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.
At its heart, data science is a noble name for a broad set of number crunching activities that were mostly…
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.
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…
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…
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…
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.
In a normal distribution, the mean is the same as the mode and the average, and the curve is…
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…
I’m proud to officially share with you today that Evo is an Open Loop participating company.
What does that mean?
Over the final quarter of 2020, Evo was a part of a team of leading AI companies in Europe brought together by Facebook that dedicated itself to developing a data-driven framework to manage AI risk while encouraging innovation and growth. This project allowed us to assess the risks in our own AI applications more deliberately and then work together to create and test an AI risk assessment framework.
My company Evo is expanding, which means we’ve been through a few hiring cycles in recent months, something that will continue throughout 2021. We are looking for data scientists that can hit the ground running and quickly integrate into the team.
And yet, we continuously struggle to find the right people. It’s not that the technical skills aren’t out there. We see hundreds of applications with years of advanced math, statistical modelling experience, coding skills, and comfort with Python libraries like Pandas and Numpy. Unfortunately, many applicants fail to highlight the non-technical skills critical to success as a data scientist.