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.

Bridging the gap between data and strategy ✦ Lead Data Strategy Consultant @ Redkite ✦ R Programmer ✦ Policy Nerd

Okay I probably have your attention here for about 10 seconds, so I’m going to be quick:

My niche is navigating the complex data world for organisations to help them deliver on their strategy

Basically, I bridge the gap between data and strategy

Want to know more? Here is how I do it:

💡 Learned as a strategy consultant what matters and what doesn’t
🎤 Storytell to help clients implement effectively (how do you properly engage the audience and infuse that story with the key takeaways/ implications)
💻 Use my R coding and strategy consulting experience to bridge the data scientist's "get lost in the data" approach with the "tell me what I need to know on one page" approach of the business executive
★ Live off my passion to see through positive outcomes, be that at work, on my blog or even when helping a friend

So why should you care?

1) I post my best tips 3-5x per week on LinkedIn
2) I make it relevant to you, as a student, business professional, executive, or whomever. My goal is to share relevant stuff with my audience
3) I try to be unique in what I share and deliver, as few people have the combination of experience I boast
4) I value feedback and input from all others out there. I pride myself on learning from others' perspectives so please add it to my content!

Also some of my other Outputs:
Medium - https://dylansjanderson.medium.com/
Blog Website - https://www.policyinnumbers.com/
Election App - https://danderson.shinyapps.io/Canada-Electoral-Model/
Academic Journal Article - https://www.cambridge.org/core/journals/canadian-journal-of-political-science-revue-canadienne-de-science-politique/article/tipping-point-of-a-strategic-vote-when-does-an-individual-vote-strategically/7FEDD21C59DF03AC15311CD13080881B

Dylan Anderson

Data success in business does not start with an AI product, a well-constructed pipeline, or a frequently used dashboard.

Success starts with the business model. This, then helps inform the org data model.

Your 𝐝𝐚𝐭𝐚 𝐦𝐨𝐝𝐞𝐥 𝐰𝐢𝐥𝐥 𝐭𝐮𝐫𝐧 𝐭𝐡𝐞 𝐨𝐫𝐠𝐚𝐧𝐢𝐬𝐚𝐭𝐢𝐨𝐧’𝐬 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 𝐢𝐧𝐭𝐨 𝐭𝐚𝐧𝐠𝐢𝐛𝐥𝐞 𝐝𝐚𝐭𝐚 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 𝐚𝐧𝐝 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 to drive business results. 

Unfortunately most organisations skip this step and get into a mess of data engineering quick fixes

Here’s a breakdown of the process from business model to each part of the data model:

1. Start with the 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐌𝐨𝐝𝐞𝐥 🏢
Map out how your organisation creates, delivers, and captures value. For example, company creates product x, ships to store y, sells to customer z and makes money. Within that, what are the data points to track and understand? You map this out, you understand the process of how the organization makes money.

2. Extending it to the 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐮𝐚𝐥 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥 🗺️
The conceptual data model then builds on top of this, rewriting how the business operates using a high-level representation of organisational data. This phase involves identifying key data entities & domains and mapping the relationships between them. This should be understandable by business stakeholders as this is the bridge between business processes and data development.

3. Adding structure with the 𝐋𝐨𝐠𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥 🏙️
Next, we refine into a logical data model. Here, we delve deeper to define the data types, attributes of each entity and the nature of the relationships. This model is still independent of technical implementation but sets out a clear structure (often through ERDs or UML) for how data is related and organised.

4. Building the 𝐏𝐡𝐲𝐬𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥 foundations 🏗️
Finally, we arrive at the physical data model. It’s the actual database schema design, complete with tables, columns, data types, and constraints. It also takes into account the performance requirements, optimization techniques, and the physical storage of the data.

Obviously this is a broad simplification of the process. Each step of this journey requires collaboration between business leaders, data architects, and engineers to ensure the data models align with the strategic goals, is technically feasible and is understood by all relevant stakeholders.

Building this properly helps everybody understand how data actually delivers value. Too often we engineer without the architecture to structure it. Fix this and you will be way better off in the long-term.

