Fabrizio Fantini

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The 5 lessons anyone could learn from the humble salmon

Photo by Markus Spiske on Unsplash

Data is an unreliable friend, and hardly anything about it is actually scientific. So what, Data Science?

Over the past 5 years, I have interviewed more than 1,000 candidate data scientists for an apparently highly coveted set of jobs at Evo Pricing. In the process I have learned that the media are portraying a fundamental lie about this profession: throwing data at off-the-shelf algorithms is really not the point.

A fundamental rethink would be appropriate, and it is likely overdue.

70 years’ history in 2 paragraphs and 1 picture

At its heart, data science is a noble name for a broad set of number crunching activities that were mostly…


Why I’m glad to work with Davide dal Farra

Evo September Spotlight: Davide Dal Farra. Image credits: Evo Pricing (CC with attribution)

Meet Davide

Evo uses AI to help businesses reduce waste and make better decisions. But our AI doesn’t build itself! The Evo Family helps us design, structure, support, run and improve our AI and its underlying infrastructure to provide the best possible recommendations to our clients.

Because our team is critical in making Evo tools a success, we want to celebrate the diverse team members around the world that help us fulfil our mission. Each month we highlight one of our outstanding team members and allow them to share a bit of their day-to-day contributions and accomplishments. They’ll even be sharing a…

Notes from Industry, BUSINESS SCIENCE

The problem isn’t the term; it’s that we don’t really mean it

Source: Piotr Kononow, Dataedo

Every few years, data science and technical terms enter the business lexicon, only to get popularised, over-hyped, and then retired from popularity. Machine learning, Artificial Intelligence, and so many other technologies are following these patterns. Unfortunately, even the most essential ideas can fall victim to this cycle.

The latest victim: data-driven.

What does it mean to be data-driven?


Misinformation and lies are killing innovation in AI: here’s how we can save it

Photo by David Pupaza on Unsplash

This month, I’ve spent a lot of time at business schools teaching people from non-technical backgrounds about data science, analytics and AI. These are smart, well-informed executives, so I’ve been surprised by some of their misunderstandings of what is currently possible with data science, as well as some of their fears about the technology.

This misinformation? It’s coming from us: data scientists.

In our zeal to advocate the possibilities of AI, we’ve lost sight of reality — to the detriment of growth.

Most notable was the CEO forgoing a cutting-edge, proven AI solution for a competitor touting quantum analytics, despite…

Notes from Industry, BUSINESS SCIENCE

The 4 design choices that can undermine ROI and impact

Photo by Jo Szczepanska on Unsplash

Over a year since the start of the Covid-19 pandemic, data scientists are still struggling to get their models back into shape. Every week or so, I see another article lamenting how the disruptions of the past year have negatively impacted machine learning models. Many organisations have stopped trying to adapt and are simply hoping to wait it out until we ‘get back to normal’.

They are going to be in for a shock when they finally realise that there’s no such thing as normal.

All of us working in data science need to recognise the failures that have caused…


Mistakes made by new data scientists are disappointingly similar to those still made by professionals

Photo by NeONBRAND on Unsplash

Over the past year, I’ve had the opportunity to introduce many students to data science. Between teaching an MBA course on Big Data at ESCP and working with students at the Politecnico di Torino to develop an algorithm to forecast trends in fashion as part of a business challenge for CLIK (Connection Lab and Innovation Kitchen), I’ve been able to share what we do with students who may not have otherwise considered its value. Seeing them understand why data science is the future has been gratifying.

In the process, I’ve become much more attuned to the types of mistakes people…


3 analytical approaches to salvage value and prevent waste

Markdowns and waste. Photo by John Cameron on Unsplash

Descriptive, predictive and prescriptive: the 3 approaches you can apply to solve any business problem using analytics. For example markdowns.

Markdowns are big: what to do with excess inventory?

Some statistics from my recent piece: over $2 trillion of inventory in the United States alone — $2,040 billion — $1.43 of inventory for every $1 in sales. So what to do with that 43% excess inventory, when it will reach end-of-life?

When a product exhausts its likelihood of selling, marking down its price is a reasonable approach to try and clear leftover inventory: salvage value, prevent waste.

There are 3…


3 ways to be more responsive to data volatility

Photo by Negative Space from Pexels

Everyone, myself included, was so excited to be done with 2020. The Covid-19 pandemic made it a challenging year, and the arrival of vaccines gave us all hope that things would be going back to normal.

A few months in, however, we realize that it will take some time for life to return to normal. Italy has just gone back into lockdown again, and vaccine rollouts continue to lag. Things are likely to be unpredictable for some time.

Even then, how will we define normal? People remain unpredictable in the best of times. Trends come and go at an alarming…


This is the appendix to my other piece:

Note: some charting functions in the support file may require using an optional free Excel add-in for best performance.

Detailed step-by-step workings of the examples provided below and also available in the illustrative support file you can freely download.

Let us assume a Normal probability distribution, with mean = 50 and standard deviation = 10. We will have 100 units of product in our warehouse, so to limit the size of the simulation.

Normal probability density function for mean = 50 and stdev = 10. Image source: author from Evo Pricing (CC with attribution)

In a normal distribution, the mean is the same as the mode and the average, and the curve is…


The 5 secret ingredients to solve for profit, rather than accuracy

Data has a better idea by Franki Chamaki on Unsplash

Customers can be unpredictable. Hardly anything about them is forecastable correctly. Change approach maybe?

Prediction errors are everywhere. Over the past 15 years, I met and worked with over 1,000 managers to help them make better decisions every day. First, while at McKinsey as a management consultant; then, at Evo Pricing developing B2B technology products, as a researcher.

Throughout this experience, I felt the fundamental need to predict the future that we humans systematically share: to feel more in control.

Letting go of the urge to narrowly forecast can, however, yield extraordinary results!

Embracing the Rapid Response approach of Prescriptive…

Fabrizio Fantini

The learnings and benefits of the ‘science of billions’: what if you could access more data than Amazon? Free Evo University project: https://evo.ltd/join

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