I post a lot on LinkedIn. I use it as others would use Twitter almost. I enjoy writing on the platform because it allows me to voice ideas, have them challenged and develop them as a result. The audience is right, the feedback is instant, and it's where my professional world lives.

But every time I publish something, it disappears into a feed I don't own. LinkedIn decides who sees it, when, and for how long. If the platform changes tomorrow, that content is gone. That bothers me.

Originally I solved this by maintaining a personal and public Notion page. It also allowed me to find back thing I had written that could be useful for customers, the team, etc. But it lacked the character and simplicity I loved so much during the early days of blogging and Tweeting.

So I built Mind, a simple personal CMS that acts as a permanent archive of everything I think, build, and publish. It’s my professional archive on the web. It’s simple, it’s fast, it’s boring. And most importantly - it’s easy, not complex.

The name "Mind" is literal. This site is a representation of my mind. The ideas I care about, the projects I've worked on, the talks and articles that made it out into the world. All in one place.

I wanted it to feel like a micro-blog. No comment sections, no follower counts, no algorithmic feeds. Just content, chronologically ordered. I grew up on Myspace, Noxa and Tumblr and I miss that simplicity. A place where you just drop things and they stay. The stack is intentionally minimal. Next.js and React for the frontend, Supabase for the database and image storage, Tailwind for styling, and Vercel for hosting. A simple admin panel allows me to write and publish. No CMS platform, no WordPress, no dependencies I don't control. The whole process was a fun learning experience, as always.

So now you can find out what’s going on in my mind either here, or on glennv.com

Enterprise organisations have hidden the last decades behind the “we are too big to fail” mentality to ignore and minimise their lack of speed, service and quality.

In a lot of cases this resulted in a culturally accaptable lack of ambition.

The narrative no longer sticks. Timelines for everything just got dramatically shorter and enterprises aren’t able to deal with it. Their structure prohibits them from moving fast. That is even if their culture hasn’t killed the excitement for change already.

The timeline is no longer 10 years. It’s significantly shorter than any of us can estimate atm.

A great reset is coming.

During a dashboard walkthrough, a client stopped us mid-explanation: "Oef, that looks complicated. I'm just going to ask the agent to take care of it."

And it worked. People are moving to conversational analytics.

It’s here. It’s happening.

Is it me or are we going from:

"VC backed organisation with no LT revenue model burning cash on extremely expensive engineers"

to

"VC backed organisation with no LT revenue model burning even more cash on AI agents"

When everything can access the same data we finally start having conversations on what to do next, rather than why my number is different from yours.

"Technology without culture doesn’t work."

— Moonhaven on Netflix.

The way AI articles capitalise every word ("Amazing Article Title With A Punchline") makes me feel like it's 2014 again and I'm writing AdWords ads for my clients.

I’ve been told I’m very bad at corporate politics but that I always speak my mind.

And I take it as a compliment.

• Silence during a deployment gap is a choice to accept churn. Proactivity is a choice to build trust.
• Churn is rarely caused by a generic failure. It is caused by a failure to meet a specific expectation at a critical milestone.

The answer to a problem is rarely "more meetings".

The decades-old "Pull Model" where a human asks a question and a dashboard provides an answer is dying. The next generation of analytics tool will warn you proactively what to pay attention to and what you next move should be.

Blurb/thought I had this week when seeing a number of new ideas being shared in the form of open-source projects:

If there's an upside to AI generated code it is that it’s boosting the availability of open-source code (and thus sharing of ideas captured by that code).

Couple of thoughts on why this happens:

• A lot of projects that are being released are projects people used to get their hands dirty and are PoCs of sorts
• Because AI makes it so much faster and easier to turn an idea into code, people don't feel the need to guard their work or demand payment like they used to
• I guess they also realise that with AI, it’s now very easy for others to replicate what they’ve built anyway

That doesn't mean all the code is good code. But it does mean an influx of ideas that are openly being shared in the form of code. That's a win for all of us.

If I'm reading your technical docs and I can't figure it out because it's not clear (not because it's too technical) and I need to resort to using Gemini or ChatGPT to know WTF I need to do it means your docs aren't good enough.

