After working with over 50 business owners as their Fractional CFO, I noticed how some consistently broke through their growth ceilings, while others remained stuck. 

The difference? The ability to understand and ACT on the numbers.

The biggest shocker here is that most businesses still rely on a basic P&L and some messy sheets to track their performance and plan ahead. We're putting a stop to that. With our best-in-class financial models and forecasts, we don't just put our clients back in control, we provid the direct actions to take to run your business without the constant guesswork

Joey Wit

I spent 48 hours at Eddie Maalouf's Agency Founders event.

The value from this will undoubtedly make our clients 7+ figures this year.

Day 1 summary from an Agency CFO POV:

1. Exponential Business Models

Agencies are a pain to scale the traditional way.
(trying to get to 100+ clients at $3K/mo).

What really stood out was how each speaker had their own angle that gave them way higher lead flow, prices or scalability.

None were just selling "email marketing for ecom brands" in isolation.

Examples:
• Jad Al Fakhani was fascinating as he pretty much captured the entire ecom ecosystem in his country. Drivings leads for his agency through his ecom course, and then turning the agency clients into deal flow for his investment company. All combined = 10x better than any in isolation.

• Jem Bourouh had my favourite business model. Instead of building all sorts of funnels, he leverages existing clients (and Ws he gets them) to get more large clients. Turning each client into more clients.

• Manick Bhan was further ahead in the AI Agent wave than any other agency I've seen. Their execution speed and ability to ride this wave are a flywheel in itself.

Takeaway: you must think bigger than your actual service. Find what unfair advantage you have and build that into your model.

2. Headaches per Dollar

I liked this one from Dee Deng 🥊 a lot.

I've always found that as the founder your Hourly Rate is more important than your Profit Margin.

But I like Headaches per Dollar even more. Three ways to focus on this:

1. Data over Opinions: don't waste hours on opinionated discussions. Get the data to back it up or move on.
2. Log Your Decisions: every time you make a big decision/pivot, write it + the reasons for it down. Every couple weeks, review this to see how it turned out. Learn from your W's and L's.
3. From Mina Elias: get a "life manager". This was like Buy Back Your Time on crack. Paying someone $5K+/mo to remove all friction = high ROI.
Got so much more in my notes on this. Will likely make a much longer post on this in the future.

3. Incremental Improvements over Big Pivots

This idea came from Alisha Conlin-Hurd 🧬 her talk on Funnels/Pages.

So much of this was on testing and iteration... creatives styles, hooks, offers etc.

I've seen so many agencies (including myself) jump from offer to offer, or change marketing strategies every couple of weeks.

But nothing will work if you don't test enough.

This was hands down the best event I've been to in the agency space.

I'll share my notes from Day 2 later this week.

And ofcourse follow all people mentioned here. All super high value.

Joey de Wit

Founder of AgenCFO - ROI-obsessed CFO services for agencies and eCom | 50+ clients served | Building own portfolio along the way | Ironman prep 7’26

Three years ago I left banking to start AgenCFO.

Been a wild ride since.

• Worked with over 50 clients (manage $55M+ in revenue now)
• Lived in 10+ countries and settled in Dubai
• Completed 2 Marathons, training for Iron Man

Tested, failed and succeeded with many different offers and strategies over the last couple of years.

But now it finally feels like we truly figured our shit out.

The path is extremely clear:

Scale AgenCFO to 50 active clients → Move into acquisitions (for ourselves & clients) → Grow with the upside we create

I don't want a high-volume "reporting" business. I want a business where we make our clients so much money and add so much value that they can't live without us.

We know how to get there. I just need two more finance pros to make this happen.

If you're either a solo CFO and want to be part of a team again (and never have to do sales again).

Or if you're stuck in a boring finance job and want to actually make an impact and have freedom.

Then send me a DM. I want to speak with you.

Your finance degree got you:

• A mediocre salary & 5% raise per year
• Two weeks' vacation
• Creating budgets no one uses

It could get you:
• Location freedom
• Clients who actually listen
• A cut of the growth you create

Different path. Way better life.

Rarely have I seen an agency "grow into profitability".

If you are at 10% net profit, don't expect to magically get to 25%+ at scale.

It's a sign your pricing, comp, delivery or model is off.

You must know exactly what it is, or you'll stay stuck at 10%.

No alternative text description for this image

Founder @ Drilldown | Custom-designed business dashboards for B2B SaaS that deliver insights in one click, eliminating the need for complex spreadsheets in just 60 days.

