As an AI Product Growth Leader, I’m on a mission to revolutionize how businesses harness data and AI to drive innovation, accelerate growth, and scale impact. I lead with a strategic vision: to make data collaboration seamless, AI adoption intuitive, and business-centric innovation scalable.

At @DataGOL, I drive growth initiatives that position us as the go-to solution for intelligent business enablement. From BusinessOS to AI Agents and Self-Serve Analytics, I build intelligent experience engines that empower business users—not just data teams—to make faster, smarter decisions.

With 20+ years across enterprise SaaS, data platforms, and AI/ML products, I operate at the intersection of strategy, technology, and customer-centricity. My passion lies in translating complex capabilities into simple, scalable value—whether that’s through a game-changing product, a repeatable growth engine, or a transformative AI initiative.

I don’t chase hype. I build real-world, value-driven solutions that solve real business problems.

🤝 Let’s connect if you’re serious about using AI and data to redefine what’s possible for your business.

Vinod SP

𝗙𝗶𝘅 𝘁𝗿𝘂𝘀𝘁 𝗳𝗶𝗿𝘀𝘁, 𝗻𝗼𝘁 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀. 𝗧𝗵𝗮𝘁’𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗺𝗮𝗸𝗲 𝗱𝗮𝘁𝗮 𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲.

A new Head of Data walks in.

𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝟵𝟬 𝗱𝗮𝘆𝘀 𝗮𝗿𝗲 𝗮 𝘁𝗲𝘀𝘁.

Many start with dashboards, pipelines, and plans. They rebuild what’s broken and expect trust to follow.

𝗕𝘂𝘁, 𝗺𝗼𝘀𝘁 𝗳𝗮𝗶𝗹.

They forget that trust, not tools, is the real foundation. You can fix every schema and still have leaders asking, “Why are we still in this mess?”

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀:

𝗣𝗵𝗮𝘀𝗲 𝟭: 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗲, 𝗗𝗼𝗻’𝘁 𝗗𝗲𝗹𝗶𝘃𝗲𝗿.

Meet every key person. Ask what data they trust. Listen to real pain, not just reports. Find your “data superusers.” See where data dies before it reaches the decision.

𝗣𝗵𝗮𝘀𝗲 𝟮: 𝗔𝗹𝗶𝗴𝗻 𝗮𝗻𝗱 𝗗𝗲𝘀𝗶𝗴𝗻.

Prioritize quick wins. Rank by impact, complexity, reach, and risk. Set clear ownership for metrics. Share updates every week.

𝗣𝗵𝗮𝘀𝗲 𝟯: 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗣𝗿𝗼𝗼𝗳, 𝗡𝗼𝘁 𝗣𝗿𝗼𝗺𝗶𝘀𝗲𝘀.

Pick the highest priority. Deliver one visible win in 30-45 days. Align on definitions so everyone speaks the same language. Over communicate wins and issues.

𝗔𝘃𝗼𝗶𝗱 𝘁𝗵𝗲𝘀𝗲 𝘁𝗿𝗮𝗽𝘀:

• Don’t rush to buy new tools.
• Don’t rebuild dashboards before fixing trust.
• Don’t promise AI if you have ten definitions of revenue.

The first 90 days decide if data drives growth or stays a reporting chore.

𝗜𝗳 𝘆𝗼𝘂𝗿 𝗖𝗙𝗢 𝘀𝘁𝗶𝗹𝗹 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝗯𝘆 𝗗𝗮𝘆 𝟵𝟬, 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 𝗲𝗹𝘀𝗲 𝗺𝗮𝘁𝘁𝗲𝗿𝘀.

Trust comes first.

Visible wins come next.

𝗧𝗵𝗮𝘁’𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝘀𝘁𝗼𝗽 𝗯𝗲𝗶𝗻𝗴 “𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗽𝗲𝗿𝘀𝗼𝗻” 𝗮𝗻𝗱 𝗯𝗲𝗰𝗼𝗺𝗲 𝘁𝗵𝗲 𝗽𝗲𝗿𝘀𝗼𝗻 𝘄𝗵𝗼 𝗺𝗮𝗸𝗲𝘀 𝗱𝗮𝘁𝗮 𝘄𝗼𝗿𝗸.

𝗛𝗼𝘄 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗿𝘂𝘀𝘁 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘁𝗲𝗮𝗺𝘀?

Building AI Agents that are powerful enough to run your business @DataGOL | Ex-Meta | AI Product Builder | Chief Data & AI officer | Harvard Business School

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𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀

I've had the same conversation with 3 different CFOs this month.

They all said some version of this: 

"My finance team just ran their own margin analysis. Without the data team. In 30 seconds."

🔁 𝗧𝘄𝗼 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀. 𝗦𝗮𝗺𝗲 𝗱𝗮𝘁𝗮. 𝗭𝗲𝗿𝗼 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻. 𝗧𝗵𝗶𝘀 𝗶𝘀𝗻'𝘁 𝗮 𝗰𝗼𝗺𝗽𝗹𝗮𝗶𝗻𝘁. 𝗜𝘁'𝘀 𝗮 𝘀𝗶𝗴𝗻𝗮𝗹.

"𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗙𝗶𝗻𝗮𝗻𝗰𝗲" becomes a standard function and the data < >finance wall collapses.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁'𝘀 𝗱𝗿𝗶𝘃𝗶𝗻𝗴 𝘁𝗵𝗶𝘀:

The era of descriptive dashboards is ending. AI agents are forcing the shift to diagnostic analytics.

𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲: "Revenue was $2.3M in May"

𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰: "Revenue spiked because Product X drove 47% of new bookings, but COGS increased 23% due to supplier changes"

One tells you the score. The other tells you how to change the game.

𝗔𝗻𝗱 𝘁𝗵𝗲 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲 𝗶𝘀 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴:

→ DataGOL launches a "𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗔𝗴𝗲𝗻𝘁" that confirms account ranges with CFOs before calculating margins (human-in-the-loop, not black box)

→ Finance teams moving from "what happened" to "why it happened" without writing SQL

→ The "golden layer" (clean, standardized data) becoming the moat not the dashboard itself

𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀 𝘄𝗶𝗹𝗹 𝗺𝗲𝗿𝗴𝗲:

• 𝗕𝗜 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 → AI-driven diagnostic layers

• 𝗔𝗱-𝗵𝗼𝗰 𝗱𝗮𝘁𝗮 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 → Conversational agents

• 𝗠𝗼𝗻𝘁𝗵𝗹𝘆 𝗿𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗰𝘆𝗰𝗹𝗲𝘀 → Real-time variance investigation

𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 𝗹𝗼𝗰𝗸-𝗶𝗻 𝘄𝗼𝗻'𝘁 𝗯𝗲 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘄𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲.

→ It'll be your diagnostic intelligence layer.

→ The teams that own this conversation will have a seat at the board table.

→ The ones still building dashboards that answer "𝘄𝗵𝗮𝘁" will keep wondering why no one looks at them.

Are you still answering "𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱" while your board wants to know "𝘄𝗵𝘆"?.

Check out DataGOL DataScience Agent in the comment.

𝟮𝟬𝟮𝟲 𝘄𝗼𝗻’𝘁 𝗿𝗲𝘄𝗮𝗿𝗱 𝘁𝗵𝗲 𝗳𝗹𝗮𝘀𝗵𝗶𝗲𝘀𝘁 𝗔𝗜.

It’ll reward the AI that survives the audit: 𝗥𝗢𝗜, 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗲 inside real teams, real systems, with real guardrails.

𝗠𝗼𝘀𝘁 “𝗔𝗜 𝘄𝗶𝗻𝘀” 𝗜 𝘀𝗲𝗲 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄 𝗹𝗼𝗼𝗸 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀:
✅ a pilot that generates
❌ a workplace that can’t metabolize the output
So the org gets buried under polished drafts… and nobody’s sure what’s true, what’s safe, or what’s worth acting on.

𝗧𝗵𝗲 𝟮𝟬𝟮𝟲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝗱𝗮 𝗶𝗻 𝟯 𝗺𝗼𝘃𝗲𝘀

𝗖𝘂𝘁 𝘁𝗵𝗲 𝘀𝗹𝗼𝗽 𝗮𝘁 𝘁𝗵𝗲 𝘀𝗼𝘂𝗿𝗰𝗲 → retire bad routines before AI scales them.

𝗧𝗿𝗲𝗮𝘁 𝗮𝗴𝗲𝗻𝘁𝘀 𝗹𝗶𝗸𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 → permissions, monitoring, compliance: non-negotiable.

𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗺𝗼𝗮𝘁 𝗶𝗻 𝘄𝗵𝗮𝘁 𝗰𝗮𝗻’𝘁 𝗯𝗲 𝗰𝗼𝗽𝗶𝗲𝗱 → proprietary data + human expertise + intentional creativity.

The “𝗔𝗜 𝗳𝘂𝘁𝘂𝗿𝗲” isn’t less human. 𝗜𝘁’𝘀 𝗺𝗼𝗿𝗲 𝗵𝘂𝗺𝗮𝗻 because:

• Creativity becomes scarce advantage when output is frictionless.

• Human expertise gets pricier when AI sounds confident about everything.

• And if AI smooths away the messy debate, you lose the work trails that keep teams aligned.

If your org gets judged on measurable value in 2026…
𝘄𝗵𝗮𝘁 𝗮𝗿𝗲 𝘆𝗼𝘂 𝘀𝘂𝗻𝘀𝗲𝘁𝘁𝗶𝗻𝗴 𝗳𝗶𝗿𝘀𝘁: meetings, workflows, reports, approvals or “AI-generated busywork”?

𝗧𝗵𝗲 𝗦𝗲𝗰𝗿𝗲𝘁 𝗦𝘂𝗽𝗲𝗿𝗽𝗼𝘄𝗲𝗿: 𝗪𝗵𝘆 𝗛𝗮𝗯𝗶𝘁𝘀 𝗕𝗲𝗮𝘁 𝗪𝗶𝗹𝗹𝗽𝗼𝘄𝗲𝗿 𝗘𝘃𝗲𝗿𝘆 𝗧𝗶𝗺𝗲! 🔥

Ever wonder why it's SO HARD to stick with something new? The answer is simpler than you think! 😊

Visuals by @Pejman Milani

Look at willpower like a candle - it starts strong but eventually burns out. We've all been there... super excited on day 1, kinda tired by day 3, and completely giving up by day 7! 🕯️
But habits? They're like an endless energy source! ⚡
The science is clear:

Willpower runs out (just like your phone battery)
Habits run on autopilot (like breathing!)
Small daily actions > big one-time efforts

What's ONE tiny habit you could start today? Maybe drinking a glass of water when you wake up? Or spending 5 minutes reading before bed?

Remember: Consistency beats intensity! Build those habit flames and watch them power your success! 🚀

hashtag#HabitHacks

𝟭𝟭 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗧𝗵𝗮𝘁 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗱 𝗠𝘆 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 🚀

Ever wonder why some leaders effortlessly inspire while others struggle despite the fancy title?

Visuals from @Pejman Milani

I've distilled my leadership journey into 11 visual frameworks that changed my approach. (#10 was my personal game-changer!)

Three Core Leadership Principles:
✅ Today's hard choices create tomorrow's success
✅ Authority comes from action, not position
✅ Clear thinking creates clear direction

𝗠𝘆 𝟭𝟭 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝘃𝗶𝘀𝘂𝗮𝗹𝘀 𝗱𝗲𝗰𝗼𝗱𝗲𝗱:
1️⃣ 𝗬𝗼𝘂𝗿 𝘁𝗶𝘁𝗹𝗲 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗺𝗮𝗸𝗲 𝘆𝗼𝘂 𝗮 𝗹𝗲𝗮𝗱𝗲𝗿
Leadership is earned daily through consistent actions that build trust, not handed out with business cards.
2️⃣ 𝗪𝗿𝗶𝘁𝗶𝗻𝗴 𝗺𝗮𝗸𝗲𝘀 𝘃𝗮𝗴𝘂𝗲 𝗶𝗱𝗲𝗮𝘀 𝗰𝗹𝗲𝗮𝗿
Put thoughts on paper and watch how quickly the gaps appear! Clear documentation transforms good thinking into great execution.
3️⃣ 𝗠𝗼𝘀𝘁 𝗼𝘃𝗲𝗿𝗻𝗶𝗴𝗵𝘁 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗶𝘀 𝘆𝗲𝗮𝗿𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗮𝗸𝗶𝗻𝗴
Great teams aren't born—they're methodically built through consistent effort.
4️⃣ 𝗠𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗮𝗿𝗲 𝗶𝗻𝗲𝘃𝗶𝘁𝗮𝗯𝗹𝗲. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲𝗺 𝗶𝘀 𝗮 𝗰𝗵𝗼𝗶𝗰𝗲
Transform failures into frameworks for future success through systematic learning.
5️⃣ 𝗛𝗮𝗿𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀, 𝗲𝗮𝘀𝘆 𝗹𝗶𝗳𝗲. 𝗘𝗮𝘀𝘆 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀, 𝗵𝗮𝗿𝗱 𝗹𝗶𝗳𝗲
The courage to make difficult choices today prevents bigger crises tomorrow.
6️⃣ 𝗙𝗲𝗮𝗿 𝗶𝘀 𝗯𝗶𝗴𝗴𝗲𝗿 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗺𝗶𝗻𝗱 𝘁𝗵𝗮𝗻 𝗿𝗲𝗮𝗹𝗶𝘁𝘆
Action is the antidote to anxiety. Start before you feel ready—momentum creates clarity.
7️⃣ 𝗠𝗮𝗸𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗯𝘆 𝗿𝗲𝘃𝗶𝘀𝗶𝘁𝗶𝗻𝗴 𝘁𝗵𝗲𝗺
Yesterday's perfect solution needs today's fresh perspective.
8️⃣ 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗰𝗼𝗺𝗲𝘀 𝗶𝗻 𝗺𝗮𝗻𝘆 𝗳𝗼𝗿𝗺𝘀
Progress rarely follows a neat path. Small, sustainable steps consistently taken beat sporadic giant leaps.
9️⃣ 𝗕𝘂𝗿𝗱𝗲𝗻 𝗼𝗳 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲 𝗶𝘀 𝗴𝗿𝗲𝗮𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗕𝘂𝗿𝗱𝗲𝗻 𝗼𝗳 𝗿𝗲𝗴𝗿𝗲𝘁
Today's effort prevents tomorrow's pain. Disciplined processes create freedom, not restriction.
🔟 𝗬𝗼𝘂 𝗰𝗮𝗻 𝗵𝗮𝘃𝗲 𝗮𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝗯𝘂𝘁 𝗻𝗼𝘁 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴
Focus beats fragmentation. Choosing what to ignore is as strategic as choosing what to pursue.
1️⃣1️⃣ 𝗔 𝗹𝗲𝗮𝗱𝗲𝗿 𝗸𝗻𝗼𝘄𝘀 𝘁𝗵𝗲 𝘄𝗮𝘆 𝗮𝗻𝗱 𝘀𝗵𝗼𝘄𝘀 𝘁𝗵𝗲 𝘄𝗮𝘆
Vision without action is just daydreaming. Your example speaks louder than any mission statement.

Which framework resonates most with your current challenges? I'd love to hear which one hits closest to home!
♻️ Repost this to help your network level up their leadership toolkit!

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𝟭𝟬 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀 𝗧𝗵𝗮𝘁 𝗪𝗶𝗹𝗹 𝗦𝗲𝘁 𝘁𝗵𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝗱𝗮 𝗶𝗻 𝟮𝟬𝟮𝟲

We are done playing with AI.

In 2026, AI agents enter work. Quiet. Deep. For real. 

I see 10 big shifts coming for enterprise and mid-market teams.

Here is my view:

1️⃣ 𝗔𝗴𝗲𝗻𝘁𝘀 𝘀𝘁𝗮𝗿𝘁 𝘁𝗼 𝘄𝗼𝗿𝗸
Not chatbots.
They read, write, click, move data, trigger flows.
They act in tools you use now.
Your team starts to manage agents, not chats.

2️⃣ 𝗣𝗶𝗹𝗼𝘁 𝗽𝘂𝗿𝗴𝗮𝘁𝗼𝗿𝘆 𝗲𝗻𝗱𝘀
No more “cool demo” with no owner.
CFO asks one question:
→ Where is ROI in 90 days?
If you show value, you scale.
If not, budget dies. Fast.

3️⃣ 𝗗𝗮𝘁𝗮 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗮𝗴𝗲𝗻𝘁-𝗿𝗲𝗮𝗱𝘆
Agents fail if data is messy.
You need data that is:
→ Easy to find
→ Clean and linked
→ Safe for wide access
I call this “business-ready data”.