Check out my article on Sunday explaining the importance of business models for data professionals, and the upcoming two articles after that diving into their interactions with data teams and how they need to be refactored for AI

Bridging the gap between data and strategy ✦ The Data Ecosystem Author ✦ Data & AI Leader ✦ Speaker ✦ R Programmer ✦ Policy Nerd

Your organisational structure underpins everything you do

But companies actively ignore it, and data people don't seem to mind

Here are three organisational structures to understand as a data person

🎯 𝐂𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐬𝐞𝐝 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 – The centralized structure is the most basic view of data where all data functions are consolidated within a single, central team or department. They are responsible for all data activities, from storage to processing & transformations to distributing insights. Usually companies start with this type of structure given its simplicity and the need to have all the data operations in one place.

⚙️ 𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐬𝐞𝐝/ 𝐌𝐞𝐬𝐡 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 – A decentralised data structure distributes data ownership, management, and activities across various domain-specific teams within the organisation. Each team is responsible for building its own data products and solutions, and managing all other data capabilities within that. Companies take a decentralised approach to develop functional domain experience (e.g., marketing, finance) within data teams and to build business-relevant products in a more agile way without the bureaucracy that centralised teams can bring.

🛠️ 𝐇𝐲𝐛𝐫𝐢𝐝/𝐇𝐮𝐛 & 𝐒𝐩𝐨𝐤𝐞 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 – The hybrid approach (or hub & spoke model) is the most common organisational structure. It combines elements of both centralized and decentralized structures, usually having a central hub for infrastructure and governance with spokes (individual business units) managing domain-specific data activities like analytics.

Check out my article that digs into this in a lot more detail, including the pros, cons, best approaches, and considerations for each of these structures

Its 2026, you shouldn't get duped by the AI everything sales pitch

These companies are confident, optimistic and polished, promising to transform data woes into AI-powered triumphs with the click of a button

And while these tools are definitely helpful, but there are gaps in every sales pitch:

🔗 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮𝗻 𝗶𝘀𝘀𝘂𝗲 – Tooling is useless if it doesn’t integrate properly, and a lot of sales teams don’t understand how this works
👥🔄 𝗙𝗼𝗿𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗣𝗲𝗼𝗽𝗹𝗲 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 – Technology does not solve for the operating model and ways of working between teams, which is often the main barrier in data
🤝 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗳𝗼𝗿 𝗰𝗵𝗮𝗻𝗴𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 – Can the company help support and train the teams using the tool?
🤖 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗺𝗲𝗮𝗻𝘀 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 – This is now a marketing tactic and often AI-enabled is nothing more than a background regression algorithm
💡 𝗡𝗲𝗲𝗱𝘀 𝘁𝗼 𝗯𝗲 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁 – Before signing on, there needs to be a proper use case to test out the tool and prove its value. That needs to come from the business, not the salesperson

So as we talk to sales professionals of data tools, the main thing to remember is: 
The path to genuine data-driven transformation is 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗮𝗻𝗱 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗶𝗻𝗴. It requires more than technology and anybody telling you differently doesn’t know what they are talking about. Push them to explain how the other elements (e.g., op model, use cases, change management, integrations) will fit in with this tech and how they can help solve for them.

What have you found the most helpful from sales professionals of data tools? What puts them above and beyond as partners?

Check out my post tomorrow for more of my thinking on this new AI-native and enabled world, and what you have to pay attention to

Don't overcomplicate the "data platform" term because everybody else does it

A data platform is a generic, catch-all term that encompasses the many technologies that underpin the process of making data accessible to business users, leading to better decision-making and insights. 

And that, in five words, is what a data platform should do— 𝐈𝐭 𝐬𝐡𝐨𝐮𝐥𝐝 𝐦𝐚𝐤𝐞 𝐬𝐞𝐧𝐬𝐞 𝐨𝐟 𝐝𝐚𝐭𝐚.

How does it do this?

By covering off three integral phases of the data lifecycle

First, it acts as a Central Platform
1. 𝐈𝐧𝐠𝐞𝐬𝐭𝐬 𝐃𝐚𝐭𝐚 – It brings data in from internal and external sources
2. 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐬 𝐃𝐚𝐭𝐚 – Helps align/ clean the data into one storage platform 
3. 𝐒𝐭𝐨𝐫𝐞𝐬 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 – Structures the data in a storage layer that is easy to use
4. 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 & 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐬 – Enables changes to the data to fulfil business needs

Next, it provides Integrated Management, Governance & Security
5. 𝐌𝐚𝐧𝐚𝐠𝐞𝐬 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 – Ensures the quality of data assets
6. 𝐒𝐞𝐜𝐮𝐫𝐞𝐬 𝐃𝐚𝐭𝐚 – Guarantees data is safely and securely managed, accessed and used

Finally, you have the Analytical/ Consumption Platform to draw out insights
7. 𝐒𝐞𝐫𝐯𝐞𝐬 𝐂𝐮𝐫𝐚𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 – Provides customised data sets for specified use cases
8. 𝐏𝐫𝐨𝐯𝐢𝐝𝐞𝐬 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 – Facilitates better decisions through data insights

That’s it. Those eight sentences describe exactly what a Data Platform should do to help a company/ users make sense of data.