We help B2B SaaS teams build an owned data system - so metrics align across the organization and growth decisions are based on evidence.

This might resonate:

As B2B SaaS companies scale, it becomes harder to understand on where growth comes from and what to do next.

Most teams already have dashboards, data integrations, and the technical ability to build new ones. 

Yet they still struggle to agree on what metrics mean, explain drivers of revenue, or produce reliable forecasts.

What you need is one set of accurate, explainable numbers that your team can rely on.

Our work focuses on the data system underneath dashboards: the definitions, structure, and logic that determine whether analytics can support decision-making at scale.

If you’re trying to make decisions based on data but the numbers don’t line up, this explains why: https://thomasvarekamp.substack.com/p/you-have-dashboards-why-are-decisions

Thomas Varekamp

I saw a horror story on Reddit that highlights the biggest AI risk in 2026 (and beyond)

A company let an AI data agent answer leadership's business questions for three months.

The output was complete fabrication:

- Sales targetted territories based on fake revenue.
- CFO presented decks filled with incorrect metrics.
- Managers made decisions based on hallucinations.

If you plug AI directly into your data without providing sufficient context, it will simply guess the answer. 

It will sound confident, but it will likely be completely wrong.

The missing piece here is the foundational work; data modeling + metadata + governance that explicitly tells the AI:

"This is how we calculate Net Revenue."
"This is the definition of a qualified lead."
"This is how we measure churn."

It might not be the most exciting part of data management, but as we rely on AI to do more of the heavy lifting, it is absolutely essential.

Drilldown | Helping B2B SaaS leaders regain control by turning data into direction.

Code = commodity. Context = everything.

OpenAI just published a breakdown of their internal data agent. 

It confirms something I've been seeing for a while now.

The problem isn't writing code (I still remember these days). 

It’s context.

OpenAI manages:

- 600 petabytes of data 
- across 70,000 tables. 
- for 3.5k employees.

Even for them, finding the "right" table was the hardest part of the job. 

Their solution? 

An AI agent that does the grunt work so analysts can focus on metrics, assumptions, and decisions.

Their data agent learns from 3 sources:

1. Experience
 → understands mistakes + past questions.

2. Existing systems
→ checks if tables exist and analyzes pipeline code.

3. Humans
→ get context from metadata, Slack and Notion.

So if AI handles the code, what’s left for the human?

The human becomes the context editor. 

Their job is not to build charts, but to define what success looks like and verify if the AI’s reasoning.

---

My takeaway: 

If your data management is a mess, even the best AI will fail.

There should be more focus on data modeling, definitions & context. 

Not more dashboards.

€13,650 new revenue last month. €0 from our new offer. 

Here is the 30-day breakdown:

Last week I soft-launched our new offer: 
The SaaS Intelligence System.

It's a service where we help B2B SaaS teams align growth metrics across the organization.

I pitched it to some potential customers - no sales yet. 

I also launched a Substack to go deeper into B2B SaaS data analytics & metrics.

People get a better understanding of what we do, which speeds up the sales process.

(+ I enjoy the writing)

A good long-term decision.

Here's an update + stats:

- closed a new client.
- extended our partnership with HeyReach.io.
- moved a client to our maintenance service.

Deals:
- 3 new deals closed
- €13.650 new revenue 

Linkedin (7 days):
- 14,184 new impressions 
- 76 new followers 

Substack (7 days):
- 86 new views 
- 5 new subscribers 

Calls (7 days):
- 2 calls with partners
- 2 calls with qualified leads

Let's see what this week brings.

HeyReach.io hit $10M ARR in 29 months. Here’s the data system we built to help them scale past $20M+ ARR.

From the outside, HeyReach.io looked like a rocket ship: 

→ 2,900+ monthly trials. 
→ 10-15% Month-over-Month growth. 
→ The Linkedin layer of the sales stack.

But internally, data was a big bottleneck. 

They were wasting hours every week manually stitching spreadsheets just to know how they were doing.

We partnered with them to install a permanent infrastructure: 𝗧𝗵𝗲 𝗦𝗮𝗮𝗦 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗦𝘆𝘀𝘁𝗲𝗺.

The 3 step execution plan:

𝟭. 𝗧𝗵𝗲 𝗔𝘂𝗱𝗶𝘁: 
We traced every trial, upgrade, and failed payment.

𝟮. 𝗟𝗼𝗴𝗶𝗰 𝗟𝗮𝘆𝗲𝗿
We coded their specific metrics definitions (not Stripe's defaults).

𝟯. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻
We built a live command center for the C-Suite.