4️⃣ 𝗧𝗵𝗲 𝘀𝘁𝗮𝗰𝗸 𝗴𝗲𝘁𝘀 𝘀𝗺𝗮𝗹𝗹𝗲𝗿
You do not want 15 data tools.
You want one brain.
One place for:
→ Pipelines
→ Store
→ Metrics
AI-native platforms win here.

5️⃣ 𝗖𝗼𝗱𝗶𝗻𝗴 𝘁𝘂𝗿𝗻𝘀 𝗶𝗻𝘁𝗼 𝗿𝗲𝘃𝗶𝗲𝘄
Engineers write less raw code.
Agents draft flows and tests.
Your best people:
→ Set clear specs
→ Review diffs
→ Guard quality at scale

6️⃣ 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝘁𝘂𝗿𝗻𝘀 𝗿𝗲𝗮𝗹
Regulators wake up.
You must show:
→ Who touched what data
→ How AI made each call
→ Why output is safe
No proof, no trust.

7️⃣ 𝗔𝗜 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝘀𝗵𝗶𝗳𝘁𝘀
Per seat makes no sense when agents do work.
Expect:
→ Pay per outcome
→ Pay per use
→ Mix of both for large deals

8️⃣ 𝗠𝗶𝗱-𝗺𝗮𝗿𝗸𝗲𝘁 𝗷𝘂𝗺𝗽𝘀 𝗮𝗵𝗲𝗮𝗱
Less tech debt.
Faster change.
Strong AI-native tools mean a small firm with clear data beats a slow giant with old stacks. 🚀

9️⃣ 𝗕𝘂𝗶𝗹𝗱 𝘃𝘀 𝗯𝘂𝘆 𝗳𝗹𝗶𝗽𝘀
Home-grown AI infra looks cool.
Cost, talent, and risk say no.
You still build use cases.
You buy the boring layers.

🔟 𝗤𝘂𝗶𝗲𝘁 𝘄𝗶𝗻𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝗺𝗼𝘀𝘁
Big press drops fade.
Real edge comes from:
→ One agent that cuts 30% cycle time
→ One workflow with clean, shared data
No noise. Only impact.

I spend my days at @DataGOL on one question:
𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗺𝗮𝗸𝗲 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗔𝗜 “𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝗿𝗲𝗮𝗱𝘆” 𝗳𝗼𝗿 𝘁𝗲𝗮𝗺𝘀 𝘄𝗵𝗼 𝗻𝗲𝗲𝗱 𝘃𝗮𝗹𝘂𝗲 𝗶𝗻 𝗺𝗼𝗻𝘁𝗵𝘀, 𝗻𝗼𝘁 𝘆𝗲𝗮𝗿𝘀?

What part of this future feels closest to your world right now?

𝗛𝗲𝗮𝗱𝘀 𝘂𝗽, 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗟𝗲𝗮𝗱𝗲𝗿𝘀: 𝗧𝗵𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗢𝗦 𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗳𝗮𝘀𝘁!

AI is making execution simple, which means good judgment is your new superpower.
Stop competing on speed and start building an 𝗡𝗲𝘄 𝗣𝗠 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺.

I will be writing the detailed series over the following week.

My 5-part series is your blueprint for this shift:
𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝘁𝗵𝗲 𝗖𝗼𝗿𝗲 𝗦𝗵𝗶𝗳𝘁: Your new moat is not in features, it's in turning chaos into clear decisions and defining high-integrity constraints.

𝗧𝗵𝗲 𝗗-𝗔-𝗘 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: Use the Delegate, Amplify, Elevate model. Give all the repeatable tasks to AI and focus human energy on irreversible decisions.

𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 & 𝗦𝗲𝗻𝘀𝗲𝗺𝗮𝗸𝗶𝗻𝗴: Get better at interpreting data (Signal) and naming the real problem (Sensemaking). Always look for "𝗗𝗶𝘀𝗰𝗼𝗻𝗳𝗶𝗿𝗺𝗶𝗻𝗴 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲."

𝗦𝗵𝗶𝗽 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆: When you build AI, you are shipping a system, not just a feature. Govern it with a clear Decision Contract.

𝗧𝗵𝗲 𝗡𝗲𝘄 𝗠𝗼𝗮𝘁: Your value is defined by four things: providing Clarity, making Evaluation the new UX, designing for Trust, and running the Machine-Human system.

The winners won’t be the ones who “move fast.”
They’ll be the ones who decide well—𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁𝗹𝘆, 𝘁𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁𝗹𝘆, 𝘂𝗻𝗱𝗲𝗿 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀.

𝗜𝗳 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗶𝘀 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗰𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝗶𝘇𝗲𝗱… 𝘄𝗵𝗮𝘁’𝘀 𝗼𝗻𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴 𝘆𝗼𝘂’𝗱 𝗻𝗲𝘃𝗲𝗿 𝘁𝗿𝘂𝘀𝘁 𝘀𝗽𝗲𝗲𝗱 𝘁𝗼 𝗶𝗺𝗽𝗿𝗼𝘃𝗲?

📉 𝗬𝗼𝘂'𝘃𝗲 𝗣𝗲𝗿𝗳𝗲𝗰𝘁𝗲𝗱 𝘁𝗵𝗲 𝗦𝘁𝗮𝗰𝗸. 𝗕𝘂𝘁 𝘁𝗵𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗦𝘁𝗶𝗹𝗹 𝗗𝗼𝗲𝘀𝗻’𝘁 𝗧𝗿𝘂𝘀𝘁 𝘁𝗵𝗲 𝗔𝗻𝘀𝘄𝗲𝗿𝘀.


You optimized your pipelines, warehouses, and data stack.
You invested in Snowflake, Databricks, dbt, and orchestration tools.
You assumed that would make AI work in real business workflows.

𝗜𝘁 𝗱𝗶𝗱𝗻’𝘁. 𝗔𝗻𝗱 𝗶𝘁 𝘄𝗼𝗻’𝘁.

Because AI doesn’t fail from lack of data. It fails from lack of context.

𝗜𝗻 𝘁𝗵𝗶𝘀 𝘄𝗲𝗯𝗶𝗻𝗮𝗿, 𝘄𝗲 𝗲𝘅𝗽𝗼𝘀𝗲𝗱 𝘁𝗵𝗲 𝗶𝗻𝗰𝗼𝗻𝘃𝗲𝗻𝗶𝗲𝗻𝘁 𝘁𝗿𝘂𝘁𝗵:

• Why “data access” ≠ “operational understanding”
• How missing context erodes trustand destroys adoption
• What it really takes to move from dashboards to autonomous agents that explain, verify, and act
• Why current stacks optimize for storage and compute—not decision quality

🚫 𝗦𝘁𝗼𝗽 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲.
✅ Start embedding business context into the foundation of your data systems.

If your AI can’t reason through a workflow like your top analyst, then all you’ve built is a very expensive tool.

https://lnkd.in/gnNP5JXJ

Product + Growth @ DataGol | AI | Financial Transformation | Defi

Thank you to everyone who joined us yesterday. If you missed the session, or are based in Europe, we will be hosting it again next week for our European partners.

Most AI projects do not fail because the models are weak.
They fail because the teams are focusing on infrastructure plumbing and get stuck with optimizing the infra. 

In our recent DataGOL webinar, we broke down a pattern we keep seeing:
Teams invest heavily in pipelines, warehouses, and data stack optimization.
Then they expect AI to reason, explain, and act inside real workflows.

It cannot.
So trust erodes.
Answers get discarded.
Momentum stalls.

We covered:

How missing business context destroys trust in AI outputs

The difference between “data access” and “operational understanding”

What it actually takes to move from dashboards to agents that can explain, verify, and take action

Why Snowflake/Databricks-style stacks optimize storage and compute, but not decision quality

The shift is simple to describe, but hard to execute:
Stop building more AI infrastructure.

Start building a #ConextOS that understands the business.
Reach out to us for 1:1 AI consulting session.

#AIinProduction #EnterpriseAI #DataPlatforms #AIInfrastructure #AgenticAI #DataGOL #OperationalIntelligence

Focus on AI Outcomes, Not Infrastructure

𝗠𝗼𝘀𝘁 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗮𝗶𝗹 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘆𝗼𝘂𝗿 𝘁𝗼𝗼𝗹𝘀 𝗱𝗼 𝗻𝗼𝘁 𝘀𝗽𝗲𝗮𝗸 𝘁𝗼 𝗲𝗮𝗰𝗵 𝗼𝘁𝗵𝗲𝗿.

