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 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.

Drilldown | a data science team in your pocket for B2B SaaS.

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.

Business intelligence is being commoditized way faster than anyone realizes.

Workflows that used to require a team of 5 are now being handled by 1 marketer using Claude Code.

Here is exactly where BI is heading in the next 12 months:

1. 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗮𝗻𝗱 𝗱𝗿𝗮𝗴-𝗮𝗻𝗱-𝗱𝗿𝗼𝗽 𝘁𝗼𝗼𝗹𝘀 𝗮𝗿𝗲 𝗮 𝗱𝗲𝗮𝗱 𝗲𝗻𝗱.

→ using code and letting AI handle it is much faster, cheaper, and robust. 

Learning Power BI now? You're in for a big surprise.

2. 𝗦𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁𝘀 𝗮𝗿𝗲 𝗼𝗻 𝘁𝗵𝗲𝗶𝗿 𝘄𝗮𝘆 𝗼𝘂𝘁 

→ no one will manually update cells and write formulas in Excel. Just hook your data to AI and ask for the insights.

3. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝟵𝟵% 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 

→ AI will do the heavy lifting. Humans will transition from "analysis" to "validation", only checking outputs.

4. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗴𝗲𝘁𝘀 𝗼𝘂𝘁𝘀𝗼𝘂𝗿𝗰𝗲𝗱 𝘁𝗼 𝗔𝗜 

→ machine learning is code-heavy. AI writes code perfectly. Humans will just set the framework. AI does the rest.

5. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 "𝗲𝗮𝘀𝘆" 𝗷𝗼𝗯.

→ AI can now auto-generate connectors, map schemas across sources, and self-fix broken pipelines in real-time. 

So, where is this going?

I think we will see companies either:

1. hire a single data person & upskill employees.
2. have external agencies handle it.

(or both)

Just as computers replaced most mathematicians, AI is doing the exact same thing to BI.

Only this time 1000% quicker.

If you disagree, you simply aren't paying attention to what's happening right now. 

I'm shocked how fast things are progressing.

Data teams will look completely different in 2 years.

The standard data analysis workflow used to work like this:

1. you have a question.
2. you ask the analyst.
3. you wait a week.
4. you get a report.

And then you figure out if you actually got what you wanted.

Now?
1. you ask the question.
2. you get the answer in seconds.

Think of this:

Marketing can ask “what is our best channel?”
…without opening 20 dashboards.

The CEO can ask “where to focus next quarter?”
…without going through slide decks.

Sales can ask “which deals need attention?”
…without digging through the CRM.

Everyone will be able to use business insights, instantly. 

So what will data teams do? 

Their primary value no longer comes from structuring and analyzing data. 

It will come from:
→ building foundations AI can understand.
→ helping others formulate questions.
→ translating insights into actions.

The titles will have to catch up with the work.

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.

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

How we built Expandi's data system

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.

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 👊

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. ✌️

The data analyst role we know won’t exist in 3 years.

And that’s a good thing.

For years, analysts were valued for one output: dashboards.

Pretty charts, nice filters and providing a surface-level view of the business.

That is ending.

Agentic AI is changing how analysts work completely. 

They’re not just dashboard builders anymore. They’re end-to-end solution developers.

That means:

1. defining business logic up front.
2. modeling data to drive strategy.
3. delivering insights without 5 handoffs.

And yes, way fewer data people will be needed.

But the ones who remain?

They’ll have more trust, and more impact.

The analyst isn’t just the “report person” anymore.

They’re becoming orchestrators behind the insights.

In 2-3 years, all data teams will become AI teams. 

Here's why:

Data teams today spend countless hours in meetings explaining dashboards and writing SQL. 

Most of this work doesn't add real value.

AI can do this faster, better, and without human bias.

In the next few years, analysts will stop spending time on dashboards and answering questions.

They will be maintaining infrastructure for AI:

- designing data models.
- adding context to data.

Here's how that works:

1. business software generates data.
2. data warehouse ensures high quality data. 
3. AI pulls insights and executes tasks.

Data will be the most valuable resource in every organization. 

This will be the source of truth for AI.

Yo, I'm Matt Mike 👋

(yes, Mike is my actual last name).

My career path has been far from linear. Here are the cliff notes (that's still a thing, right?)

2015: graduated with an English degree
2016: became a teacher
2017: became a dean of students
2019: left education for B2B sales
2019: hated sales. took a pay cut to become an operations analyst
2021: got promoted to operations supervisor

2022 and onward: became a business analyst (FIRST DATA JOB). Started documenting the journey on social media where I now have a combined following of 100,000+ tech professionals. I've been through a few different data roles with progressive complexity and now do BI consulting while continuing to help others progress their tech careers through content.

Because of my background, I have a passion for helping people with non-technical backgrounds break into tech and advance their careers. My technical passion is business intelligence analytics.

🤝🏻 Brands: mjomike@gmail.com
🎥 YouTube: @mattmike
📩 Newsletter: https://thdatapoint.substack.com/

Matt Mike

I changed careers at 31.

I was a teacher, then a Dean of Students first.

From there, I spent a few years in a non-technical corporate role. But that was my introduction to technical tools.

I decided I wanted to get into data analytics and went for it. The process start to finish took about 6 months.

People often think that you should be settled into a career in your 30s, but it's never too late to pivot from one area to another.

We put a lot of pressure on ourselves to "figure it out" but life isn't always so linear.

If you want a change. Make it happen.

BI Developer & Analytics Consultant 🤘 | YouTube @ Matt Mike | Former Educator & Dean of Students

SQL is a fundamental analytics skill.

It's the language of databases and how we query those databases for the information we need.

My latest read, "𝑆𝑄𝐿 𝑓𝑜𝑟 𝐷𝑎𝑡𝑎 𝐴𝑛𝑎𝑙𝑦𝑡𝑖𝑐𝑠" shows you how to do just that.

But it goes far beyond the basics.

Into things like:

↳ Manipulating data with Python
↳ Presenting, transforming, and updating data
↳ Defining datasets from existing datasets
↳ Window functions
↳ Performant SQL
↳ Processing JSONS and Arrays
↳ Advanced data types
↳ Inferential statistics

And best of all, it's practical.

Big thanks to the authors:

Jun Shan​
Haibin Li
​Matt Goldwasser​
Upom Malik​
Benjamin Johnston

Looking forward to learning some cool stuff :)

Grab the book here if you're interested: https://packt.link/0oLy1

#packtpartner

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