"Make it as complex as we need to, not as complex as we can."

Picked up during an internal meeting at Human37. This one goes in the manifesto.

I'm intrigued by the fact that some organisation go through crazy hoops to make their reporting work "as it has always has" even though they have migrated to newer and better solutions that reflect reality better.

How many conversations I have had this year talking about retro-fitting Mixpanel or Amplitude reports to a UA session standard simply because exec reporting works that way is mind blowing.

The fact that you can choose conversion windows simply makes people bug. Break free from the standards Google told you to follow 10+ years ago. Reporting can really be custom for your business. Embrace it.

Real-time data ingestion for personalisation is often the first technical requirement we get when doing customer data strategies.

• (almost) Everyone wants real-time data for personalisation.
• (almost) No one is able to translate real-time data in meaningful customer interactions.
• (almost) No one is able to build a strategy/use case that is ROI positive when taking the cost of real-time ingestion into account.

Real-time use cases are what will move the needle marginally once you've done all the rest. For the vast majority near real-time is good enough because they still have so much ground to cover.

Prioritise, mature and make more money by saving money.

I believe that discipline is one of the most underrated traits someone can have, including in business.

The discipline to do the task whether it’s fun or not, because it is the task that needs to be done in order to move to the next step.

It doesn’t mean I like disciplined people because they take care of the annoying work. I like them because they understand that building something inevitably also includes dull work but that they have the ability to deploy discipline against it to get it done.

I can’t believe how undervalued and untapped the potential of loyalty still is today.

Everyone thinks of growth as acquisition. Growth is significantly easier when churn is low and CLV is up.

I recently came across Paul Graham's blog of essays.

"Knowledge grows fractally. From a distance its edges look smooth, but when you learn enough to get close to one, you'll notice it's full of gaps. These gaps will seem obvious; it will seem inexplicable that no one has tried x or wondered about y. In the best case, exploring such gaps yields whole new fractal buds." source

The following reminded me of something I call "relentless curiosity". When I'm asked what makes people successfully in the role of solution architect my answer has always been being relentlessly curious. Asking more questions to build an understanding since understanding is required to craft solutions.

What PMs can learn from Deadmau5 and the creation of the song "The Veldt".

"The song was initially created during a 22-hour live streaming session in March 2012. The following day, Zimmerman (Deadmau5) found Chris James, who created his own vocal rendition of the song, via Twitter. Zimmerman was impressed with James' vocals, particularly with the lyrical references to the Ray Bradbury story, and confirmed that the official release of the song would include James' vocals." - From Wikipedia.

The discovery was actually captured during the livestream and is now hosted forever on Youtube.

The song currently has 86M streams on Spotify.

PMs - Even though you might think you know better. Listen to your users, you might be missing more gems than you know. You're never that good that you don't need user feedback.

Wishing you all a "The Veldt" moment.

When everything connects to everything the risk is that data ends up in platforms for which customers never have given their consent. Think of data captured under analytics consent that's being streamed to marketing and advertising platforms.

Interestingly the vendors consider the lack of guardrails none of their responsibility while everyone is proud of their downstream steaming capabilities. When asked about it (I have :-)) everyone seems to be pointing towards the data warehouse as "the central point where the problem should be managed".

When a vendor says they're privacy centric but state that "privacy should be handled in the data warehouse" what they're saying is "you're on your own". It's a nice way of saying "solve it yourself".

Bottom line - Don't pay too much attention to privacy positioning of vendors. It's a topic that needs to be mentioned and requires some checkboxes from a gtm perspective. Very few are able to actually provide extensive guardrails. Ensure you have your own processes to put guardrails on how data gets piped from one tool to another. Cause you are really on your own.

Most companies don’t really do analytics. They simply do tracking.

And if you’re lucky some reporting.

I wish more organisations would “do” vs “talk about what they’re going to do”. So many people are paid to just do the latter. It’s mad. Most things aren’t that hard.

I want to build things, make progress. And slides don’t count.

The difference between customer support and excellent customer support?