For more on this, subscribe to my Data Ecosystem newsletter (link in the comments) where I go into more detail into Data Platforms, what they really mean and how they should be built!

I think a lot of the hype around AI is super misguided. There is a huge focus on tools or tech, but that angle is missing the point

Realistically the business model, org structure and operational systems were never designed for AI, yet AI is just being bolted on to a broken ecosystem

If I were to put a rough label on what this means, I think it's something like 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝐃𝐞𝐬𝐢𝐠𝐧. This is where "systems" means the full sociotechnical stack: architecture, operating model, tooling, governance, and human interfaces all designed as a coherent whole rather than assembled, standalone pieces

To be clear, this idea isn’t enterprise architecture, data/ AI strategy or even AI solution implementation. Rather it’s the holistic approach to AI that properly approaches a legacy business that needs to be refactored and redesigned for where it is trending. In my mind, this has three layers:

𝐓𝐡𝐞 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐥𝐚𝐲𝐞𝐫 is the foundation for your AI and data systems. Traditionally you'd architect your data systems based on technical data requirements. But now your architecture needs to be designed with from the perspective of AI and how it interacts with the rest of the business. I call this the 𝑨𝑰 𝒊𝒏𝒕𝒆𝒓𝒂𝒄𝒕𝒊𝒐𝒏 𝒎𝒐𝒅𝒆𝒍. How will agents traverse this system? Where do you need deterministic guardrails vs. probabilistic reasoning? What does a trustworthy handoff between systems look like?

𝐓𝐡𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐦𝐨𝐝𝐞𝐥 𝐚𝐧𝐝 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥 𝐥𝐚𝐲𝐞𝐫 is where the organizational strategy and realities integrate with AI’s operational requirements. This is where most companies fall apart. Rather than bolting on these things to the existing org structure, 𝒄𝒐𝒎𝒑𝒂𝒏𝒊𝒆𝒔 𝒏𝒆𝒆𝒅 𝒕𝒐 𝒓𝒆𝒇𝒂𝒄𝒕𝒐𝒓 𝒕𝒉𝒆𝒊𝒓 𝒐𝒑𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝒎𝒐𝒅𝒆𝒍 𝒂𝒓𝒐𝒖𝒏𝒅 𝑨𝑰-𝒂𝒖𝒈𝒎𝒆𝒏𝒕𝒆𝒅 𝒘𝒐𝒓𝒌𝒇𝒍𝒐𝒘𝒔. With the systems reorganized with AI architecture embedded, AI can better reflect how the business actually intends to operate in the future.

𝐓𝐡𝐞 𝐢𝐧𝐭𝐞𝐫𝐨𝐩𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐭𝐫𝐮𝐬𝐭 𝐥𝐚𝐲𝐞𝐫 is the most underappreciated component. As organizations accumulate AI tools, you end up with a fragmented ecosystem of agents, models, and automations that weren't designed to talk to each other coherently. This creates a siloed system, similar to how data tools/ technologies were set up way back then. How do you redesign how those systems interact 𝒕𝒐 𝒊𝒎𝒑𝒓𝒐𝒗𝒆 𝒕𝒓𝒖𝒔𝒕𝒘𝒐𝒓𝒕𝒉𝒚 𝒂𝒏𝒅 𝒍𝒆𝒈𝒊𝒃𝒊𝒍𝒊𝒕𝒚 𝒇𝒐𝒓 𝒏𝒐𝒏-𝒕𝒆𝒄𝒉𝒏𝒊𝒄𝒂𝒍 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒔𝒕𝒂𝒌𝒆𝒉𝒐𝒍𝒅𝒆𝒓𝒔? This combines governance, process improvement, and cultural integration upon the AI implementation.