Before: conflicting numbers & manual work.
After: automated, real-time insights for all teams.

You can't run a $10M ARR company on guesswork. 

You need good data.

Swipe to see the complete breakdown.

(ps. check the comments for details on how we work ↓)

The $10M ARR data system we built for HeyReach

Here's the real story of how I'm doing. 2025 in numbers:

Costs: €237.802  
Revenue: €223.498
Profit: €-14.304

I worked more. I spent more. But I ended the year with less money in the bank.

So I past few months I decided to take a step back.

Looking at the situation from a distance, I realized something...

Our model is completely broken.

The problem: it takes weeks to understand the data context of new clients. This forces us to ask quite some money for our services.

The reality is that data rarely has high-priority. 

To understand the value, someone must have experienced a painful data moment.

So I sat down to think: how can we make this work?

Clients are happy, we have great testimonials, we clearly offer value. 

I started to analyze our best clients. That's when I noticed something...

It was never: "We love the charts." 

It was: "We love that we now finally own a system to leverage data for growth decisions."

I realized the value isn't in us writing the code (clients usually have great engineers). 

It was in designing the system & transferring the knowledge.

I spent the last few months documenting our entire IP: 

→ full customer journey tracking.
→ models to combat churn.
→ accurate ARR reporting.
→ marketing attribution.
→ deep ICP analysis.

Basically all the things a B2B SaaS can do to drive more ARR with data.

So in 2026 I'm making some changes...

Instead of us building the data system and handing it over, we are building it together.

It's a structured program where we set everything up step-by-step in 12 weeks.

Meaning: lower price point + you get the systems & skills.

Already pitched it to some prospects. Feedback has been very positive so far.

Pretty confident that this is the right direction for the business.

Let's see what 2026 brings!

P.S. I’m looking for a few companies to kick this off. If you want to see the full breakdown, shoot me a DM and I’ll send it over.

New €5,000 deal. 3 deals in pipeline. 6 calls. 

Here’s my 7-day update:

I spent most of the week doing outreach, no automation. 

I put quite some time writing personal messages. Response rate is fairly high (10-20%).

Had some good chats. Definitely works if you care.

𝗢𝗙𝗙𝗘𝗥 𝗨𝗣𝗗𝗔𝗧𝗘:

Based on these conversations, we now include a 1-day on-site build in our offer.

Meaning, we come to your office & build your data system together.

I’ve done this in the past. 

High-energy, fun, and speeds up the results.

𝗪𝗘𝗘𝗞𝗟𝗬 𝗦𝗧𝗔𝗧𝗦:

Deals:
- finalizing new €5,000 deal.
- 3 open deals for new offer.
 
Calls (7 days):
- 5 calls with partners.
- 1 follow-up call with lead.

Substack (7 days):
- 97 new views.
- 8 new subs.

Linkedin (7 days):
- 28,887 new impressions.
- 101 new followers.

We're heading into the right direction I think.

Ready for the week ✌️

Expandi hit $10M ARR bootstrapped. Here’s the data system we built to help them grow faster.

From the outside, Expandi was an unstoppable force:

→ 0 to $7M ARR in under 3 years.
→ fully bootstrapped.

But as they scaled, internal problems were growing.

- marketing couldn't measure LTV. 
- finance struggled to reconcile $ vs. € ARR. 
- teams burning hours updating spreadsheets.

We partnered with them to install The SaaS Intelligence System.

The execution plan:

𝟭. 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘇𝗲

We identified growth blockers and defined clear goals for every department. 

No vanity metrics.

𝟮. 𝗕𝘂𝗶𝗹𝗱

We implemented complex ARR logic (New, Expansion, Churn) into a centralized warehouse they own.

𝟯. 𝗔𝗱𝗼𝗽𝘁

We put the whole team (Marketing, Customer Success and Finance) on shared metrics. 

Before: no realtime insights.
After: trusted metrics for decision-making.

Swipe to see how we built their data system.

How we built Expandi's data system

I worked with 20+ SaaS companies. 

They all hit the same problem at $2M+ ARR.

→ Data chaos.

Most founders try to fix this by building "more dashboards."

But that is the wrong approach. 

The outcome is always the same:

→ “Stripe says one thing, HubSpot another”
→ “I can't forecast and track trends reliably”
→ “We always argue which number is correct"

A ton of data. Nothing is connected.

❌ marketing burns budget on leads with low LTV.
❌ RevOps forecasts revenue based on fugazi.
❌ engineers waste hours on broken data.

