About

Paul Rougieux

Paul Rougieux is a forest economist and wood products engineer. He worked at the Joint Research Centre of the European Commission (JRC), at the French National Institute of Agricultural and Environmental Research (INRAE) and at the European Forest Institute (EFI). In these positions, he was analysing international trade patterns and preparing macroeconomic scenario predictions of future wood demand. He also analysed the implications of harvest scenarios on the forest carbon sink.

Paul used python and R throughout these jobs. Both programming languages have an extensive community that release the implementation of statistical method, algorithms and data preparation tools as open source packages. Paul is the creator of the python package biotrade, a tool to harvest FAOSTAT and UN Comtrade data into a local database, with harmonized variable names. He is the co-creator of EU-CBM-HAT with Lucas Sinclair, Viorel Blujdea and ROberto PIlli. A tool to run the Carbon Budget Model of the Canadian Forest Service for European countries. He is the maintainer of the R package FAOSTAT.

Inspirational quotes

We are standing on the shoulders of giants. The following quotes will give you a flavour of the philosophy underpinning our training, programming and consulting services.

John K. Thompson in his 2020 book “Building Analytics Teams”:

“I traveled to every continent and spent much of my times on the road and in discussions with executives, managers and people who should be involved. The primary objectives of those meetings were to: […] - Let them know that we were not there to judge their ideas and current state of operating, but to help them see how data and analytics will help them reach and exceed their operating goals. - Improve employee engagement and remove the tedious parts of staff members’ duties to enable those staff members to focus on the more creative aspects of their work that leverages their experience and expertise.

On the critique of no-code approches to creating data science software. Think about the analogy with a word processor. You don’t write an article or a report by choosing every word in a drop down menu.

Matt Turck in Conversation with Florian Douetteau, CEO, Dataiku:

“Data and AI in the enterprise is mostly not about a magical product, or a flying machine driven by AI. It’s mostly about the business processes, probably hundreds of them that you have inside the company. Most companies operate like a clockwork, meaning you’ve got many, many business processes that work together in order to create value. Possibly for any decent-sized company, 500-1000 of them. And data and AI is mostly about optimizing each of them step-by-step to make them more efficient, and more automated. And that’s why it’s so hard, it’s because data and AI in the enterprise is mostly about this very long transformation that most enterprises will have to go through. It’s probably a 20-25 year journey, and we are one third into it. And at the end of the journey, you have completely new way to work, with data and AI being very pervasive.”

• https://mattturck.com/saas-dead/

“for the more specialized enterprise apps, customers feel like they can/should “build” internally rather than “buy” ”