I’m still at the early stages of figuring this out, but as part of my proposition, I’m doing workshops on this and how to embed it in organizations. I’m trialling this with a few organizations at the moment so if it is something you are interested in, let me know.

It's been six months since the last issue of The Data Ecosystem landed in your inbox.

On Sunday, that will finally change

Today, I’m announcing The Data Ecosystem is back!

For me, navigating the data ecosystem topic by topic, meme by meme, was never about creating a popular newsletter or building out my social media following. 

It was about solving the complexity of the data ecosystem. Making each topic easier to understand. Trying to figure out how this industry actually fits together. 

The data industry has a ton of great expertise, and it by no means lacks content. But it does lack connected content. You get thousands of posts about engineering, Python, data mesh, or AI (especially now), but almost nothing that explains how these things relate to each other, and why that relationship matters to you and your organization.

That's the gap The Data Ecosystem was built to fill. And we're far from done filling it.

So here is the release schedule/ plan for the next two months:

𝐌𝐚𝐫𝐜𝐡 
🏗️ Business Models Part 1 – Explaining them and why they matter
🔗 Business Models Part 2 – Their relevance for the data world
🤖 Business Models Part 3 – Reconstructing them for the world of AI

𝐀𝐩𝐫𝐢𝐥 
🧭 Data Strategy Part 1 – Setting your strategic data direction
⚙️ Data Strategy Part 2 – Understanding how to execute on that direction
🎯 Use Cases – What are they and how to frame them to create value
💰 Valuing Use Cases – Potential guest article by Nick Zervoudis 

After that we’ve got a lot more topics. Also open to ideas, perspectives, guest collaborations and sponsorship articles!

The main thing in my head, though, is that it’s good to be back!

In this hectic and crazy AI-enamoured world, companies need partners that are both strategic and technically minded, focused on delivering tangible value

But also those who aren't stuck in legacy ways of thinking, bureaucratic policies or expensive engagement fees

As I enter the freelance world, I want to be that partner. I've seen what works and what doesn't in consulting, and know the industry needs a fresh perspective on how and what it delivers to its customers

So after a decade across 4-5 consulting firms (big, small, in-between), here are the propositions I’m building for the market:

1️⃣ 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐃𝐚𝐭𝐚 𝐀𝐝𝐯𝐢𝐬𝐨𝐫𝐲 — A strategic sounding board for senior data leaders navigating complex decisions in relative isolation. It is often hard for Data Leaders to learn about best practices or seek guidance, as peers within the organization don’t fully understand the complexity of what they're dealing with. With monthly or bi-weekly sessions, I can help provide that perspective without shooting from the hip or engaging a consulting firm on a wildly overpriced engagement (I’m also cheaper and better than a Gartner membership…). 

2️⃣ 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐢𝐧𝐠 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 — Scoped engagements across data strategy, governance, data quality, AI product development, operating model design, technology architecture, and ecosystem assessment. I’ve delivered all of these projects before, and do so with a senior mindset and a holistic perspective.

3️⃣ 𝐃𝐚𝐭𝐚 & 𝐀𝐈 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐖𝐨𝐫𝐤𝐬𝐡𝐨𝐩𝐬 — Two distinct workshop offerings designed to quickly create clarity and a path forward. The first is a Data Ecosystem Assessment that maps how data flows through your organization, identifies gaps & interdependencies among your data domains, and provides an actionable plan to holistically address them. The second is an AI Systems Design workshop where we assess the organization’s business model and systems, refactoring them for where AI should play in the future. Both are designed to be customized for your organization while embedding market best practices, with clear, actionable outputs delivered immediately.

If any of this resonates, whether you're a data leader who could use a sounding board, an organization mid-transformation, or an executive trying to figure out where AI actually fits, I'd welcome the conversation.

More details coming soon on each of these. For now, I'm open for business.

The Data Ecosystem is officially back LinkedIn! And we are starting with a good one

As huge AI companies make a mockery of business fundamentals, we are here to explain to them what a business model actually is and the importance of it

Because, despite what these companies say, most companies can’t survive indefinitely by losing money the more they sell…

In this ten minute read , you get the best thinking around:
💡 What Is a Business Model and Why It Matters
🔍 Common Business Model Archetypes
🌟The Evolution of Business Models in the Data Era

Check it out here - https://lnkd.in/epG-hfr7 

Remember, success in your business and in your data team starts with the business model. It makes up so much of how the company operates, makes money and differentiates that it is impossible to ignore (or you can ignore it to your own peril)

The next two weeks we will get deeper into the future thinking on business models by looking at why they are important for all domains across data and how they will evolve with what we are seeing in the AI world

Oh and finally, if you don’t recognize this meme or scene from Arrested Development, go watch it. It is comedy gold

If you build it, they will come…user feedback that is, not users 😯

In the end your dashboard isn't for you, it's for the user! 