The solution is not more dashboards and tools.
It's a data system you own & control.

I call it The SaaS Intelligence System.

During a 12-week program, we help you build a company-wide system for your data.

What you'll get:

1. growth metrics aligned across teams.
2. consistent revenue forecasts.
3. focused dashboards for decisions.

You own the code. We leave after 12 weeks.

Swipe to see how it works ↓

(or check the comments for the details)

How I align growth metrics across B2B SaaS teams

I used to love Power BI. 

Now I hate it.

Not because it’s a bad tool. But because of how most teams use it.

Power BI lets you build dashboards.

But to build dashboards, you need to model the data first. And that’s the trap.

Most people do datamodelling inside the BI tool. That’s a huge mistake.

Why?

Because as your business grows, your data grows too.

What starts as a simple report becomes a spiderweb of logic.

- impossible to trace.
- brutal to maintain.
- easy to break.

Here’s what to do instead:

1. build a proper data warehouse.
2. manage your single source of truth there.
3. use Power BI only as a presentation layer.

A good warehouse gives you:

- version control → roll back mistakes.
- traceability → find and fix issues fast.
- quality checks → know the data is right. 

When you separate logic from visuals:

- people trust the numbers.
- teams make faster, better decisions.
- you don’t have to rebuild everything later.

It’s cleaner, smarter, cheaper.

Don’t duct-tape your data stack.

Build it right from the start.

I am looking for new tech partners to integrate into our $1M - $10M ARR client data stacks.

Context:

We build high-performance data systems. 

We are looking for best-in-class tools to implement for our customers and drive revenue.

🛑 Not looking for:

- generic lead gen or SEO services.
- dev shops / outsourcing agencies.
- "Waitlist only" products (must be live).

✅ I am looking for data & AI products in:

1. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲

- CDPs.
- reverse-ETL.
- pipeline orchestration.

2. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲

- AI agents.
- data enrichment.
- out-of-the-box ML models.

Or anything that turns data into revenue or operational speed.

Bonus: you have a BigQuery connector.

Plz do not DM me - drop it in the comments.

Here’s a format: 

[𝘯𝘢𝘮𝘦 𝘰𝘧 𝘵𝘰𝘰𝘭] + [𝘱𝘳𝘰𝘣𝘭𝘦𝘮 𝘺𝘰𝘶 𝘴𝘰𝘭𝘷𝘦] + [𝘰𝘶𝘵𝘤𝘰𝘮𝘦]

If you fit the stack, I will DM you to see how we can collaborate.

We are entering the era of the 1-person data team.

I spent the past week testing AI data agents. It made me to rethink everything:

- data science.
- data analysis.
- data engineering.
- analytics engineering.

AI can now execute all of it.

Which makes one thing very clear: most data tools with buttons are going to fizzle out.

Why pay for a rigid analytics platform when an AI can write, refactor, and maintain the underlying code directly?

My predictions:

1. 𝗖𝗼𝗱𝗲-𝗵𝗲𝗮𝘃𝘆 𝘀𝘁𝗮𝗰𝗸𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗱𝗼𝗺𝗶𝗻𝗮𝗻𝘁.

AI makes raw code the fastest, cheapest, and most robust way to build data systems. 

Writing and maintaining SQL, Python, and infrastructure is no longer the bottleneck.

2. 𝗗𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗰𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝘆.

Moving data from A → B is a solved problem. 

Just give the API docs to an AI agent and ask to build the pipeline. 99% of the time it will one-shot it.

(try Claude Code if you don't believe me)

3. 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘄𝗶𝗹𝗹 (𝘀𝘁𝗶𝗹𝗹) 𝗯𝗲 𝗸𝗶𝗻𝗴.

AI can write the code, but it doesn't know your complete business context. 

Deciding what to model, why it exists, and how it should be interpreted remains a human responsibility.

The next 12 months are going to be VERY interesting.

You are paying engineers $1,000 to solve a $50 problem.

Got off a call with a founder ($3M+ ARR range). He complained his dashboards always broke.

After chatting, I found the issue. 

This is the data stack:

1. custom scripts pulling in data
2. all dumped in Google Sheets.
3. Looker Studio for dashboards.
  
He had expensive developers writing scripts to pull data from Stripe and HubSpot and put into @Google Sheets.

This might work at $500k ARR with limited data. 

But it breaks at $3M.

Why? 

Because every time something changes, your high-leverage engineer spends 8 hours fixing a script.

This is a waste of resources.

