Gareth Sims

Here are some pretty awful facts... ⚠️ Only 1 in 5 AI & data professionals in the UK are women ⚠️ Globally, men outnumber women in data roles 4 to 1. ⚠️ Women make up 50% of the population, yet they represent only 20% of AI and data professionals ⚠️ 50% of women in the data and tech sectors leave by mid‑career, highlighting systemic barriers —The broken rung! That’s why I’ve joined Women in Data®—as an ally—because closing this gap isn’t just the right thing to do, it’s essential for better outcomes in our industry. Women in Data® is a free to join, global network of 90,000+ members driving real change through mentorship, events, and initiatives like Girls in Data. Expect Digital Edge Magazine to do more and be held accountable for representation and providing a voice for all women in data and technology leadership roles. 💡 Launching in Issue 05 next month, our new regular feature will spotlight these challenges—interviewing the next generation of female tech and data leaders, sharing experiences from both men and women, and gathering expert insights from those driving change. Why? ✅ Because diversity isn’t just morally vital—it powers better outcomes. Supporting this movement means embedding equity into data and tech culture. ✅ Because representation matters—We want to help address the vast gap between potential and presence in these fields. ✅ Because retention is as urgent as recruitment—with half of women leaving mid‑career, the need for supportive networks is more crucial than ever. If you care about building a more inclusive, innovative data community—You can join as well...Click here → www.womenindata.co.uk #WomenInData #DiversityInTech #DataLeadership #Allyship

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“We delivered 42 dashboards last quarter.” Cool. How many decisions did you influence? (…awkward silence…) I’ve had many conversations this month with senior data leaders who are ready to leave the industry. Burned out. Disillusioned. Not because they don’t care. But because they’re stuck in the same trap: → Constant requests → Constant delivery → Zero impact They’re running a dashboard factory, even though they never wanted to. And it’s killing their teams’ morale. Because here’s the thing: Most data teams are too busy reporting the past to shape the future. We’ve trained stakeholders to believe data = dashboards. And we’ve built entire operating models around shipping charts instead of solving problems. The outcome? Output up 📈 Outcomes down 📉 Data team joy = non-existent Want to get out of this trap? 👉 Join 2,500+ data leaders who read my free newsletter for weekly tips on building impactful data teams in the AI-era: https://lnkd.in/gWwcifxk ♻️ Repost if your team sighs when hearing the words: “Can you just add one more chart?”

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I’ve been in business for nine years this month. Here are nine (of the many) things I’ve learnt about data storytelling: 1. As sexy as data visualisation sounds, we’re now saturated in data visuals that are too easy to ignore. 2. The audience decides what works, not me. 3. The hard part isn’t finding numbers, it’s understanding what matters. 4. Without context, data is too easy to misread. 5. The same dataset can tell multiple truths, depending on the lens. 6. People repeat the story back, not the numbers. 7. The majority of people who want to tell data stories don’t, because it’s hard. 8. Data analysts have more power and influence than they realise. 9. People connect with the message, not the method. What would you add? ---- Kia ora, I'm Kat 👋 I wrote The Data Storyteller's Handbook to help improve how we communicate data insights. Check it out here: https://lnkd.in/gWs2kTP Use the code 9YEARS for 50% off the PDF (September 2025 only).

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AI is moving faster than most teams can handle. Data Management and literacy is now a vital skill for ALL your teams This week inside CTRL//SHIFT AI, here’s what we unpack for our members: 🚨 $364B projected AI spend from Big Tech in 2025 🧠 The predicted 10,000+ jobs lost to AI in July globally, and where the new demand is growing 💸 $2B raised by Thinking Machines to push autonomous agent AI ⚔ Amazon vs Walmart: The AI supply chain battle is heating up 📈 Governments are mandating upskilling — and why soft skills now drive hiring But here’s the thing no one’s saying loud enough: 74% of AI initiatives fail. Not because of tech, but because of poor data management and understanding, lack of AI strategy, poor to no training for their teams and no human context. That’s why we built CTRL//SHIFT: A space for founders, operators, professionals, and teams to not just “learn AI”… But to build confidence, clarity, and systems that work, without burning out. ✨ Data Fundamentals ✨ Strategy ✨ Mindset ✨ Automation ✨ Real human support 👀 Want in? Join the free tier of CTRL//SHIFT AI now and get weekly drops like this, real-time insights, and a smarter way to stay ahead of the shift. https://lnkd.in/e4PMb3BP

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Creating a custom data analytics solution for a business is apparently only half the job. I thought I understood that by now… But no one warned me how tricky the implementation phase would get! Once the system was built, I had to train the staff to actually use it …and that challenged me on a whole new level. Here’s what I learned as a result. 📌 I can’t be too “high-level” with my explanations. I had to simplify every concept. 📌 A custom-built solution is never truly “done” until it replaces the legacy system in use (in this case, pen-and-paper books) and is easy enough for the team to use while getting more done in less time. 📌 Teaching requires deep patience, especially when you’re someone who naturally learns fast. I’ve had to slow down, adapt to each person’s pace, and create an environment where learning feels safe and exciting. Even talking to business owners has been a lesson in communication: 📌 No tech jargon. 📌 No buzzwords. 📌 Just clear value in as few words as possible I am very much still a work in progress But this journey is stretching me in all the right ways. I now see how vast data analytics application really is and can be. Approaching it from this angle has allowed me to apply every skill I have and test the skills I’m still developing. So if you’re a data analyst reading this, you need to start thinking in terms of real-world application. What kind of problems do you want to solve with your skillset? That’s where your career clarity lies. And if you are still struggling to find yourself or figuring things out, it's okay too! Rome wasn't built in a day; you will get there! I want to help! Drop where you’re currently at in your journey in the comments I’ll do my best to give you a head start. 😊 #DataAnalytics #BuildInPublic #DataDriven #Growth #Career #KeepLearning

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