And their feedback isn't just advice; it's actionable intelligence. So here are some tips:

👂 𝐋𝐢𝐬𝐭𝐞𝐧 𝐛𝐞𝐟𝐨𝐫𝐞 𝐝𝐞𝐟𝐞𝐧𝐝𝐢𝐧𝐠 – Your dashboard may be your baby, but your baby needs to grow up. And when feedback comes in, absorb it before formulating a counter-argument

🔎 𝐒𝐞𝐞𝐤 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐬𝐢𝐥𝐞𝐧𝐭 𝐦𝐚𝐣𝐨𝐫𝐢𝐭𝐲 – Only a fraction of users voice their opinions so find ways to capture input from others through surveys or one-on-one chats.

💡 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞, 𝐝𝐨𝐧’𝐭 𝐩𝐚𝐧𝐢𝐜 – Not all feedback needs immediate action. Categorize suggestions into 'Now,' 'Soon,' and 'Later' buckets based on urgency and impact.

🔄 𝐈𝐭𝐞𝐫𝐚𝐭𝐞, 𝐝𝐨𝐧'𝐭 𝐨𝐯𝐞𝐫𝐡𝐚𝐮𝐥 – Small, frequent updates are better than rare, seismic shifts. Your users will thank you for not changing everything at once.

📏 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐭𝐡𝐞 𝐢𝐦𝐩𝐚𝐜𝐭 – Before and after making changes, look at metrics like user engagement and task completion times. Did your tweaks lead to improvements?

⚙️ 𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐲 𝐭𝐡𝐞 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧𝐬 – Explaining the connections between databases and dashboard tools goes a long way in creating trust. Using tools like Orchestra (which are drag and drop, low-code) to do this make it that more relatable for stakeholders

🗂️ 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭, 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭, 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭 – Keep a log of all feedback and changes to track your progress and provide insights for future projects.

🙏 𝐀𝐜𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐚𝐧𝐝 𝐭𝐡𝐚𝐧𝐤 – Remember to thank the feedback-givers. Even a simple "Thank you for your input!" goes a long way in building a positive feedback culture.

So, how do you handle feedback for your dashboards? Any other tips?

The best thing about data is the conversations everybody wants to have to make things better

One of my favourite topics to discuss is data quality and its recent relevance in this new AI world

Teams need to take the time to think about this intersection in more detail because they are missing out

…And trust me, 80-90% of the organisations I work with aren’t doing this. There are so many data quality quick wins they can invest in to get them to the next level, if only they dug into it further and spent some time really thinking about what works!

 DQE, Data Quality Everywhere released their latest “Let’s Talk Data” podcast, where I had these great conversations with Philippe BOULANGER, PhD. It was a ton of fun and I recommend a listen for anybody wanting to learn more about the intersection of data quality and AI!

Open article: Ep2 | Data quality: How AI can turn data quality from a challenge into a competitive advantage by smartlink.ausha.co

Ep2 | Data quality: How AI can turn data quality from a challenge into a competitive advantage

Do you know what your company’s business model is?

Well, it is likely one of these six. Understanding how it is made up and what makes it successful is essential for your career and to ensure your work adds value

📦 𝐏𝐫𝐨𝐝𝐮𝐜𝐭-𝐅𝐨𝐜𝐮𝐬𝐞𝐝 – A business develops and sells a tangible thing or digital artifact for a one-time fee or a subscription.  These businesses generate revenue from each product sold, with the highest expenses tied to the cost of the goods and sales/ marketing to promote their sale

🧑‍💼 𝐒𝐞𝐫𝐯𝐢𝐜𝐞-𝐅𝐨𝐜𝐮𝐬𝐞𝐝 – Provides intangible solutions delivered on an ongoing or project basis, such as consulting, maintenance, or delivery support. Many companies offer both products and services, such as banks that might charge you for a loan (a product) and financial advice (a service)