You add “AI on top” of a broken stack.

I see this again and again when I talk with B2B founders and operators.

- The story is always the same.
- The product helps clients save time.
- The team still drowns in manual work.

𝗛𝗲𝗿𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗳𝗼𝘂𝗻𝗱𝗲𝗿 𝗜 𝘄𝗼𝗿𝗸𝗲𝗱 𝘄𝗶𝘁𝗵.
His SaaS app cuts 80–90% of founder time in fundraising.
- Strong value.
- Happy users.

𝗜𝗻𝘀𝗶𝗱𝗲 𝗵𝗶𝘀 𝗼𝘄𝗻 𝘁𝗲𝗮𝗺, 𝘄𝗼𝗿𝗸 𝗹𝗼𝗼𝗸𝗲𝗱 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀:
• Call transcripts land in the CRM
• Team members read each call
• They pull out fund mandate, stage, check size
• They paste data into fields, one by one
• Boards in Asana do not move unless someone drags cards.
• Research for each investor runs “one by one by one” because bulk tools hallucinate.

𝗧𝗼𝗼𝗹 𝗰𝗼𝘀𝘁 𝗴𝗿𝗼𝘄𝘀 𝘄𝗶𝘁𝗵 𝗲𝘃𝗲𝗿𝘆 𝗻𝗲𝘄 𝗲𝗺𝗮𝗶𝗹 𝘀𝗲𝗮𝘁 𝗮𝗻𝗱 𝗰𝗮𝗹𝗲𝗻𝗱𝗮𝗿.
- The service scales.
- The engine does not.
- The answer for him was not a “new all-in-one platform.”
- He needed one intelligent system between all his tools.
That is where we brought in @DataGOL as "AI Native Intelligence & Execution Platform"

Here is how we implemented it:

→ 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗹𝗮𝘆𝗲𝗿
- Transcripts go in.
- AI pulls structured fields with a shared language.
- Data flows back into the CRM without a human hand.

→ 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗯𝗿𝗼𝗸𝗲𝗿
- CRM actions trigger status changes.
- Project tools move on their own.
- No one drags cards all day.

→ 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗲𝗻𝗴𝗶𝗻𝗲
• Multi-agent AI runs deep research in bulk.
• Scores and ranks targets.
• Shows sources so you can trust the results.

→ 𝗦𝘆𝘀𝘁𝗲𝗺 𝗼𝗳 𝗮𝗰𝘁𝗶𝗼𝗻
- Once the middleware works well, you replace old tools
- One by one
- With less risk and clear ROI proof.

Fast ROI comes from one AI Native layer that makes your whole stack work like one product.

DataGOL

𝗧𝗵𝗲 𝗘𝗻𝗱 𝗼𝗳 𝘁𝗵𝗲 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 𝗕𝗼𝗮𝗿𝗱 𝗗𝗲𝗰𝗸 𝗦𝗰𝗿𝗮𝗺𝗯𝗹𝗲: 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗗𝗮𝘁𝗮𝗚𝗢𝗟'𝘀 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗔𝗴𝗲𝗻𝘁

There's a shift happening in how SaaS based finance teams interact with their data.
I'm calling it 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗙𝗶𝗻𝗮𝗻𝗰𝗲.

𝗧𝗵𝗶𝗻𝗸: AI that doesn't just tell you what happened, but identifies exactly why it happened. In seconds.

𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲:
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 = "Revenue down 12%. Margins compressed."
𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 = "May COGS spike in Product Line X. Drove 73% of the margin erosion."
One reports the score. The other finds the insights that lost the game.

𝗬𝗼𝘂𝗿 𝗯𝗼𝗮𝗿𝗱 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝗱𝗼𝗻'𝘁 𝘄𝗮𝗻𝘁 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱.
They want to know why Q2 margins spiked while Q3 collapsed.
And right now?
Your data team finds out what was presented... after the meeting.

𝗜'𝘃𝗲 𝘀𝗲𝗲𝗻 𝗶𝘁 𝗶𝗻 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝘀𝗲𝗰𝘁𝗼𝗿𝘀.
CFO builds the board deck. The data team scrambles to reverse-engineer the "why" question two weeks later.

𝗠𝗼𝘀𝘁 𝘁𝗲𝗮𝗺𝘀 𝗱𝗼 𝗼𝗻𝗲 𝗼𝗳 𝘁𝘄𝗼 𝘁𝗵𝗶𝗻𝗴𝘀:
→ Hire expensive consultants for ad-hoc analyses ($50k+ per engagement)
→ Duct-tape BI tools that still need manual SQL and weeks of turnaround
Neither gets you diagnostic depth in real-time.

𝗗𝗮𝘁𝗮𝗚𝗢𝗟'𝘀 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗔𝗴𝗲𝗻𝘁 𝘄𝗮𝘀 𝗯𝘂𝗶𝗹𝘁 𝘁𝗼 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝘁𝗵𝗮𝘁 𝗲𝗻𝘁𝗶𝗿𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄.
Here's what happens now:
✓ 𝗔𝘀𝗸 𝗶𝗻 𝗽𝗹𝗮𝗶𝗻 𝗘𝗻𝗴𝗹𝗶𝘀𝗵: "What drove the margin variance in Q3?"
✓ 𝗔𝗴𝗲𝗻𝘁 𝗰𝗼𝗻𝗳𝗶𝗿𝗺𝘀 your Chart of Accounts before calculating (human-in-the-loop)
✓ 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲𝘀 𝘁𝗵𝗲 𝗱𝗿𝗶𝘃𝗲𝗿𝘀—product line, customer segment, cost category
✓ Suggests the 𝗻𝗲𝘅𝘁 𝗱𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 before you ask it
✓ 𝗦𝗵𝗼𝘄𝘀 𝗶𝘁𝘀 𝘄𝗼𝗿𝗸—Python code, logic trail, audit-ready transparency

𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀:
→ From raw NetSuite chaos → boardroom-ready insight in under 60 seconds.
→ Full transparency into how every number was calculated.
→ No more "black box" AI guessing.

𝗡𝗼 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱. 𝗡𝗼 𝗦𝗤𝗟. 𝗡𝗼 𝗽𝗶𝘃𝗼𝘁 𝘁𝗮𝗯𝗹𝗲 𝗮𝗿𝗰𝗵𝗮𝗲𝗼𝗹𝗼𝗴𝘆.𝗝𝘂𝘀𝘁 𝗮𝘀𝗸.
Diagnostic Finance isn't about prettier dashboards.
𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝗮𝗻𝘀𝘄𝗲𝗿𝗶𝗻𝗴 "𝘄𝗵𝘆" 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲 𝗯𝗼𝗮𝗿𝗱 𝗮𝘀𝗸𝘀.

Are you still reporting what happened? Or are you ready to diagnose why?

DataGOL

𝗗𝗔𝗧𝗔 𝗦𝗖𝗜𝗘𝗡𝗖𝗘 𝗔𝗚𝗘𝗡𝗧 stands out as it delivers real time business ready insights directly embedded into your finance, sales, product workflows.

It's deeply integrated, maintains context across your data, proactive and action oriented, solving the "𝘄𝗵𝗼 𝗼𝘄𝗻𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮" problem and driving decisions.

𝗜𝗧'𝗦 𝗔𝗟𝗪𝗔𝗬𝗦 𝗢𝗡, 𝗖𝗢𝗡𝗧𝗘𝗫𝗧 𝗔𝗪𝗔𝗥𝗘, 𝗘𝗫𝗘𝗖𝗨𝗧𝗜𝗢𝗡 𝗙𝗢𝗖𝗨𝗦𝗘𝗗 𝗗𝗔𝗧𝗔 𝗣𝗔𝗥𝗧𝗡𝗘𝗥

𝗬𝗢𝗨 𝗖𝗔𝗡𝗡𝗢𝗧 𝗠𝗜𝗦𝗦 𝗧𝗛𝗜𝗦!!

T𝗛𝗘 𝗕𝗘𝗦𝗧 𝗗𝗔𝗧𝗔 𝗦𝗖𝗜𝗘𝗡𝗖𝗘 𝗔𝗚𝗘𝗡𝗧 - Assisting your Finance, Product and Sales Team. 𝗔𝗹𝘄𝗮𝘆𝘀 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 - Responding within minutes with insights that were not possible before.

Whether you are analyzing revenue trends, sales or.. etc..

