Fabian Werkmeister

I help you to become fluent in Data and enable you to translate between business & tech ★ Data Scientist @FUNKE ★ 🐍Python 🤖Data Science 🛠️Data Engineering

Why do so many data projects fail to deliver real business value?
Because they often start with a technical discussion, not a business problem.
➡️ And you've likely felt the same frustration as so many others...

Investing in a complex data solution only to find it's over-engineered, over-budget, and doesn't actually solve your core challenge.
You're left with high costs, slow processes and no actionable outcome.

Imagine instead a data solution that is simple, powerful, and cost-effective. One that feels like it was designed exactly for your business and just works.
This is what I can build for you.

I started my career on the business side and switched to data along the way, where my deep passion for that field motivated me to self-taught all the skills I now have.
Sitting on both sides of the table allowed me to really understand and connect both worlds.

This is what you get, if you work with me:

Business-First Approach:
You get a business-minded partner, which listens to your challenges first to ensure the solution we build together is one you actually need. No confusing tech-speak, just pure focus on actionable results for you and your business.

Pragmatic & Cost-Effective Solutions:
You get a lean, powerful, and low-maintenance data analytics platform on Google Cloud Platform, which simply works.

Collaborative & Transparent Process:
You will be a partner in the process, not a spectator. Through regular check-ins and feedback, we ensure alignment at every stage to build something, which actually helps you.

➡️ If you're ready to finally get results from your data, connect or DM me and let’s chat!

Data Scientist

No Budget for Managed ETL? 💰
Here is a lean and cost-effective alternative 👇 

Managed ETL services can kill your budget
They are designed to punish you for your growth
They feel essential, but their pricing will spiral as your data volume increases

But you can also build something yourself
Use dltHub and serverless functions / containers to perform data ingestion:
✅ You only pay for execution time, aligning cost directly with usage
✅ You can focus on the actual problem, since dlt does the heavy lifting for you

What's the biggest headache dlt solved for you recently? ✌ 

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Hi, I'm Fabian👋

I write about building lean and cost-effective analytics platforms on GCP

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Lean & cost-efficient data systems on GCP for businesses to finally turn their data into decisions | 🛠️ Senior Data Engineer @ FUNKE

I don't have a linear career in data.
For a long time, I let that be a limiting belief.

I studied economics and worked in that field for some years
I learned what KPIs are and what really drives numbers and businesses
I did a lot of powerpoints and fought with excel, but this wasn’t a profession for me

So I took a data science bootcamp and switched to data
I still remember how everyone in the bootcamp was way better at coding than me
For years in data, I felt I was always playing catch-up to the people who grew up with this stuff

But with time and more seniority, this perception shifted
I notice that using tech and tools doesn’t generate value on its own
Value is created in knowing when to use which tool to drive a business action

With this in mind, I began to see my non-linear career path as my biggest asset.
Since I lived in both worlds, I also know very well how to connect them.
I start with the action, then build the absolute minimal system for that.

I may not be the best coder or technical expert out there
But I speak and understand business language
And this enables true solutions

If you’re also coming from a non-traditional path, stop trying to fit the mold.
Your diverse background isn't a weakness.
It can be your ultimate advantage.


What’s an unexpected asset your career path has given you?

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Hi, I'm Fabian👋
I write about building lean and cost-effective data systems on GCP
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The biggest lie in data
"We just need a new tool"


A new tool won't fix a broken process.
A new tool won't magically create a data-driven culture.
The most successful projects follow a business-first mantra.
👉 Tools are the last piece of the puzzle.

✅ Focus on Culture: Talk with the business teams.
✅ Focus on Processes: How are things done right now and how should they work in the future?
✅ Focus on the User: Identify the friction points between the data and the action or decision taken by an end user.

Remember:
Tools are just the vehicle - culture and process are the engine!

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Hi, I'm Fabian 👋

I write about building lean and cost-effective data systems on GCP
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Stop obsessing over the architecture diagram.
Obsess over the problem.

❌ Typical data project goes like this:

Month 1-2: Vendor selection
Month 3-4: Pipelines and modeling
Month 5: Dashboarding
Month 6: Show it to the business
(and find out it’s useless)


✅ The Value Path looks like this:

1. The "Why" Phase (40% of effort)
Deep stakeholder interviews.
Find out problems and decisions to be made.
Don't write a single line of SQL until you know that.

2. The "How" Phase (10% of effort):
Lean tech selection.
Most tools deliver the same output.
Choose what is convenient and available.
(if possible choose also what is cheap)

3. The "Build" Phase (50% of effort):
Minimalist implementation.
Automating only what is necessary.
Build the shortest bridge between data & decision.


