[🇧🇷] Altamente recomendo ler os artigos diretamente da fonte:
[🇬🇧] I highly recomend reading the articles straight form the source:
O Amodiovalerio Verde fez uma apresentação na PendomoniumX Munich 2025 e fez uma apresentação que me chamou a atenção por conta desse slide:
Também vale a pena ler:
Beyond Dashboards: Why Your Beautiful Dashboards Might Be Making You DumberTL;DR (for bullet-point enthusiasts)The Illusion of ClarityPrinciple 1: Avoid the Data DelusionTL;DR (for bullet-point lovers)Principle 2: Adopt a Data-Informed ApproachTL;DR (for people who believe reading full paragraphs is optional):Final ThoughtPrinciple 3: Choose What to MeasureTL;DR (for teams still adding metrics like it’s a hobby):Principle 4: Use Frameworks as Filters, Not BlueprintsTL;DR (For Leaders Scanning Before Their Next Meeting)Principle 5: Focus on Adoption, Not Just DeliveryTL;DR (For Leaders Reading This Between Two Strategy Calls)Principle 6: Know Your Tool Stack’s BoundariesTL;DR (for the confident scroller):Principle 7: Build Layered Dashboards to Scale Thinking
Beyond Dashboards: Why Your Beautiful Dashboards Might Be Making You Dumber
TL;DR (for bullet-point enthusiasts)
- Dashboards are not decisions.
- AI won’t replace judgment – it exposes it.
- This is the intro to an 11-principle series on data, decisions, and AI.
- Yes, it’s long. Clarity is worth your time.
- Slides coming later for your team debates.
The Illusion of Clarity
- We track everything and decide nothing.
- Features shipped, no one used them.
- Metrics moved, no one knew why.
- Roadmaps full. Strategy hollow.
What’s coming next?
Here are the 11 principles we’ll explore:
- Avoid the Data Delusion Statistically significant. Strategically irrelevant. That’s the trap.
- Adopt a Data-Informed Approach Being data-driven is like staring into the fridge hoping a meal appears.
- Choose What to Measure A metric without a decision is expensive noise.
- Use Frameworks as Filters, Not Blueprints Frameworks don’t decide for you – they stop you from staring into the void.
- Focus on Adoption, Not Just Delivery Shipping is a cost. Adoption is the asset.
- Know Your Tool Stack’s Boundaries You don’t have one truth. You have a stack of partial truths.
- Build Layered Dashboards to Scale Thinking One-size-fits-all dashboards fit no one. Especially your executives.
- Manage Multi-Product Portfolios Separately Blended metrics create Franken-metrics. Useful to no one.
- Reconcile Metric Definitions Before Analysis If teams argue about numbers, they’re arguing about definitions.
- Build Thinking Systems, Not Reporting Systems Dashboards aren’t the goal. Better decisions are.
- Turn AI into a Judgment Multiplier AI multiplies judgment. Without judgment, there’s nothing to multiply.
Principle 1: Avoid the Data Delusion
TL;DR (for bullet-point lovers)
- Avoid mistaking busywork for meaningful progress by questioning if data is creating an illusion of clarity.
- Data without human judgment is just noise; it should be an input for strategy, not a substitute for it.
- Use AI to sharpen your questions and find real problems, not to automate trivial tasks that accelerate waste.
- Focus on whether experiments fundamentally change your direction, not on small, strategically irrelevant wins.
- Shift your team from celebrating data to making decisions by asking what you will do differently with the information.
The real danger is that AI is exceptionally good at making motion look like progress.
…
However, when used with intent, AI becomes a powerful tool for augmenting judgment, not replacing it.
Principle 2: Adopt a Data-Informed Approach
TL;DR (for people who believe reading full paragraphs is optional):
- Being “data-driven” is a trap. It builds passive teams who wait for numbers to give them permission to think.
- Data-informed teams lead with hypotheses, use data to pressure-test thinking, and leave judgment where it belongs: with humans.
- “What does the data say?” is the wrong question. Start with: “What are we trying to learn?”
- AI doesn’t have opinions. If you don’t have a hypothesis, AI won’t help you; it will overwhelm you.
- Shift your mindset: data is not the answer. It’s the sparring partner. You’re the one supposed to think.
Final Thought
- “Data-driven” teams look busy.
- “Data-informed” teams make decisions.
- Dashboards track history. Judgment shapes it.
Principle 3: Choose What to Measure
TL;DR (for teams still adding metrics like it’s a hobby):
- Every metric has a cost. Not money, but something worse: attention. Metrics consume focus, fuel debate, and create cognitive load.
- Track only what informs decisions. Interesting numbers don’t drive action. Vanity metrics waste leadership energy.
- AI will scale whatever signals you feed it. Garbage in? Smarter-looking garbage out. Choose signals that matter.
- Think cockpit, not buffet. Dashboards should steer, not decorate. Track fewer, sharper, decision-driving metrics.
- Use the 6-question checklist before adding any metric. Every number must earn its place.
We measure what’s easy, not what’s useful.
[Me lembrou o princípio ”What gets measured, gets managed”]
Before adding a metric, force this conversation:
- What strategic goal does this support? If unclear, it doesn’t belong.
- What decision will this inform? No decision? Remove.