𝗦𝗶𝘁𝘁𝗶𝗻𝗴 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝗲𝘁 - directly creating and accessing data pipelines, managing context and creating intelligence from your data.

Want a personalized 𝗧𝗥𝗜𝗔𝗟 Comment below.

#DataScience #Transparency #TrustInAI #DataGOL

𝟭𝟬𝗠 𝗨𝘀𝗲𝗿𝘀. 𝟴𝘅 𝗩𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻. 𝗛𝗲𝗿𝗲’𝘀 𝗪𝗵𝗮𝘁 𝗪𝗮𝘀 𝗠𝗶𝘀𝘀𝗶𝗻𝗴.
We looked at a “successful” gaming platform: 10M active free-to-play users, super sticky… and still stuck at ~8x exit.

I saw this in a gaming client at @DataGOL
- Free-to-play.
- Very sticky.
- Exit stuck at 8x.

𝗪𝗵𝘆?
User data looked nice on a slide.
Inside, they had HTML + vibes.
- No real events.
- No cohorts.
- No path from “player” to “payer”.

𝗛𝗶𝗴𝗵 𝗗𝗔𝗨, 𝗹𝗼𝘄 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴.

The truth is:
- You don’t scale from 8x to 20x with more features.
- You scale with a better brain.

𝗛𝗲𝗿𝗲 𝗶𝘀 𝗵𝗼𝘄 𝗜 𝘁𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁 𝗻𝗼𝘄:

𝗙𝗶𝗿𝘀𝘁, 𝗶𝗱𝗲𝗻𝘁𝗶𝘁𝘆 + 𝗶𝗻𝘁𝗲𝗻𝘁.
- One lake for all data.
- One “virtual CRM” on top.
- One clean stream of events from product and ads.
Then I stop saying “users”.
I see segments, with clear intent.

𝗡𝗲𝘅𝘁, 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗹𝗶𝗻𝗸𝘀.
- Every campaign message flows into the same brain.
- Every deposit and payment links back to a touch.
- I can ask “what creates LTV?” and get a clear view.
Most tools stop here.
They show reports, not answers.

𝗟𝗮𝘀𝘁, 𝗮𝗰𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗮𝗴𝗲𝗻𝘁𝘀.
- Small Python agents watch each player.
- They look at recency, frequency, and value.
- They trigger one next action, not ten.
Email, offer, KYC nudge, in-game push.
Personal, fast, cheap.

𝗥𝗲𝘀𝘂𝗹𝘁:
- Lower cost to reactivate.
- Less waste in marketing.
- More users move to deposit, subscribe, or compete.

Teams say “we have a monetization issue”.
I see an intelligence gap.

𝗜𝗳 𝘆𝗼𝘂 𝗵𝗮𝗱 𝟭𝟬𝗠 𝘂𝘀𝗲𝗿𝘀 𝗻𝗲𝘅𝘁 𝗺𝗼𝗻𝘁𝗵, 𝘄𝗵𝗮𝘁 𝘀𝘆𝘀𝘁𝗲𝗺 𝘄𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝘁𝗿𝘂𝘀𝘁 𝘁𝗼 𝗴𝘂𝗶𝗱𝗲 𝗲𝘃𝗲𝗿𝘆 𝗺𝗼𝘃𝗲?

If you want to build that intelligence for your product, @DataGOL helps teams build the full stack AI agents.

𝗛𝗼𝘄 𝗮𝗿𝗲 𝘆𝗼𝘂 𝘂𝘀𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘁𝗼𝗱𝗮𝘆 𝘁𝗼 𝗱𝗿𝗶𝘃𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗿𝗲𝗽𝗼𝗿𝘁𝘀?

𝗧𝗵𝗲 𝗣𝗠 𝗠𝗼𝗮𝘁 𝗧𝗵𝗮𝘁 𝗔𝗜 𝗖𝗮𝗻'𝘁 𝗥𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗲

There's a role emerging in AI-native companies that doesn't have a name yet.
It's the "Judgment PM".

𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲:
𝗧𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁 𝗣𝗠 = Discovery → prioritization → requirements → delivery → iterate
𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝗣𝗠 = Turns ambiguity into irreversible decisions that machines can't make

𝗙𝗼𝗿 𝗮 𝗱𝗲𝗰𝗮𝗱𝗲, 𝘆𝗼𝘂𝗿 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 𝘄𝗮𝘀 𝗵𝘂𝗺𝗮𝗻 𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁.
Ship faster. Write more PRDs. Run more sprints.
That era is over.
In an AI-native world, execution speed is no longer scarce.
Judgment is. Context is. Trust is.

𝗔𝗻𝗱 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄, most product teams are responding the wrong way.
More gates. More committees. More process.
That's not a strategy. That's organizational scar tissue.

𝗧𝗵𝗲 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝗣𝗠 𝗱𝗼𝗲𝘀 𝘁𝗵𝗿𝗲𝗲 𝘁𝗵𝗶𝗻𝗴𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆:
✓ 𝗖𝗿𝗲𝗮𝘁𝗲𝘀 𝗰𝗹𝗮𝗿𝗶𝘁𝘆 — turns chaos into a sharp decision: "We're doing X, not Y, because Z"
✓ 𝗕𝘂𝗶𝗹𝗱𝘀 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 — treats eval as a first-class product capability ("Evaluation is the new UX")
✓ 𝗗𝗲𝘀𝗶𝗴𝗻𝘀 𝘁𝗿𝘂𝘀𝘁 — ships explanations, confidence scores, and undo/rollback as core features

𝗔𝗜 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝘀 𝘁𝗵𝗲 𝘀𝘂𝗿𝗳𝗮𝗰𝗲 𝗮𝗿𝗲𝗮 𝗼𝗳 𝘄𝗵𝗮𝘁 𝗰𝗮𝗻 𝗯𝗲 𝗯𝘂𝗶𝗹𝘁.
✓ Your operating model must increase the surface area of what can be validated.
✓ The PMs who figure this out will own the decisions that matter.
✓ The ones who don't will keep writing PRDs that machines could generate in 30 seconds.

Comment if you're already making this shift?

DataGOL = 𝗟𝗼𝗼𝗸𝗲𝗿 + 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗶𝗻 𝗼𝗻𝗲 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺

I've had the same conversation with 5 different performance marketing teams this Month.

They all said some version of:
"𝗜 𝗰𝗮𝗻'𝘁 𝗲𝘅𝗽𝗹𝗮𝗶𝗻 𝘁𝗼 𝗺𝘆 𝗖M𝗢 𝘄𝗵𝘆 𝗼𝘂𝗿 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝗱𝗼𝗻'𝘁 𝗺𝗮𝘁𝗰𝗵. 𝗜 𝗱𝗼𝗻'𝘁 𝗲𝘃𝗲𝗻 𝗸𝗻𝗼𝘄 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗰𝗼𝗺𝗲𝘀 𝗳𝗿𝗼𝗺."
This isn't a data quality problem. It's a trust problem.
And it's driving a shift from "𝗕𝗹𝗮𝗰𝗸-𝗕𝗼𝘅 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀" to what I'm calling "𝗧𝗿𝘂𝘀𝘁-𝗙𝗶𝗿𝘀𝘁 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀".

Here's the difference:
𝗕𝗹𝗮𝗰𝗸-𝗕𝗼𝘅 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 = Dashboards you can't trace. Numbers you can't validate. Feature requests for every custom query.
𝗧𝗿𝘂𝘀𝘁-𝗙𝗶𝗿𝘀𝘁 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 = Full visibility into SQL structure. Complete data lineage. Slice data any way you need, instantly.

𝗬𝗼𝘂𝗿 𝗯𝗼𝗮𝗿𝗱 𝗮𝘀𝗸𝘀 𝘄𝗵𝘆 𝗖𝗔𝗖 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝟮𝟬%.
- You pull the dashboard. The number's there.
- But you can't explain how it was calculated. You can't trace it back to the source.
You're defending a number you don't fully understand.

