more open stakeholders became. Transparency created room for tough questions.

🔹 9. Decide on the legal angle early
One key decision was whether the framework should be legally binding or serve as a reference guide. Getting clarity on this upfront prevented confusion later.

🔹 10. Train and empower champions
After the framework was approved, I worked with stakeholders to identify champions who would train their teams. That step was essential to moving from “document on paper” to “culture in practice.”

💡 My takeaway:

A Data Sharing Framework isn’t just about systems and protocols—it’s about people.

👉 If you’ve ever been part of building or using a data sharing framework, what lessons stood out for you?

The Data Problem is the People Problem

Every evening, on the train from work, my mind doesn’t switch off.

Munich moves past the window — commuters, lights, quiet conversations — and I’m somewhere else entirely. Thinking about DATA.

Not the CODE. Not the MODELS. The PEOPLE around them.

I’ve spent years building data systems for some of the world’s most complex problems — Child Mortality Prediction, flood forecasting, food security, and agricultural risk in the world , East and Central Africa. And somewhere along the way, I noticed a pattern.

The projects that failed rarely failed because of bad data.

They failed because someone didn’t believe in its value.

A department head who saw data sharing as losing control.
A team that had been burned before and wasn’t ready to trust again.
A brilliant engineer who spoke only in technical terms to people who needed plain language.

I’ve sat in those meetings. I’ve watched a room full of intelligent people talk past each other — not because they disagreed on the destination, but because nobody had built a bridge between what the data could do and what the business actually needed to hear.

Data is only as powerful as the humans willing to act on it.

The cleaning, the engineering, the modelling — those are skills you can learn. What’s harder to teach is the ability to walk into a room of sceptics and bring them along.

That’s the work nobody talks about.

What’s the most human challenge you’ve faced in a data project? I’d love to hear it.

I grew up in Kenya watching Arsenal. 22 years later, the data finally makes sense.

Last night, Arsenal won the Premier League for the first time in 22 years.

I am a Gooner. Have been since I was a kid in western Kenya, watching grainy replays of Thierry Henry glide past defenders like they weren’t there. For many of us who grew up in Africa, Arsenal wasn’t just a football club. It was a feeling. A way of seeing the world.

But today I’m not writing as a fan. I’m writing as a data person. And the data behind this club tells a story that goes far beyond football.

Let me start with the season just won.

Arsenal conceded only 26 goals all season. The fewest in the league. Their goalkeeper, David Raya, kept 19 clean sheets. But what strikes me most isn’t that number. It’s what it represents: a team that decided the most reliable path to winning was making it almost impossible to lose. They didn’t dazzle every week. They were just relentlessly, quietly, difficult to beat..

Since 2022, Arsenal spent 562 days at the top of the Premier League table — more than any other club. For three years in a row, they finished second. The trophy kept escaping them. But they never left the top.

That is not bad luck. That is character. And eventually, character converts.

Now here is where it gets interesting for me as a data person.

Arsenal have an estimated 100 million fans worldwide. Indonesia and the USA each generate roughly 9% of their global search traffic. Brazil contributes 6%. And Africa? Africa is woven through this club in ways that rarely get spoken about.

Aliko Dangote — the wealthiest person on the African continent, worth over $30 billion — has been publicly trying to buy this club for years. Khaligraph Jones, Kenya’s most prominent rapper, literally rapped about Arsenal in a hit song. Raila Odinga was a Gooner. Paul Kagame is a Gooner. The current UK Prime Minister gifted Arsenal merchandise to a sitting US President as one of his first diplomatic acts in office.

Arsenal’s fanbase includes a sitting head of government, Africa’s richest person, a 7× Formula 1 world champion, a reigning NBA champion, and four Oscar-winning actors. This is not a coincidence.

I want to sit with that for a moment.

We live in a world where people constantly debate what it means to belong. Where you grew up. Which passport you carry. Which language you think in. But 100 million people, from Lagos to Jakarta to Rio to Munich, woke up this morning connected by the same red shirt.

Here is what the data is actually saying, if you look past the numbers:

Arsenal are the most hated fanbase in the Premier League right now — 43% negative sentiment online, the joint highest in the division. Meanwhile Manchester City, who face 115 alleged financial rule breaches, sit at just 25%.

You know what that tells me? We are hated because we are present. Because we refuse to be quiet. Because we kept showing up at the top of the table for three years with nothing to show for it, and still believed.

Hate is data too. It tells you who people feel threatened by.

I work in data every day — building AI systems to predict floods, estimate crop yields, understand where hunger is heading before it arrives. And one thing this work has taught me is that the most important data is not always the numbers themselves. It is the story the numbers are trying to tell.

Arsenal’s story is this:

Stay consistent long enough and the results catch up. Build something that people across cultures and continents feel belonging to, and you have something that lasts. Be so good — and so present — that even the people who hate you cannot ignore you.

That is not just a football philosophy. That is a life one.

22 years. 562 days at the top. 100 million people.

We are home.

— A Gooner from Kenya, writing from Munich 🔴

#Arsenal #PremierLeague #DataStorytelling #Football #Leadership #Africa #COYG

AI ADS PERFORM WORSE THAN HUMAN ONES

AI can make advertising faster, cheaper, and easier to scale.

But there’s a catch: looking good isn’t the same as performing well.

A recent study of 3,000 consumers found that AI-generated ads were difficult for people to distinguish from human-made ads — yet, on average, they performed 14% worse on short-term sales potential and 17% worse on long-term brand equity.

That distinction matters far beyond advertising.

The lesson for businesses adopting GenAI is simple:

Don’t measure AI by how much content it produces or how much it saves. Measure it by the outcomes it creates.

AI is excellent for:
→ Scaling and versioning
→ Prototyping and experimentation
→ Localization
→ Repetitive, conventional creative work

But humans still matter most where context, emotion, culture, originality, and judgment are involved.

The future isn’t AI vs. humans.

It’s AI + human judgment — with the discipline to know where each creates the most value.

#GenerativeAI#ArtificialIntelligence#AI#Marketing#Innovation#DataDriven