- What action will we take if this changes? If the answer is "nothing," stop tracking.
- What behavior does tracking this reinforce? Metrics shape incentives. Careful what you count.
- What are we stopping to make space for this? Adding without subtracting is building a landfill.
- Who owns this metric? No owner? No accountability. No point.
Dashboards aren’t reports. They’re steering wheels.
To avoid vanity metrics:
- Start with intent.
- Define the signal.
- Track only what informs action.
Dashboards don’t exist to display data. They exist to help you decide.
Principle 4: Use Frameworks as Filters, Not Blueprints
TL;DR (For Leaders Scanning Before Their Next Meeting)
- Frameworks don’t make decisions. They focus attention.
- Used well, they sharpen clarity. Used poorly, they paralyze teams.
- AI doesn’t solve the problem. It multiplies it, generating frameworks without judgment or context.
- Leadership isn’t about choosing the cleverest model. It’s about enforcing discipline.
- One decision. One primary framework. To enforce clarity, start with one primary framework per decision. The goal is to choose a single, dominant lens for any given problem.
- Stacking models creates noise. Choosing the right lens for the right problem creates clarity.
- Frameworks are useful. Leadership is mandatory.
A framework’s job is simple: Focus attention, highlight signals, and provide a temporary lens for the conversation.
Quick refresher:
- AARRR? Great for growth loops. Useless in understanding user motivations.
- HEART? Good for UX monitoring. Tells you nothing about business impact.
- OKRs? Align execution. But doesn’t account for core product health.
- North Star Metrics? Focus attention, but can focus you on the wrong thing.
- JTBD? Helps you understand needs, but offers no prioritization.
Are we optimizing for clarity or for complexity?
Frameworks don’t prevent bad decisions. They just make bad decisions look methodical.
Framework overload is often a sign of missing focus from leadership.
Someone must choose focus. That’s leadership.
Leadership Checklist: How to Use Frameworks Properly
- Choose One Primary Framework Per Decision. For any single objective, select the one framework that frames the problem best.
- Declare the Boundaries. What does this framework ignore? Make it explicit.
- Name the Decision. What choice is this framework helping to make?
- Challenge the Fit. Why this framework? Why now? Default to rejecting it.
- Lead. Frameworks focus. Leaders decide.
Your customers don’t care what framework you used. They care what you delivered.
Principle 5: Focus on Adoption, Not Just Delivery
TL;DR (For Leaders Reading This Between Two Strategy Calls)
- Shipping is overhead. Adoption is the asset.
- In B2B SaaS, removing features is rarely easily feasible. Prevention might be your only scalable strategy.
- In B2C SaaS, unused features lead to silent churn. Users remove themselves.
- AI won’t tell you what success looks like. It can help surface adoption signals but not define value for your customers.
- Your product is a system for driving outcomes, not a catalogue of releases.
- Shift the conversation from: “What did we ship?” to “What is delivering value?”
The backlog is full. The roadmap is full. Velocity is high. Features are shipping.
And yet… leadership starts asking questions nobody can answer:
- “Are customers using the last three features we shipped?”
- “Which features generate the most value?”
- “What about the ones we built last year?”
Because every feature your teams ship, that your customers don’t use, adds silent operational cost:
- Support tickets (“How do I use this thing?”)
- Training materials (“Here’s how to ignore that setting.”)
- UX clutter (confusing interfaces that hurt adoption of valuable features)
- Maintenance burden (keeping code alive just because someone, somewhere, might use it)
A feature can be technically correct but strategically irrelevant.
Product teams need to think like portfolio managers, not feature brokers.
- What features generate actual value?
- Which ones degrade UX clarity?
- Which features are silent liabilities?
Ask your teams: “Of what we’ve already shipped, what isn’t delivering value? And why?”
That’s where leadership begins.
So the challenge is simple, but not easy:
- Declare adoption as a strategic metric.
- Track it relentlessly.
- Treat non-adoption as debt.
- Prevent before you need to remove.
- Let AI scale your observation, but never your strategy.
If you’re not tracking adoption after launch, you’re not managing a product. You’re managing a feature factory.
Final Reflection: Product is a System. Adoption is Proof.
Your product isn’t a collection of features. It’s a system designed to drive outcomes. Every feature either strengthens that system… or weakens it. Every shipped feature is a strategic decision. Every adopted feature is a strategic victory. Everything else? It’s just overhead.
Principle 6: Know Your Tool Stack’s Boundaries
TL;DR (for the confident scroller):
- Your operational data lives in specialized tools. That’s a reality, not a failure.
- The goal isn’t one dashboard to rule them all. It’s a coordinated system where each tool plays its role.
- Every tool has a purpose and blind spots. Your CRM knows the deal, your analytics knows the click, but neither knows the whole story.
- AI can find patterns across tools, but only if you teach it the boundaries of each data source first.
- Stop duct-taping dashboards together. Start building a system of federated clarity.
You wanted answers. You got complexity.
The myth isn’t that there should be one source of truth.
The myth is that there’s one truth.
… the visual symptom of a deeper issue: a lack of agreement on metric definitions and authoritative sources.
A Quick Story (Because Metaphors Are Sticky)
[🇧🇷] Muito boa história. Vá ler no artigo original.
[🇬🇧] Really good. go to his article to read!