𝗠𝗼𝘀𝘁 𝘁𝗲𝗮𝗺𝘀 𝗱𝗼 𝗼𝗻𝗲 𝗼𝗳 𝘁𝘄𝗼 𝘁𝗵𝗶𝗻𝗴𝘀:
→ Accept the black box (and the trust gap)
→ Build custom solutions (and burn budget on data engineers instead of User Acquisition )
We built DataGOL "Data Science Agent" to eliminate that trade-off.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗼𝘂𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗴𝗲𝘁𝘀 𝗻𝗼𝘄:
✓ 𝗙𝘂𝗹𝗹 𝗦𝗤𝗟 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆—trace any metric to source in seconds
✓ 𝗦𝗹𝗶𝗰𝗲 𝗮𝗻𝗱 𝗽𝗶𝘃𝗼𝘁 𝗱𝗮𝘁𝗮 𝗶𝗻 𝗰𝗼𝘂𝗻𝘁𝗹𝗲𝘀𝘀 𝘄𝗮𝘆𝘀—no feature requests
✓ 𝗔𝗜-𝗻𝗮𝘁𝗶𝘃𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 (imagine Looker with ChatGPT built in) - AI and agent capabilities directly into the workflow
✓ 𝗖𝘂𝘀𝘁𝗼𝗺 𝗰𝗼𝗵𝗼𝗿𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗿𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 - to understand nuanced user behaviour

𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? Budget that used to fund data validation now goes directly into user acquisition.

The teams that figure this out will outspend competitors where it matters.

Are you still working with black-box tools? Or have you made the shift to trust-first?

#DataScience #Transparency #DataAnalytics #TrustInAI #DataGOL

𝗪𝗵𝘆 𝟱𝟲% 𝗼𝗳 𝗖𝗘𝗢𝘀 𝗦𝗲𝗲 𝗡𝗼 𝗔𝗜 𝗥𝗲𝘁𝘂𝗿𝗻𝘀

𝗧𝗵𝗲𝗿𝗲'𝘀 𝗮 𝗳𝘂𝗻𝗻𝘆 𝘁𝗵𝗶𝗻𝗴 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄.
Leadership thinks the infrastructure is in place.
Employees say it isn't even close.

𝗧𝗵𝗲 𝗱𝗮𝘁𝗮 𝗼𝗻 𝘁𝗵𝗶𝘀 𝗶𝘀 𝘀𝘁𝗿𝗶𝗸𝗶𝗻𝗴.
81% of the C-suite believe a clear AI policy exists in their organization.
Only 28% of employees agree.
That's a 53-point perception gap.

𝗢𝗻 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴? 𝟴𝟭% 𝗼𝗳 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝗿𝗲𝗽𝗼𝗿𝘁 𝗯𝗲𝗶𝗻𝗴 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝗻 𝗔𝗜.
Only 27% of individual contributors say the same.
On tool access? 80% vs. 32%.

𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝘁𝗵𝗶𝗻𝗴...
This isn't a communication problem. It's a feedback loop failure.
If leadership believes the foundation is already built, they remain blind to the bottlenecks preventing teams from moving beyond basic prompting.

𝗧𝗵𝗲 𝗰𝗼𝗻𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀 𝗮𝗿𝗲 𝘀𝗵𝗼𝘄𝗶𝗻𝗴 𝘂𝗽 𝗶𝗻 𝘁𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀.
Workday found that 37% of AI time savings are immediately lost to rework, employees cleaning up outputs that weren't good enough the first time.

𝗧𝗵𝗮𝘁'𝘀 𝗻𝗼𝘁 𝗮𝗻 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗮𝘁'𝘀 𝗮𝗻 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗺𝗮𝘀𝗾𝘂𝗲𝗿𝗮𝗱𝗶𝗻𝗴 𝗮𝘀 𝗼𝗻𝗲.
Meanwhile, PWC data shows 56% of CEOs report no significant financial return from AI.
But 12%—the "Vanguard"—are seeing simultaneous cost and revenue gains.
The difference? They're 2.6x more likely to have embedded AI into core processes, not peripheral tasks.

𝗠𝘆 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻:
- The AI winners of 2026 won't have better models.
- They'll have closed the 53-point gap between what the boardroom believes and what the frontline experiences.
- The technology is not the bottleneck.
- The misalignment is.
𝗔𝗿𝗲 𝘆𝗼𝘂 𝘀𝗲𝗲𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗽𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 𝗴𝗮𝗽 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻? 𝗢𝗿 𝗵𝗮𝘃𝗲 𝘆𝗼𝘂 𝗳𝗼𝘂𝗻𝗱 𝗮 𝘄𝗮𝘆 𝘁𝗼 𝗰𝗹𝗼𝘀𝗲 𝗶𝘁?

𝗗𝗮𝘁𝗮𝗚𝗢𝗟: 𝗧𝗵𝗲 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗘𝗱𝗴𝗲 𝗧𝗵𝗮𝘁 𝗣𝘂𝘁𝘀 𝗠𝗮𝗿𝗸𝗲𝘁𝗲𝗿𝘀 𝗶𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹

I keep hearing the same thing from performance marketers this month:
"𝗜 𝗱𝗼𝗻'𝘁 𝘁𝗿𝘂𝘀𝘁 𝘁𝗵𝗲 𝗔𝗜 𝗼𝘂𝘁𝗽𝘂𝘁𝘀 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗜 𝗰𝗮𝗻'𝘁 𝘀𝗲𝗲 𝘄𝗵𝗮𝘁'𝘀 𝗳𝗲𝗲𝗱𝗶𝗻𝗴 𝘁𝗵𝗲𝗺."

Here's the interesting part: 𝘁𝗵𝗲𝘀𝗲 𝘀𝗮𝗺𝗲 𝘁𝗲𝗮𝗺𝘀 𝗮𝗿𝗲 𝗮𝗹𝘀𝗼 𝘀𝗮𝘆𝗶𝗻𝗴 𝘁𝗵𝗲𝘆 𝗰𝗮𝗻'𝘁 𝗹𝗶𝘃𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗔𝗜.
𝗧𝘄𝗼 𝗼𝗽𝗽𝗼𝘀𝗶𝗻𝗴 𝗱𝗲𝗺𝗮𝗻𝗱𝘀. 𝗦𝗮𝗺𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻.
The next analytics battleground isn't AI vs. no-AI.
It's transparent AI vs. blackbox AI.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜'𝗺 𝘀𝗲𝗲𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝘀𝘁𝗮𝗰𝗸 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻:
𝗧𝗵𝗲 𝗼𝗹𝗱 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵:
→ AI tools bolted on afterward (no visibility into logic, no context and no data governance)
→ BI tools for dashboards (rigid, predefined models)
→ Custom data teams for flexibility (expensive)


𝗧𝗵𝗲 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵:
→ Decision Traces, ContextOS, Reasoning, and data infrastructure in one layer
→ Custom cohort analysis without engineering tickets
→ SQL visible behind every insight
Tools like @DataGOL are leaning into this, their positioning is essentially
"BI 𝘄𝗶𝘁𝗵 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗼𝘃𝗲𝗿 𝗶𝘁, 𝗯𝘂𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝘄𝗵𝗮𝘁'𝘀 𝗳𝗲𝗲𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗜."

𝗧𝗵𝗲 𝘀𝗶𝗴𝗻𝗮𝗹 𝘂𝗻𝗱𝗲𝗿𝗻𝗲𝗮𝘁𝗵 𝘁𝗵𝗶𝘀 𝘀𝗵𝗶𝗳𝘁:
→ Performance teams are done choosing between speed and trust.
→ They want to ask natural-language questions and see the query logic.
→ They want AI-generated insights and the ability to check its homework.

𝗙𝗼𝗿 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗺𝗮𝗿𝗸𝗲𝘁𝗲𝗿𝘀 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝘀𝘁𝗮𝗰𝗸, 𝘁𝗵𝗿𝗲𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝗻𝗼𝘄:
✓ Can you cohort by custom events, not just default metrics?
✓ Can you see the underlying SQL structure?
✓ Can you trust insights without a data team translating?

The teams that solve this will move faster and spend less on internal data infrastructure.
The ones that don't will keep paying the "talent tax" just to get answers they're not sure they believe.

Are you already seeing this convergence in your stack? Or are AI and transparency still living in separate tools?

#DataScience #Transparency #DataAnalytics #TrustInAI #DataGOL

General Management and Business Development expert with proven experience in management and negotiation of high value strategic contracts as well as managing large business units from Operations to Business Development. Proven ability in driving business growth and diversification strategies. I have strong management experience in the Middle East, Asia and Europe, managing large multi cultural teams working in Oil & Gas environments.

Experience in Oil Rig construction & Offshore drilling and providing medical services to Oil & Gas clients.

