How to shape business metrics and stories into Dashboards

Are business Dashboards easy to design? You tell me: you’ll need to draw a clear picture of the most valuable information about a business subject, in less than a minute. Of course, it should also be visually appealing, interactive, quick, and self-explanatory, what else?!


Although for many designers the Dashboards conception and development are intuitive activities, especially since the explosion of friendly tools and self-service BI, I believe that some guidelines and a methodology are necessary. To make a difference in the field we need to define our own style, our signature.

I have constantly been trying to “discipline” myself…

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How human perception drives Dashboard designers

Design is one of the essential factors of a successful dashboard. At least as much as its content and the quality of the underlying data. Whereas the dashboard’s content and data quality are clearly measurable, design is more difficult to evaluate objectively, but fortunately we have some guidelines.

Design can cause weakness

Why boring dashboard are boring? Why engaging dashboards are engaging? Dashboard designers need to know what can push a data visualization to rank low or high in the user’s appreciation range.

Some empiric but effective ideas that can give Dashboard design hints are the “Conventional” and “Gestalt” principles.

In this story I…

Image from Patrick Tomasso edited by Author

Tableau recently released a new feature “Quick LOD” that should make it easier to build LOD calculations. As an intensive LOD user, I want to try this feature out and share my thoughts with you.


· Quick reminder about LOD expressions
Testing "Quick LOD"
Testing Outcomes
In a nutshell…

Quick reminder about LOD expressions

When we build a data visualization we often need to dispose of multiple aggregation levels of the same measure at the same time, by using LOD calculations.

If we make a conceptual parallel with SQL, LOD expressions allow defining more than one different GROUP BY clauses in…

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Tips and Tricks

An effective approach to analyse and understand complex SQL

In this story I want to share a possible way to tackle the tricky understanding of complex (or complicated) SQL queries. When we need to handle some existing SQL code, maybe non-documented, poorly written, or uses SQL versions that we don’t master just yet, it can be frustrating to deal with it.

Whatever thing we need to do with it, just running it as it is, using it in a Data Science or Analytics process, modifying it, etc., in any case we need to understand it.


· What can make SQL tricky · Breaking SQL complexity ∘ 1. Improve…

New data specializations always offer opportunities to the ones who can see them coming.

In this 2-parts story, I want to share some ideas about Self-Service Analytics.

In Part-1 I give an overview of what SSA is, explain the opportunities it offers to Data passionates, and walk through the subjects one needs to learn or improve to work in Self-Service Analytics.

In Part-2 I look in more detail at the needed skill sets, toolkits, and possible entry points to plan the SSA learning.

Revolutionary SSA

I call “Self-Service Analytics” the stack techniques and skills that allow anyone (not only technical users) in…

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This divulgation series aims to explain different topics about Data subjects, in a simple but effective way.


In general, a data model is a representation of how data is organized into database tables. I know, that’s not a rigorous/academic definition, but my only goal here is to be clear. For a given collection of data, representing a Business Process, there is a number of possibles ways to organize store them: do we store all data in 1 big table? Or in 2, in 100,.. ? It depends. For any Data Analytics- driven subject, ranging from classic Business Intelligence to the wildest Data Science applications, a very comfortable and efficient way is using Dimensional Modeling.

The topics will…

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Hands-on Tutorials

Anatomy of a KPI template visualization in Dashboards. Hands-on demo with Tableau, from scratch.

Key Performance Indicators (KPIs) are the bricks of any Dashboard, in any Business domain. A smart way to make those bricks is developing a KPI template, which is a set of expressions and parameters that blend data to explain the Business. Ok, but how can we make a KPI template?


In this article, I’m going to explain and show how to develop the underlying objects of a clean KPI visualization in a professional Dashboard. The idea is to build a KPI template that can be applied to any business domain.

I will do it by Tableau, but it will be…

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How to make your SQL upskilling more effective, through ready-to-go leader databases (free).

“Upskilling” in any Data topic means 2 complementary things: learning new skills (languages, algorithms, etc.), and keeping up-to-date the skills you have already built.

In this story, my goal is to share with you some ways to upskill SQL / PL-SQL / T-SQL skills.

Let’s keep SQL-laziness away

Any Data Science discipline, or any job title containing the “..Data..” word, requires at some point to query a database and to do some developments. Actually, any job interview I ever did (either as an interviewer or interviewee) tested the candidate’s SQL expertise, among other things.

But here comes the problem: once we get the job…

2020 gives birth to the unified “Social Credit System” in China. Before it becomes pandemic, I explain why Data Science can’t judge humans.

2020 will go down in history as the year of Covid-19. But another event could impact society even more: the final phase of the “Social Credit System” implementation in China.

I am going to share with you some of my thoughts on the topic, from a Data perspective. And I want also to raise some questions in particular (but not only) for Data Scientists.

Why do we care about the Chinese case? Because it is the first…

My 0$ “real-world” Data Science environment at home: Database, ETL, Data analytics, Dashboarding, in 9 steps.

In this my first Medium post I want to share with you a way to create a “real-world” data environment at home, based on the same components that most companies use to handle their data. Of course, on a much much smaller scale, and for free.

Why? I used similar frameworks when I was preparing for job interviews, to learn more about new Data Science tasks, reporting, database administration and development, and also to organize my own work.

The framework I’m presenting here is…

Maw Ferrari

Data freelance. Data literacy evangelist. Math & music hobbyist.

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