You can build a "perfect" scalable platform in 6 months that nobody uses

Or you can build an "imperfect" targeted solution in 6 weeks that changes the business.

—
Hi, I'm Fabian👋
I write about building lean and cost-effective data systems on GCP
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My data team was most often seen as a cost center.
Here is how we shifted this to become enablers 👇

I used to build the thing I was asked for.
I finished the tickets.
I delivered the code.

But that often left behind a black box solution that no one on the business side truly understood. 
A technical problem was solved without transferring the value of it to the business.
That's how data projects solely generate costs.

Do this instead
✅ Prioritizing the business problem over technical solutions.
✅ Designing the platform to deliver a single, clear business action.
✅ Measuring success by time or costs saved, not by processed data volume.
✅ Transferring knowledge so the business team can understand and act on the data without you.

That’s how you build trust.
That’s how you generate value tied to the costs.
That’s how your projects and your team are seen as an investment.

TL;DR
Any data project needs to directly and measurably impact the bottom-line of the business
➡️ if your project doesn’t deliver this, it will only be seen as a financial liability

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Hi, I'm Fabian 👋
I write about building lean and cost-effective data systems on GCP
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SQL is important for a data engineer
But the most valuable skill isn’t taught in tutorials

I’m not talking about Terraform, Python or BigQuery
These are merely tools and techniques to work with data
My most important skill is the ability to translate:

Business ➡️ Tech
Stakeholder: "I need real-time data for my weekly sales report"
Me: "We need a simple batch pipeline that updates data each morning"

Tech ➡️ Business
Tech: "We implemented incremental loading for big source tables"
Me: "Your Monday morning dashboard will now be ready at 8:00 AM instead of 10:30 AM."

When you bridge this gap with communication, you do two things:
👉 You protect the business from over-engineering (and over-spending)
👉 You work on things stakeholders actually care about (time and money)

Learn to speak and understand business language
That is how you move from a data engineer to a strategic business partner

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Hi, I'm Fabian👋

I write about building lean and cost-effective data systems on GCP
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The era of Big Data is over
The era of Big Action will finally unlock the value of data

For the past decade, billions were spent on data stacks
We now have 100+ tools to pipe, clean and visualize data
And yet, decision-making velocity hasn't improved

The systems we built were designed for observation, not operation
Your data system is worthless until that potential is converted into movement

Let's look at a simple example:
➡️ 100 customers haven't purchased in 3 months

𝐓𝐡𝐞 𝐨𝐥𝐝 𝐰𝐚𝐲
- Discover this (most businesses don't even get to this point)
- Download CSV
- Format in Excel
- Upload to HubSpot
- Manually trigger "we miss you" email
- By the time you do this, 50% of them have already moved to a competitor

❌ 𝗧𝗵𝗮𝘁 𝗶𝘀 𝗳𝗿𝗶𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝘄𝗵𝗲𝗿𝗲 𝘃𝗮𝗹𝘂𝗲 𝗹𝗲𝗮𝗸𝘀 𝗼𝘂𝘁 𝗼𝗳 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺


𝗧𝗵𝗲 𝗻𝗲𝘄 𝘄𝗮𝘆
- Your system detects these automatically
- Your system pushes these to HubSpot
- An email workflow is triggered
- You get a ping to approve this
- Emails are sent

✅ 𝗪𝗶𝘁𝗵 𝗺𝗶𝗻𝗶𝗺𝗮𝗹 𝗶𝗻𝘃𝗲𝘀𝘁, 𝘆𝗼𝘂 𝘁𝗼𝗼𝗸 𝗶𝗺𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗮𝗰𝘁𝗶𝗼𝗻


Data systems need to pivot
From a reporting source to a trigger
From "what happened?" to action-oriented systems.


𝗪𝗵𝗮𝘁 𝘄𝗮𝘀 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁 "𝗲𝘅𝗽𝗼𝗿𝘁-𝘁𝗼-𝗰𝘀𝘃" 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝘆𝗼𝘂 𝗸𝗶𝗹𝗹𝗲𝗱? 😉 

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Hi, I'm Fabian Werkmeister👋
I write about building lean and cost-effective data systems on GCP
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I used to be a data snob
And this attitude prevented valuable solutions

As a data engineer, I think in terms of outputs
I designed whole workflows with a clear result in mind
This was most often a nice dashboard or an automated report

When someone asked for an "Export to Excel" button, I rolled my eyes
I thought "Why do I build of all this if it just ends in a spreadsheet?"
I was missing the point completely!