I am also fluent in the Chinese language (Mandarin)

You can contact me at: stevenbudgen@hotmail.co.uk

Specialties: General Management, P&L Management, Oil & Gas Sector, Business Development, Marketing Strategy, Marketing Communications, Drilling, Shipbuilding, Medical Services, Corporate sector, Operations, Management of large multi cultural teams, Tender Submissions, E Marketing, Customer Liason, Brand Identities, Direct Marketing, Managing Marketing Performance, Analysis & evaluation, Behavioural Aspects of Marketing.

Steven Budgen

𝗩𝗲𝗻𝗲𝘇𝘂𝗲𝗹𝗮: 𝗙𝗿𝗼𝗺 𝗢𝗶𝗹 𝗣𝗼𝘄𝗲𝗿𝗵𝗼𝘂𝘀𝗲 𝘁𝗼 𝗘𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗖𝗼𝗹𝗹𝗮𝗽𝘀𝗲

Venezuela’s story is one of the most dramatic economic reversals in modern history. Few nations have fallen from extraordinary wealth to widespread hardship so quickly. What makes the collapse especially tragic is that it wasn’t caused by a lack of resources, but by how those resources were managed.

In the 1920s, Venezuela struck oil and everything changed. Almost overnight, it became one of the world’s leading oil producers. Oil wealth transformed a largely rural nation into an energy powerhouse. For decades, living standards rose, infrastructure expanded and a strong middle class emerged. By the 1970's and early 1990's, Venezuela ranked among the richest countries in Latin America and was once considered the third wealthiest nation in the Western Hemisphere. Even as late as 2012, shortly before Hugo Chávez’s death, Venezuela was publicly praised by Bernie Sanders for the ''American Dream'' properties.

Oil revenue funded highways, universities, healthcare & booming consumer spending. Caracas became a symbol of wealth. But beneath the surface, the economy became dangerously dependent on oil exports whilst other industries declined. When oil prices were high, the system worked. When they fell, weaknesses were exposed.

The turning point came in 1998 with the election of Hugo Chávez. Promising to empower the poor & challenge elites, he used oil revenue to fund vast social programs. In the short term, poverty fell & access to services improved. However, long term stability was sacrificed. Industries were nationalised, price controls imposed, foreign investment fled & corruption prevailed.

After Chávez’s death in 2013, Nicolás Maduro inherited a fragile economy & pushed it into collapse. Hyperinflation destroyed the currency, shortages became widespread and millions fled the country. Sanctions, authoritarian rule and continued mismanagement accelerated the decline. Venezuela became a warning of how resource wealth, combined with poor governance, can destroy prosperity.

In January 2026, the crisis reached a new peak when Nicolás Maduro was captured in a bloodless extraction operation. Donald Trump announced the US would temporarily oversee Venezuela’s transition, sparking speculation over America’s interest in Venezuela’s vast oil & rare earth resources. Trump stated US oil companies would help fund reconstruction through energy extraction.

This has fuelled debate over whether increased supply could pressure oil prices or instead boost US energy companies. Several industries could benefit and uncomfortable, instability often creates opportunity. Personally, I believe oil and oil related companies will be major winners in 2026, with or without Venezuelan supply.

From a humanitarian perspective, the hope remains, Venezuela’s future is rebuilt in the interests of its people.

First step to securing your future is investing.

https://lnkd.in/ddva7THi

Elite Popular Investor at eToro / Private Investor. Host of Achieving Alpha Podcast and Approachable Investor Channel.

𝟭𝟬 𝗙𝗮𝘀𝗰𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 𝗔𝗯𝗼𝘂𝘁 𝗚𝗿𝗲𝗲𝗻𝗹𝗮𝗻𝗱

Recently, U.S. President Donald Trump has again drawn global attention by suggesting the United States should acquire Greenland. The proposal has been firmly rejected by both Denmark and Greenland, with criticism from NATO members. Trump argues that Greenland’s strategic Arctic location is vital to U.S. national security, particularly as melting ice opens new shipping routes and intensifies competition with Russia and China. 

He has also pointed to Greenland’s mineral wealth and its potential role in missile defence. At one point, Trump even threatened tariffs on European NATO allies to apply pressure in negotiations.

At the 2026 World Economic Forum in Davos, Trump claimed he and NATO Secretary General Mark Rutte had agreed on a “framework” for future Arctic cooperation, withdrawing tariff threats as a result. While Trump framed this as progress, Denmark and NATO officials stressed that Greenland’s sovereignty was never up for negotiation and discussions focused on broader strategic cooperation rather than ownership.

Greenland often feels like one of the most mysterious places on Earth. Vast, remote, and shaped by ice, it plays a much bigger role in global affairs than its population suggests.

𝟭. 𝗧𝗵𝗲 𝗪𝗼𝗿𝗹𝗱’𝘀 𝗟𝗮𝗿𝗴𝗲𝘀𝘁 𝗜𝘀𝗹𝗮𝗻𝗱

Greenland is the largest island on Earth, covering around 2.16 million square kilometres.

𝟮. 𝗗𝗼𝗺𝗶𝗻𝗮𝘁𝗲𝗱 𝗯𝘆 𝗜𝗰𝗲

Around 80 percent of Greenland is covered by ice, holding enough water to dramatically affect global sea levels.

𝟯. 𝗔 𝗦𝗺𝗮𝗹𝗹 𝗣𝗼𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻

Only about 57,000 people live in Greenland, mostly along the coast.
4. Rising Sea Levels

If fully melted, Greenland’s ice sheet could raise sea levels by roughly seven metres.

𝟱. 𝗡𝗼 𝗜𝗻𝘁𝗲𝗿𝗰𝗶𝘁𝘆 𝗥𝗼𝗮𝗱𝘀

There are no roads between towns. Travel is by air, sea, or in some cases, dog sled.

𝟲. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗯𝘂𝘁 𝗟𝗶𝗻𝗸𝗲𝗱 𝘁𝗼 𝗗𝗲𝗻𝗺𝗮𝗿𝗸

Greenland governs itself domestically but relies on Denmark for defence and foreign policy.

𝟳. 𝗠𝗶𝗱𝗻𝗶𝗴𝗵𝘁 𝗦𝘂𝗻 𝗮𝗻𝗱 𝗣𝗼𝗹𝗮𝗿 𝗡𝗶𝗴𝗵𝘁

Parts of Greenland experience months of continuous daylight in summer and darkness in winter.

𝟴. 𝗥𝗮𝗽𝗶𝗱 𝗜𝗰𝗲 𝗟𝗼𝘀𝘀

 Ice loss is accelerating, making Greenland central to climate research.

𝟵. 𝗙𝗶𝘀𝗵𝗶𝗻𝗴 𝗗𝗼𝗺𝗶𝗻𝗮𝘁𝗲𝘀 𝗘𝘅𝗽𝗼𝗿𝘁𝘀

 Around 90 percent of exports come from fishing and seafood.

𝟭𝟬. 𝗥𝗶𝗰𝗵 𝗠𝗶𝗻𝗲𝗿𝗮𝗹 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀

Greenland holds significant rare earth minerals, increasing its geopolitical importance.

Together, these facts show Greenland is far more than a frozen wilderness. It is a place of growing strategic, environmental, and political significance and the global tug of war over it is far from over.

𝗚𝗼𝗼𝗱 𝗦𝘁𝗮𝗿𝘁 𝘁𝗼 𝘁𝗵𝗲 𝗬𝗲𝗮𝗿 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗣𝗼𝘁𝗳𝗼𝗹𝗶𝗼 📈

The year has started strongly, with the portfolio up around +𝟴% 𝗬𝗧𝗗
after a tough end to 2025. Markets have remained volatile, but that’s often where opportunity appears. A mix of patience, selective risk taking and sticking to process has paid off so far. Returns are positive, drawdowns have been contained, and conviction is higher than it was heading into the year. It’s also been a particularly good start for crypto and some of the newer stock positions.

𝗡𝗲𝘄 𝗣𝗼𝘀𝗶𝘁𝗶𝗼𝗻𝘀 (𝗖𝗵𝗮𝗿𝘁𝗲𝗿 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 & 𝗔𝘀𝗵𝗺𝗼𝗿𝗲)

New Positions in Charter Communications and Ashmore Group may look very different, but that contrast is intentional. Charter remains a misunderstood business. While cord cutting is real, broadband is still essential infrastructure. Cash flows are strong, capital intensity is easing and market pessimism feels overdone relative to fundamentals.

Ashmore Group offers exposure to emerging market debt at a point where expectations are extremely low. Asset managers tend to be most attractive near cycle lows, and Ashmore’s balance sheet strength and operating leverage make it well positioned if EM sentiment improves. The position has benefited from the re rating of Venezuelan sovereign bonds, with the holding up nearly 26% over the past two weeks following the capture of Maduro.