🛒 𝐌𝐚𝐫𝐤𝐞𝐭𝐩𝐥𝐚𝐜𝐞 – Creates a platform for third-party sellers and buyers to transact, making revenue from commissions or fees for enabling the marketplace. The primary product is the marketplace or environment to facilitate these types of sales

🔁 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧 – Offers continuous access to a product or service for a recurring fee, combining product support and ongoing updates or content. You could bucket this into product/ service, but I separated it

💱 𝐓𝐫𝐚𝐝𝐢𝐧𝐠/ 𝐁𝐫𝐨𝐤𝐞𝐫𝐚𝐠𝐞/ 𝐀𝐠𝐞𝐧𝐜𝐲 – An extension of a marketplace, but more specific by acting as an intermediary to connect buyers and sellers or facilitate transactions/ arrangements. All of this is done for a fee or commission, or the business pockets the profits from any price differences

🏪 𝐅𝐫𝐚𝐧𝐜𝐡𝐢𝐬𝐞/ 𝐋𝐢𝐜𝐞𝐧𝐬𝐢𝐧𝐠 – A business that grants others the right to operate under its brand, processes, or intellectual property in exchange for fees or royalties. The revenue comes from enabling others to execute the business model via brand licensing

From a data perspective, each business model archetype has different analytical priorities or needs while measuring different KPIs. Understand those for your company by starting with these business fundamentals, trust me!

Read the full article on business models in my newsletter from this past week (we have two more articles on how you need to adapt these archetypes for data operations and in the AI-world coming up)

Ultimately, success in business cannot be defined by a single idea, financial results, or a single product. 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐬𝐭𝐚𝐫𝐭𝐬 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐦𝐨𝐝𝐞𝐥.

Jeff Bezos initially sold books online, but his business didn’t end there. Amazon’s business model is 𝐜𝐞𝐧𝐭𝐫𝐞𝐝 𝐨𝐧 𝐜𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐜𝐨𝐧𝐯𝐞𝐧𝐢𝐞𝐧𝐜𝐞 𝐟𝐨𝐫 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬, enabling the organization to evolve from selling books into an unparalleled e-commerce empire, underpinned by Prime membership and world-class logistics.

Satya Nadella helped Microsoft deliver incredible business results by shifting the company’s business model from a 𝐡𝐚𝐫𝐝𝐰𝐚𝐫𝐞 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐫 𝐭𝐨 𝐨𝐧𝐞 𝐭𝐡𝐚𝐭 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜𝐚𝐥𝐥𝐲 𝐟𝐨𝐜𝐮𝐬𝐞𝐬 𝐨𝐧 𝐜𝐥𝐨𝐮𝐝, 𝐀𝐈, 𝐚𝐧𝐝 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬.

𝐓𝐡𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐦𝐨𝐝𝐞𝐥 𝐢𝐬 𝐭𝐡𝐞 𝐛𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐟𝐨𝐫 𝐡𝐨𝐰 𝐚𝐧 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐨𝐩𝐞𝐫𝐚𝐭𝐞𝐬, 𝐦𝐚𝐤𝐞𝐬 𝐦𝐨𝐧𝐞𝐲, 𝐚𝐧𝐝 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐬 𝐯𝐚𝐥𝐮𝐞 𝐭𝐨 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬. It underpins everything that happens, from product development to supply chain to customer experience.

𝒀𝒆𝒕, 𝒕𝒐𝒐 𝒐𝒇𝒕𝒆𝒏, 𝒕𝒉𝒆 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒎𝒐𝒅𝒆𝒍 𝒊𝒔 𝒑𝒐𝒐𝒓𝒍𝒚 𝒅𝒆𝒇𝒊𝒏𝒆𝒅 𝒐𝒓 𝒎𝒊𝒔𝒖𝒏𝒅𝒆𝒓𝒔𝒕𝒐𝒐𝒅.

The inability to understand and explain the business model is 𝐟𝐮𝐫𝐭𝐡𝐞𝐫 𝐞𝐱𝐚𝐠𝐠𝐞𝐫𝐚𝐭𝐞𝐝 𝐢𝐧 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐰𝐨𝐫𝐥𝐝. 𝐃𝐚𝐭𝐚 𝐩𝐞𝐨𝐩𝐥𝐞 𝐨𝐟𝐭𝐞𝐧 𝐥𝐚𝐜𝐤 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐨𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞. Instead of learning about revenue, costs, and customer journeys, they learn about data cleaning, modelling, and technical skills. What you get is businesspeople speaking one language and data professionals speaking another.