❌ My job isn't to dictate how people work.
✅ My job is to enable them to work.


If a finance manager is faster in Excel, my fancy dashboard is actually a bottleneck.
If a sales rep needs a CSV to close a deal, my shiny report is blocking revenue.
My business users know way better how they can work efficiently than I do!

👎 I stopped being religious about the tools
👍 I started being religious about the outcome


Now, my philosophy is simple:
👉 Build the pipeline
👉 Ensure the data is available and trusted
👉 Let the user decide the last mile to be actionable

If that means an Excel export or a csv-file?
So be it.

The result at the end of the day is what counts.
Not the tool they used to get there.

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Hi, I'm Fabian Werkmeister 👋

I write about building lean and cost-effective data systems on GCP

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Does your data project have a six-figure price tag?
Bad news, you’ve already failed

A six-figure data stack is not an investment.
It’s a financial liability.
Start with something super simple👇

✅ Identify the Core Problem:
Stop looking at tables and talk to people.
👉 What specific business problem is causing pain?
➡️ "We are spending $50k/month on ads, but I can't tell, which campaigns are actually driving sales"

✅ Define the Actionable Outcome:
Don't start to build before you have a clear goal in mind.
👉 What is the simplest, clearest decision or action that needs to be enabled?
➡️ "I need to see CPA by campaign side-by-side so I can cut the losers, if they fall under value X"

✅ Select the Minimalist Tool:
Only now, start to build something!
👉 What is simplest, most cost-effective setup to deliver that specific action?
➡️ Get campaign data via ETL-tool or scripts, build a simple SQL-pipeline with a cron job, pump it into something consumable for your end user (Excel, BI tool, etc.)

That's it.
That's how you build a fast MVP, which costs almost nothing.
And with this foundation you can iterate and scale up always later on.


What’s one business problem you solved recently where you intentionally chose a simple tool over a complex one? 👇 

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Hi, I'm Fabian👋
I write about building lean and cost-effective data systems on GCP
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Some people meditate.
I build pipelines.

There is a meditative quality to Data Engineering that I haven't found anywhere else.
It satisfies my deep need for organization.
Taking something messy and shaping it into a logical, reliable structure.

It’s like solving a massive puzzle.
Every piece has a place.
Every transformation has a reason (hopefully 😉).

Building pipelines also leads to another passion of mine: serving others.
I use my love for structure to make life easier for the people around me.
I take the complexity onto my plate so they don't have to deal with it.

It's the perfect synergy. 
Solving someone else's problem while satisfying my inner needs.
And that’s why I love what I’m doing each and every day! 


There's nothing quite like that feeling when your pipelines wrap up with a satisfying green checkmark, is there? 😉

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Hi, I'm Fabian👋
I write about building lean and cost-effective data systems on GCP
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Tobias Zwingmann

RAPYD.AI

Independent AI consulting to create profitable AI solutions to innovate and grow your business. Fast.

Managing Partner

O'Reilly Media

Author and Instructor

LinkedIn

LinkedIn Learning Instructor

D2A2

Chief of Research - Applied AI

Springboard

- Helping Springboard as a subject matter expert in AI, especially for data analytics
- Mentoring students who want to switch careers to data science / data analytics 
- Top Mentor Award in 2021 (Springboard Inspire)

AI SME & Data Science Mentor

Insurgence

Partner

Toptal

Independent Consultant

Data Science Dojo

ML/AI Instructor

Deutsche Messe AG

Systematic retrieval, analysis and utilization of (unstructured) data from diverse sources to build new products and processes.

- People movement analytics 
- Computer vision with focus on text retrieval

Senior Data Scientist

Building & maintaining predictive models. Generating and communicating insights about customer trends and behavior to product owners and senior management.

- Churn Prediction
- Next Best Offer Recommendations 
- Customer Segmentation
- Visual Analytics

Leading various international trade show projects in e.g. China, India, Indonesia, Mexico, Myanmar, South Africa, South Korea, Turkey.

Project Director

Deutsche Messe Dubai Branch

Management Trainee

Author of AI-Powered BI (O'REILLY) | AI Coach & Mentor | I help organizations leverage AI and use data more intelligently to improve business performance. Sharing AI use cases & learnings.

I didn’t start my AI/ML journey doing “Profitable AI”.