𝟭% 𝗥𝘂𝗹𝗲 𝗖𝗼𝗺𝗶𝗻𝗴 𝗶𝗻𝘁𝗼 𝗘𝗳𝗳𝗲𝗰𝘁 1️⃣

A key change this year has been the introduction of a 1% minimum position size. Smaller positions often add complexity without meaningfully contributing to returns. This shift forces higher conviction, better research, and clearer decision making. It’s still being implemented and should be fully in place over the coming weeks.

𝗙𝗲𝗱 𝗨𝗻𝗹𝗶𝗸𝗲𝗹𝘆 𝘁𝗼 𝗖𝘂𝘁 & 𝗜𝗻𝗳𝗹𝗮𝘁𝗶𝗼𝗻 🏛️

Expectations for near term rate cuts continue to fade. Inflation remains sticky, labour markets are resilient, and growth is holding up. The Fed appears comfortable with a higher for longer stance, shifting investor focus back to balance sheets, pricing power, and real cash generation. A pause at the next FOMC meeting looks likely.

𝗪𝗵𝗮𝘁 𝗠𝗲𝘁𝗮𝗹𝘀 𝗔𝗿𝗲 𝗧𝗲𝗹𝗹𝗶𝗻𝗴 𝗨𝘀 🥇

Gold and silver are back near all time highs, signalling continued demand for safety and growing concerns around fiat debasement. The broader rise in commodities often appears late cycle, alongside inflation risk, geopolitical uncertainty and uneven growth.

𝟮𝟬𝟮𝟲 – 𝘁𝗵𝗲 𝗬𝗲𝗮𝗿 𝗼𝗳 𝗖𝗿𝘆𝗽𝘁𝗼 🪙

2026 could be a pivotal year for crypto. Post halving dynamics, improving regulation, and rising institutional participation may set the stage for another major cycle. Volatility will remain extreme, but crypto continues to act as a high beta expression of liquidity and risk appetite. If conditions align, the asymmetric upside could once again come into focus.

𝗙𝘂𝗹𝗹 𝗲𝗧𝗼𝗿𝗼 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼:
 https://lnkd.in/ddva7THi

𝗧𝗵𝗲 𝗣𝘀𝘆𝗰𝗵𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗘𝗱𝗴𝗲 𝗶𝗻 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗻𝗴 - 𝗘𝗹𝗲𝘃𝗮𝘁𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗠𝗶𝗻𝗱𝘀𝗲𝘁 |‪ 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝘄𝗶𝘁𝗵 𝗠𝗶𝗺𝗶 𝗛𝗼 (𝗞𝗼𝗿𝗮 𝗧𝗿𝗮𝗱𝗲𝘀)

𝗗𝗲𝗮𝗿 𝗩𝗮𝗹𝘂𝗲𝗱 𝗟𝗶𝘀𝘁𝗲𝗻𝗲𝗿,

For 𝗘𝗽𝗶𝘀𝗼𝗱𝗲 𝟱𝟱 of the 𝗔𝗰𝗵𝗶𝗲𝘃𝗶𝗻𝗴 𝗔𝗹𝗽𝗵𝗮 𝗣𝗼𝗱𝗰𝗮𝘀𝘁, I was delighted to interview a good friend of mine, Mimi Ho who is also known as Koratrades on eToro.

𝗠𝗶𝗺𝗶 𝗛𝗼 𝗶𝘀 𝗮𝗻 𝗘𝗹𝗶𝘁𝗲 𝗣𝗿𝗼 𝗜𝗻𝘃𝗲𝘀𝘁𝗼𝗿 𝗼𝗻 𝗲𝗧𝗼𝗿𝗼 𝗮𝗻𝗱 𝗳𝗼𝗰𝘂𝘀𝗲𝘀 𝗼𝗻 𝗖𝗿𝘆𝗽𝘁𝗼, 𝘁𝗲𝗰𝗵 𝗮𝗻𝗱 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻. 𝗪𝗶𝘁𝗵 𝗮 𝗳𝗼𝗰𝘂𝘀 𝗼𝗻 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝗮𝗻𝗱 𝗶𝗻𝘃𝗲𝘀𝘁𝗶𝗻𝗴 𝗽𝘀𝘆𝗰𝗵𝗼𝗹𝗼𝗴𝘆. 𝗦𝗵𝗲 𝗵𝗮𝘀 𝗮 𝗹𝗮𝗿𝗴𝗲 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗼𝗻 𝘁𝗵𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝘄𝗶𝘁𝗵 𝟭,𝟲𝟴𝟵 𝗰𝗼𝗽𝗶𝗲𝗿𝘀 𝗮𝗻𝗱 𝟳𝟯,𝟬𝟬𝟬 𝗙𝗼𝗹𝗹𝗼𝘄𝗲𝗿𝘀.

She is a Psychology Double Major so has a lot of experience in the importance of the Psychological Mindset of Investing. She is also certified with a CISI Level 3 in Wealth and Investing Management.

She has over 12 years experience in Investing and has been featured in some of Australia's largest Publications.

We discussed what holds Women back from getting into investing, What Psychological characteristics make a good investor, Are you born with it? (Nature) v's Whether its acquired (Nurture), How Mimi navigated tough experiences, How to maintain your self worth during difficult periods
Why People Chase Winners, Why doing nothing is sometimes the best solution, Whats more difficult to manage Fear or Greed, Psychological Impact of Investing Narratives, the different types of intelligence and how
Influencers can drive sentiment.

𝗧𝗶𝗺𝗲𝘀𝘁𝗮𝗺𝗽𝘀:

https://lnkd.in/d-t5Pt3k

00:00 - Teaser
00:58 - Introduction to Mimi Ho (Koratrades)
05:05 - What Holds back Women from Investing
09:01 - What Personality Types and Skillset does it take to be a good investor
12:25 - Nature V's Nurture in Investing Skills
14:46 - Examples of Tough Experiences Mimi Has Had
17:48 - Why People Fear taking losses more than motivated to make gains
22:10 - What is a Harder Emotion to Control Fear or Greed
26:47 - Maintaining a sense of Self Worth even during bad times of Performance
28:42 - Psychology of Investing Narratives
34:22 - Softening Decision Fatigue
40:33 - Different Types of Psychological Intelligence
45:11 - How to Properly Assess Market Sentiment & Sources
48:29 - Find Out More about Mimi Ho (Koratrades)

𝐃𝐢𝐬𝐜𝐥𝐚𝐢𝐦𝐞𝐫: 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐧𝐨𝐭 𝐢𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐚𝐝𝐯𝐢𝐜𝐞. 𝐃𝐨 𝐲𝐨𝐮𝐫 𝐨𝐰𝐧 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐛𝐞𝐟𝐨𝐫𝐞 𝐛𝐮𝐲𝐢𝐧𝐠 𝐨𝐫 𝐬𝐞𝐥𝐥𝐢𝐧𝐠 𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐢𝐧𝐬𝐭𝐫𝐮𝐦𝐞𝐧𝐭𝐬.

𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝗼𝗻 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗲𝗰𝗼𝗻𝗼𝗺𝘆!

𝗘𝘁𝗼𝗿𝗼 𝗣𝗿𝗼𝗳𝗶𝗹𝗲𝘀:

𝗠𝗶𝗺𝗶 𝗛𝗼 (𝗞𝗼𝗿𝗮𝘁𝗿𝗮𝗱𝗲𝘀): https://lnkd.in/dKadQAWJ

𝗦𝘁𝗲𝘃𝗲𝗻 𝗕𝘂𝗱𝗴𝗲𝗻 (𝗯𝗲𝗲𝘀𝟴𝟰): https://lnkd.in/ddva7THi

https://lnkd.in/dEYdvk4U

Open video: The Psychological Edge in Investing - Elevating Your Mindset |‪ Interview with Mimi Ho (Kora Trades) on youtu.be

The Psychological Edge in Investing - Elevating Your Mindset |‪ Interview with Mimi Ho (Kora Trades)

taplio

The Battle

Vinod SP

Steven Budgen

Followers

Posts

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Performance ScoreVinod SP's LinkedIn Performance

Performance ScoreSteven Budgen's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Vinod SP

Steven Budgen

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Vinod SP's LinkedIn Performance

Performance ScoreSteven Budgen's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreVinod SP's LinkedIn Performance