The result? 𝐓𝐡𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐦𝐨𝐝𝐞𝐥 𝐮𝐧𝐝𝐞𝐫𝐩𝐢𝐧𝐧𝐢𝐧𝐠 𝐜𝐨𝐦𝐩𝐚𝐧𝐲 𝐬𝐮𝐜𝐜𝐞𝐬𝐬 𝐢𝐬𝐧’𝐭 𝐰𝐞𝐥𝐥 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐨𝐨𝐝 𝐛𝐲 𝐝𝐚𝐭𝐚 𝐭𝐞𝐚𝐦𝐬. Nobody will admit this, but this (alongside a lack of understanding of business strategy) is the root cause of most issues that keep companies from getting value from their data.

I wrote about this exact subject this week in The Data Ecosystem, definitely an article you should not miss! Link is in the comments, enjoy!

If we were to simplify the business model, there are three main questions you need to answer:
🏢 What does your business do?
💰 How does your business make money?
⭐ What is your differentiator?

These questions might seem simple, but they form the foundation of every successful organization. Let me break down why each matters and how they connect to create your business model framework.

𝐖𝐡𝐚𝐭 𝐃𝐨𝐞𝐬 𝐘𝐨𝐮𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐃𝐨?
- This defines the purpose of why your company exists and its value proposition to the market/ customers
- The problem? Companies either (1) overcomplicate what they do or (2) explain it in a highly rational and unappealing way. Your value proposition should be compelling, not a corporate buzzword salad

𝐇𝐨𝐰 𝐃𝐨𝐞𝐬 𝐘𝐨𝐮𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐌𝐚𝐤𝐞 𝐌𝐨𝐧𝐞𝐲?
- Understanding revenue generation is vital for assessing financial health and sustainability
- Different revenue models create different organizational behaviours and priorities. A subscription business operates very differently from a one-time product sale business.

𝐖𝐡𝐚𝐭 𝐈𝐬 𝐘𝐨𝐮𝐫 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐭𝐨𝐫?
- This uncovers what sets your business apart from competitors. Your differentiator is your competitive advantage—the unique selling proposition that gives you a market edge.
- Strong differentiators contribute to brand identity, customer engagement, innovation, etc.

𝑻𝒉𝒆𝒔𝒆 𝒕𝒉𝒓𝒆𝒆 𝒒𝒖𝒆𝒔𝒕𝒊𝒐𝒏𝒔 𝒆𝒏𝒄𝒂𝒑𝒔𝒖𝒍𝒂𝒕𝒆 𝒕𝒉𝒆 𝒆𝒔𝒔𝒆𝒏𝒄𝒆, 𝒗𝒊𝒂𝒃𝒊𝒍𝒊𝒕𝒚, 𝒂𝒏𝒅 𝒄𝒐𝒎𝒑𝒆𝒕𝒊𝒕𝒊𝒗𝒆 𝒑𝒐𝒔𝒊𝒕𝒊𝒐𝒏𝒊𝒏𝒈 𝒐𝒇 𝒂𝒏𝒚 𝒄𝒐𝒎𝒑𝒂𝒏𝒚 
Being able to answer them succinctly helps businesses communicate strategy, attract partners and investments, and guide operational decisions.

Answering these three questions 𝐞𝐧𝐚𝐛𝐥𝐞𝐬 𝐲𝐨𝐮 𝐭𝐨 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐞 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐬𝐭𝐚𝐧𝐝𝐬 𝐟𝐨𝐫. And that helps you determine 𝐡𝐨𝐰 𝐭𝐨 𝐮𝐭𝐢𝐥𝐢𝐳𝐞 𝐝𝐚𝐭𝐚 𝐭𝐡𝐫𝐨𝐮𝐠𝐡𝐨𝐮𝐭 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐭𝐨 𝐝𝐫𝐢𝐯𝐞 𝐦𝐚𝐱𝐢𝐦𝐮𝐦 𝐯𝐚𝐥𝐮𝐞.

And value is a highly sought-after aspiration, so understanding it is something any data professional (regardless of their role) should aim for, as it helps them contribute to the delivery of the wider business model.

Read the full article on business models in my newsletter from this past week (we have two more articles on how these will adapt in the AI-world coming up too)

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