I started it as a Data Scientist who built models that looked impressive on paper:

– Customer churn prediction with 95% accuracy ✅
– Next-best-offer systems with 15% uplift ✅
– Data-driven B2B products generating 6-figure revenues from day one ✅

But the hard lesson I’ve learned after 10+ years at the intersection of business and technology is that none of this matters if you can’t show results on the bottom line.

I’ve spent the last 5+ years in applied AI consulting where my work has taken me inside global enterprises in automotive and insurance, mid-sized companies with 500–1,000 employees, solopreneurs aiming to double their output, and small business owners uncovering $10k+ monthly savings in unexpected places. 

The contexts differ, but the patterns of what makes AI succeed (and fail) remain remarkably consistent.

Success is not about:
- Chasing the next shiny AI tool
- Hiring data scientists from Silicon Valley
- Hoping IT will figure it out

It’s about applying the principles of Profitable AI to deliver outcomes that matter: doing things faster, at lower cost, with higher quality — or scaling what you do to entirely new levels.

Along the way, I’ve shared many of these lessons in my books AI-Powered Business Intelligence (O’Reilly, 2022) and Augmented Analytics (O’Reilly, 2024), as well as in my weekly newsletter that is now read by 10,000+ professionals at companies like Google, Amazon, Gucci, H&M, Santander, and Mercedes-Benz.

So If you’re tired of the shiny AI demos and want to see it deliver real results instead — I think you found a good place.

We help you build amazing AI products faster.

Gemini 3 is making a lot of noise. And it's a really great model. "Is it better than GPT-5?!" My take: Use whatever works for you. Both are LLMs, both have their quirks. But if you want to know where each shines, here's what I found:

Gemini 3 = Messy Inputs → Clean Outputs
Throw it chaos: screenshots, transcripts, logs, mixed documents. It's great at compressing that mess into structure. Best when you're very specific about the output format you want. Seems like if you need to organize messy information, call Google.

Examples:
- Summarizing multiple meeting recordings into structured notes
- Extracting data from a messy pile of PDFs, screenshots, and spreadsheets

GPT-5.1 = Clean Inputs → Complex Thinking
Give it organized information and let it reason. It's great at planning, narrative, and multi-step decisions. Best when the task itself is complex, not the input.

Examples:
- Writing code (though Claude still beats it here)
- Building a strategy doc from a clean brief

Scenarios to combine both:

1/ Competitive analysis
Gemini 3 gathers data from competitor sites, pricing, and reviews; GPT 5.1 delivers the strategy

2/ Client onboarding
Gemini 3 cleans and organizes intake docs; GPT 5.1 produces the project plan

3/ Sales pipeline review
Gemini 3 compiles CRM data, calls, and emails; GPT 5.1 spots trends and recommends actions.

Don't try to find "the" best. 
Find whatever works best for you.

Fun fact: The infographic was done by me with Gemini-3 image gen ("Nano Banana"), first shot, Prompt in comments.

AI Advisor | O’Reilly Author | Instructor @ LinkedIn Learning | I find & realize profitable AI opportunities – here to share wins, fails, examples & frameworks.

I see a lot of confusion around AI workflows, AI agents, workflow automation, and agentic workflows. Most people can't even tell the difference. Which isn't a problem until you want to build an AI solution that is neither over-engineered nor insufficient.

So I put together a quick guide that breaks down each type and when to use what, along with a few examples.

Read the full blog post here: https://lnkd.in/erYqeVPi

AI Workflows vs. AI Agents

ChatGPT launched today 3 years ago. ONLY three years I should say. Feels like a decade compressed. To catch you up on what’s happened, I generated this little timeline with the help of ChatGPT Deep Research, had Gemini 3 double-check it, and Claude 4.5 generated the PDF.

Tasks like this would have taken a whole day in the "old" world.
Now done in less than 60 minutes with AI.

We're still far away from AGI.
But I don't care.
We're living in the most exciting timeline.
And I can’t wait to see what this timeline looks like 3 years from now.

3 Years Since ChatGPT

A while ago, I shared the AI Profit Stack – a simple framework to assess whether your AI projects are actually creating profit. People loved it, clients used it. Then someone asked me:

“How do I apply this to my Copilot licenses?”

Short answer:
You don’t.

Long answer:
Trying to measure all AI with the same ROI formula is exactly how you end up with fictional numbers, cynical finance teams, and AI steering committees that debate themselves into extinction.

Today I've shared the fix I'm using now in every client engagement – and the one I want you to have too.

Find it here here:

Open article: The Two Tracks of AI ROI by blog.tobiaszwingmann.com

How to separate 'AI that helps' from 'AI that pays'

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