Here's what I suggest for most companies running on Google Workspace:

→ ETL tool (Weld): automate the extraction.
→ BigQuery: store the data.
→ Looker Studio: build dashboards.

Build the system once, trust the numbers, and save some money.

You cannot run a $15M ARR SaaS if your dashboards take 10 min to load.

That was Rentman. 

They just raised $22M. Growing fast. 

But their data couldn't keep up:

- discussions about conflicting metrics.
- dashboards took 10+ minutes to load.
- decisions based on vibes because "no data"

And we all know that more money doesn't make things simpler...

It makes things more complex.

(this is also true for data btw)

So I helped them to build a data system they could own and extend.

The process nutshell (3 steps): 

1. defined + aligned metrics.
2. set up data pipelines.
3. build dashboards.

Results: 

→ instant-loading reports.
→ teams trust & use the same data.
→ a system they can maintain and extend.

After I was done they hired their own data team.

Pretty cool.

Swipe to see the transformation ↓

PS. We help B2B SaaS teams build a data system they own. Check the comments for a link with details 👨‍💻

How I built a data system for Rentman ($15M ARR)

Stop tracking vanity metrics. 

You don't need 50 dashboards to run a $10M ARR SaaS. 

You need 10 max.

Most teams build reports for every questions that pops up.

The result: dashboard overload.

They have 50+ reports in HubSpot/Looker Studio, but probably only just 2 of them.

We worked on data systems for 20+ scaling B2B SaaS companies. 

The problem is not creating dashboards. It's building dashboards with clear intent.

Here is part of the blueprint we implement for clients:

1. Pipeline: velocity & bottlenecks.
2. Funnel: conversion rates by stage.
3. Attribution: which campaigns actually drive revenue.
4. Journey: every touchpoint before the deal.

These are the control center for your commercial team.

Swipe to see the full breakdown ↓

(use this for inspiration)

The B2B SaaS GTM Dashboard Blueprint (10 examples)

4 deals in pipeline. 2 calls. 

A 7-day update:

I spent a chunk of the week experimenting with Claude Code, and holy shit I'm impressed.

A single person can now easily fulfill all the roles of a data team.

Data work will look very different in 12 months. 

𝗢𝗙𝗙𝗘𝗥 𝗨𝗣𝗗𝗔𝗧𝗘:

Due to these new insights, I'm testing out new things we can do for our clients.

Technical obstacles are pretty much a thing of the past.

My idea is to put more focus on advanced data science work.

Meaning: building custom prediction models (churn prediction, ARR forecasting, etc.)

This used to take ages to build & validate, but not anymore. 

𝗪𝗘𝗘𝗞𝗟𝗬 𝗦𝗧𝗔𝗧𝗦:

Deals:

- 4 potential deals in pipeline.

Calls (7 days):

- 1 call with a previous client.
- 1 call with a new lead.

Substack (7 days):

- 24 new views.
- 1 new sub.

(will publish new article soon)

Linkedin (7 days):

- 40,259 new impressions.
- 74 new followers.

Let's see if we can improve this week 👊

7 dashboards I built for a $1,448,950 ARR B2B SaaS.

Most companies track vanity metrics.

We focus on the 3 core pillars of performance: 

1. Revenue.
2. GTM.
3. Product.

Here is the breakdown:

1. Revenue Performance

We don't look at just "Total ARR."
We break it down by MRR Movements:

- new business.
- net growth rate.
- net revenue retention (NRR).

2. The GTM Machine

We don't guess where leads were get stuck.
We visualize the full lifecycle:

- deal funnel.
- contact lifecycle.
- pipeline velocity.

3. Product & Support Health
  
We don't assume features were "useful." 
We measure it.

- time saved.
- feature adoption.
- support efficiency.

---

You can copy these dashboard layouts today.

But if the underlying data system isn't set up well, the numbers will be wrong.

Check them below 👇

7 Dashboards I Built For a $1,448,950 ARR B2B SaaS

I see a massive shift in growing B2B SaaS.

They move away from default reports (Stripe, HubSpot, ChartMogul)...

to building a Data Warehouse.

Why?

Because generic dashboards are built on generic assumptions.

As soon as your:

- billing.
- pricing.
- sales motion.

...is different from the default, these dashboards break.

A Data Warehouse ensures your data is accurate, automated, and trusted.

But don't take my word for it. 

Here are the 4 specific things founders love about the switch:

1. Custom logic > default metrics

Tools like Stripe have rigid definitions. They don't know your business context. 

We build custom logic so the data reflects the real world.

