The start of a new year is often the right moment to slow down, and think about yourself.
To invest in your skills.
To strengthen your foundations.
To question how (and why) you work the way you do.
We live in a time where many organizations believe that AI will automatically optimize our work.
That it will fix inefficiencies, clarify decisions, and save time.
I don’t believe that.
Before optimizing dashboards, before accelerating workflows, before adding AI on top of everything, we should first be confident about:
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the quality of our data
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the methods we rely on
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and our understanding of the business we serve
Because optimizing or replacing dashboards that are:
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not consulted
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not trusted
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or not actionable
doesn’t create value.
It only creates the illusion of progress.
A small encouragement
If you want to build dashboards with clarity and intent treating them as products with a clear purpose, not just outputs, this is where it begins.
I’ve gathered everything I teach about Design principlesfor Data Visualization in this training : Learn Design Driven Data Visualization.
As a thank-you for being a Dataviz Clarity subscriber, access is temporarily available with a 40% discount for the next 24 hours only.
To get the 40% discount, use the coupon "SUBSCRIBERS2026"
Just a way of thanking you and helping you to start 2026 the right way.
This offer ends on Wednesday the 6th at 11:00 AM (Paris time).
A sequence that actually works
1. Understand the problem
Top priority: disqualify it if it doesn’t create value.
Before thinking about solutions, tools, or optimization, the first job is to decide whether the problem is worth solving at all.
If it doesn’t support a real decision, answer a meaningful business question, or improve an existing workflow, the best solution is often… no solution.
2. Create the solution
Top priority: always design for real users, in their real context.
A solution only exists if it works for:
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end users
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their environment
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their constraints, habits, and level of data literacy
Designing in abstraction is how we end up with dashboards that look good but fail in reality.
3. Deliver
Top priority: make it usable today, tomorrow, and at scale.
Delivery is not about "shipping something".
It’s about ensuring that what you deliver can be:
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understood quickly
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reused consistently
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maintained over time
If it only works once, it’s not a delivery. It’s a prototype.
Bonus: Create your own process (and keep it simple)
Define a clear design process.
Then regularly revise it by removing everything that slows you down.
Keep in mind that "What-if" scenarios kill time to market.
Over-adaptation kills adoption.
If you try to adapt your process to every team, every exception, every edge case, you’ll end up with a giant factory that no one follows.
A good process is not flexible by default.
It is clear, lightweight, and repeatable.
Dataviz Clarity 2026 roadmap (1st smester)
Q1 - 2026
Launch of 3 core offers:
- Audit & Interviews
A diagnostic offer based on audits and stakeholder interviews to establish a clear state of the art and identify priority improvement areas. (currently ongoing for 3 companies) - Dashboard Design Framework
A structured framework delivered through workshops and coaching, helping teams design clear, purposeful, and decision-driven dashboards. (currently ongoing for 5 companies)
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Data Transformation Offer
A comprehensive offer focused on mindset shift and analytics maturity, combining learning paths, workshops, and mentoring for business and data teams. (currently ongoing for 4 companies)
Foundational work:
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Translation of the website and all offers into French 🇫🇷
Q2 - 2026
Content & audience development:
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Quarterly free webinar or free training
A recurring free session delivered via email or video to educate Data Analysts and leaders on the Design principles (format to be finalized). -
Micro-learning video series
Short, focused videos for data analysts and small analytics teams, centered on practical design principles. -
YouTube channel launch
Long-form educational content around analytics clarity, dashboard design, and product thinking.
New training module:
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How AI Can Support Better Problem Discovery
A dedicated module focused on using AI to improve discovery, framing, and understanding of business needs not dashboard optimization.
If you're interested, feel free to book a slot with me right now (click here).
Aurélien
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