Provide solutions, not only answers.

I don't care that the feature does not work this way, tell me which part of your product might be able to facilitate what I'm actually trying to solve.

The same goes for consulting btw.

If you work in analytics, data or are a data consumer in any way (yes, that includes asking questions to analysts) you need to be familiar with Kidlin's law.

Kidlin's law - "If you write the problem down clearly, then the matter is half solved."

Analytics is the side of the business where everything has been reduced to data points while the business questions we're trying to answer are usually "big", "opaque" or "vague".

Writing down these questions clearly forces you to specify different pieces of the problem and refine them. As a result, it helps everyone to understand which assumptions, hypotheses and data points are required to compose an answer to the questions.

I only recently learned about Kidlin's law even though we've been adopting something similar called "user stories" at Human37 since day 1.

This one is for all the Solution Architect (or whatever the equivalent role) at SaaS companies and was triggered by a SaaS vendor asking me what I thought of their Solution Architect / professional services team.

Note this is a reflection on SaaS SAs. I'm not speaking on behalf of service provider SAs like ourselves here.

As far as I'm concerned Solution Architects in SaaS (or whatever the equivalent role name) - are the "special forces" within SaaS organisations.

• They have a deep understanding of the product, its capacities and limitations. They understand all the tools, why they are used and what their limitations are.
• Unfortunately in most cases they're brought in to turn a bad situation around (this includes a bad sale in many cases).
• Adapt and overcome is what they do when the product hits a limit when executing a client case.
• They consistently get undervalued, are never put in the limelight and rarely get any credit.
• They usually operate like one man armies dealing with different client teams. As a result they need project management, people and technical skills. Most of them are well rounded profiles.
• When they're not working on a client they train. I know SAs who have a backlog of tests they "would like to run" to identify weak spots in the product to anticipate any upcoming scenario.

This is a call for SaaS companies to value their SAs. They are dealing with clients every day and know how the product is being used and misused. If you're in product make sure to have a direct line with them. Unfortunately it's rarely the case that SAs can directly feedback into product. This should change.

SAs are the SaaS heroes that don't wear capes and save the day. They deserve more credit.

I've been lucky enough to work with some stellar teams and have learned a ton from them.

When starting with a customer data strategy marketers systematically overestimate the value of behavioural data when it comes to driving value for the business.

I've found that organisations who start with use cases that leverage confirmed 1st party data are able to drive results more effectively vs the ones that focus on use cases leveraging behavioural data.

Leveraging existing data from a single 1st party system (e.g: transactional data) can drive more value for an organisation than anonymous (or even identified) behavioural data. That last one will almost always only leads to marginal gains.

My hypothesis is that the reason for this focus on behavioural data is that marketers have always been using with anonymous data gathered through tags and pixels.

💭 Focus on confirmed behaviour vs implied behaviour. Especially when the actions you are taking based on this data are non-existent to start with.

I'm intrigued by the fact that there's organisations who believe they need server-side GTM tracking (or more complex setups) to "regain" X% of their tracking in order to then do ABSOLUTELY NOTHING with the data they regained 🤡.

Know what you build/buy and why. Don't just do it because "people say you need to".

And please, only capture data to use it. Not just to have it.

"Banks got robbed because they hold money, companies get robbed because they hold data. So plan for when it happens, not if it happens."

That's an idea I shared with a number of organisations during a series of sessions around data & ethics.

But why am I sharing this today? Because this morning I woke up to a message from The North Face indicating that they suffered a data breach and some of my data might be impacted.

Even though the news is 💩 , it was "good" to read that a process was in place. They shared:

• What happened and how they reacted. Authorities were involved and the breach was submitted to the data governing authority.
• What information could be impacted. It looks like they are applying data minimisation principles to reduce the risk of WHEN a breach happens.
• How this could impact me. -A point of contact to raise questions.

Prepare for when, not if.

📣 Analytics is bigger than GA4 📣

There I've said it.

Every time I go to an analytics conference I'm surprised that 90% of the content is about GA4. The market has never been so diverse as it has been today. Go explore it.