“Stripe was showing 20k in churn, but in reality, it was just two failed payments... If we cannot agree on what happened, how can we make a decision?"

— Ilya Stojkovski, CRO at HeyReach.io

2. Flexibility 

Most tools lock your data. If something breaks, you're dependent on them to fix it. 

We build a warehouse that you can maintain and extend yourself.

“What I really like is... that it's set up in a way that we can extend it ourselves... we're not dependent on one person or company.” 

— Simon Paul, CTO at Rentman

3. Escaping "Spreadsheet Monday"

Many teams spend their Monday manually cleaning CSVs. 

We automate the pipelines so you go from manual data entry to instant analysis.

“On Mondays, it took us 6 hours of manual entry... Our data warehouse saves us alteast 2 to 3 hours every week.” 

— Tim, Head of Finance at Level.works

4. Customer journey analysis

As you grow, data gets scattered. 

Marketing is in one tool. 
Product is in another. 

We bring these sources together to measure true ROI.

“Before, we were looking into multiple data sources... Now we can see which specific channel brings us not only signups, but actually brings us LTV.” 

— Glenn, CEO at Expandi

---

If data is a priority in 2026, we can help.

What you'll get:

1. aligned growth metrics across teams.
2. data warehouse built on best practices.
3. focused set of dashboards for decisions.

You own everything. We leave after 12 weeks.

Check the comments for the details. 👇

Founder: "I want an AI agent to do my ARR forecasting."

Me: "Show me your data infrastructure."

The reality check:

If your data is scattered across Stripe, HubSpot, and Google Sheets...

You don't need more AI. You need a Data Warehouse.

To build a "AI-Native" business, you have to fix your messy data first.

The old way:

❌ marketing calculates spend in spreadsheets.
❌ product counts users in Mixpanel.
❌ revops tracks ARR in Stripe.

→ Problem: when data is disconnected, AI lacks context.

Here's why that matters:

1. Conflicting definitions.
  
Marketing defines a "Lead" differently than Sales. The AI doesn't know which logic to trust.

2. Hallucinations.

When an agent can't find a single source of truth, it guesses. Guessing = hallucination.

3. Token bloat.

Your AI has to burn tokens trying to understand three conflicting data sources instead of answering the question.

The new way:

✅ centralize data in a Data Warehouse.
✅ add context + definitions.
✅ deploy AI agents on top.

→ Result: consistent, accurate, and cheap answers.

An AI agent is only as smart as the data it feeds on.

If you can't trace a customer from ad click to renewal, neither can your AI.

Fix the data first.

The agents come second.

Most dashboards & reports fail for one specific reason.

It rarely has to do with the tool or the data.

The problem is much simpler: metric definitions.

A dashboard full of metrics means nothing if no one agrees on what they mean.

I see this pattern constantly:

❌ sales trusts a spreadsheet over the dashboard.
❌ finance / Product report conflicting churn %.
❌ "active user" has 3 different definitions.

Before you build the dashboard, build the Metrics Dictionary.

It can be a simple Google Sheet that lists out your metrics + descriptions.

Here are the columns we include in it:

1. unit → is it €, $, %
2. filters → how to slice the data
3. calculation → how it's calculated
4. department → who cares / uses it
5. metric owner →who is accountable
6. refresh rate → how often it updates
7. description → what the metric means
8. data source → where the data comes from

I attached the template we use in the comments.

Copy it, tweak it, use it. ✌️

Most B2B SaaS teams skip the most important step in their data system.

You have the raw data (step 1). 
You have the visuals (step 3). 
But you are missing the logic (step 2).

The result:

- dashboards don’t match.
- teams don’t trust the numbers.
- everyone rebuilds the same metric, slightly different.

The fix?

A 3-layer system:

1. Data sources

→ business tools generate data.
→ Stripe, HubSpot, Google Analytics, etc.

2. Logic (where most go wrong fail)

→ raw layer: stores untouched data.
→ analytics layer: cleans & reshapes it.
→ semantic layer: defines the metrics.

3. Consumption

→ notifications.
→ dashboards.
→ reports.

When you get this right:

- growth metrics are consistent.
- people trust and use data.

When you don’t?

→ rework.
→ mistrust.
→ wasted decisions.

Don't just store more data.

Structure it so it can actually be used.

PS. Check the article in the comments where I describe why more dashboards won't solve your data problems.

taplio

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Thomas Varekamp

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taplio

The Battle

Joey Wit

Thomas Varekamp

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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);}Joey Wit's LinkedIn Performance

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