Andrea De Mauro - Data Analytics Made Easy

Data Analytics Made Easy

Use machine learning and data storytelling in your work without writing any code

Andrea De Mauro

BIRMINGHAM - MUMBAI

Data Analytics Made Easy

Copyright 2021 Packt Publishing

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Producer: Tushar Gupta

Acquisition Editor Peer Reviews: Saby Dsilva

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Technical Editor: Gaurav Gavas

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First published: August 2021

Production reference: 1250821

Published by Packt Publishing Ltd.

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ISBN 978-1-80107-415-5

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Writing a book that tries to combine theory and practice of such a vast field has been possible only thanks to the eye-opening inputs, the rigorous feedback, and the heartfelt encouragement of so many wonderful people. Towards all those colleagues and friends, I am now left with a sense of profound gratitude. Although writing the names of some of them does not do them full justice, let me thank: Dimitrios Skoufakis, Maria Navrotskaya, Francesco Pisan, Salvatore Gatto, Leonardo and Alessandro De Mauro, Lenka Dzurendova, Michele Pacifico (how could I do without his brilliant advice?), Adam Graham, Kate Daley, Angelo Spedicati, Francesco Lefons, Marcin Czajkowski, Alessio Villardita, Antonio Faraldi, Giorgio Binenti, Giuseppe Papaianni, Jacek Ludwig Scarso, Jon Thomson, Piril Paker Yagli, Paolo Palazzo, Gilda Notaro (whom I dearly miss), Felice Di Tanno, Giorgio Demetrio, Roberto Bellotti, Marcello Lando, my dear parents Gianfranco and Maria Teresa, Dyi Huijg, Cristina Trapani-Scott and all the other hosts at the Shut Up & Write! events that accompanied so much of my evenings and weekend writing, Katia Cocca, Davide D'Emiliano, Simona Palomba, Luisa Fabro, Antonio Gatto, Daniela Meo, Rachel Breslin, Laurent Eyers, Antonella Rossi, Kasia Bojanowska, Fabio Pistilli, Jerryn Cherian, Saurabh Dichwalkar, Michael Leonhardt, Kacper Hankiewicz, Nori Reis, Tutku Oztekin, Carolina Martinez, Miguel Estrella (the picture of me on the front cover is his, although I recognize thatgiven the poor subjectit doesn't give full justice to his outstanding skills as a photographer), Mario Galietti, Nicola Lopez, Antonio Fazzari, Paola Lucetti, Vinay Ahuja, Giuliana Farbo, Taide Guajardo, Luca Merlo, Paolo Grue, Francesca Sagramora, Nicolas Kerling, Guy Peri, and all the other amazing friends, colleagues, and leaders at P&G who have passionately worked alongside me to elevate the role of data analytics in the way business is done. It would have been impossible for me to address this subject without having the opportunity to investigate it systematically alongside my many precious partners in academic research and university teaching: Valentina Poggioni, Mohamed Almgerbi, Adham Kahlawi, Andrea Sestino, Paola Demartini, Cristiano Ciappei, Gianluca Cubadda, Luca Petruzzellis, Pasquale Del Vecchio, Giusy Secundo, Andrea Bacconi, Gaetano Cascini, Francesca Montagna, Nikos Tsourakis, Dogan Duven and the amazing staff at the IUG, Alberto Pezzi, Simone Malacaria, Marco Greco, and Michele Grimaldi. I am in debt to the authors of the two thought-provoking forewords, Andy Walter and Francesco Marzoni: the best pages of the entire book are certainly theirs.

I am also very grateful to Tushar Gupta, Ravit Jain, Namrata Katare, Bhavesh Amin, Gaurav Gavas, and the rest of the amazing team at Packt for their high-quality professional support (they made a real book out of a shaky manuscript) and the vast patience they had with me throughout the last few months, and with Scott Fincher for making the book way better thanks to his careful content review and precious feedback. A special thank you goes to my dear Sawka G. Scarso: her true writing talent has always been my primary source of inspiration. Without her patience, support, and writing coaching, I wouldn't have gone far.

If it weren't for all these people, this book wouldn't be in your hands. So, if you find it helpful, the full appreciation should solely go to them.

Andrea De Mauro

A common misconception undermines the effectiveness of most digital transformations across industries. It's about the belief that hiring a pool of data and digital experts is enough to become a data-enabled, cognitive organization.

The continuum of data, analytics, and artificial intelligence is pervasive in nature. Creating sustainable value with data requires multifunctional teams of data experts, digital experience designers, business strategists, business process experts, and many more roles to partner up and work together with a common denominator of knowledge about both data and business. It's a team sport, as Andrea will say.

Hence, at least two types of capability development efforts are needed to shape a future-ready organization. They are the ones that develop:

• business literacy of the few digital experts, so that they can proactively identify data-driven business opportunities and influence, with inclusive collaboration and credibility, different functions and processes;
• data literacy of the many professionals in the organization and beyond, so that they can be key actors in co-developing digital and cognitive capabilities as well as leaders of process transformation and systemic adoption of those capabilities.

Several efforts of Andrea over the past years and the pages in this book are an important contribution to the industry and an essential tool for anybody who has stakes in the development of Data Literacy for The Many in their professional (or personal!) ecosystem. In fact, it's not just about the professional arena. It's about enabling every individual to play an active role in the quest to improve the state of the world. It's about making better collective decisions for our society. All with the power of data.

By democratizing data literacy and understanding analytics, we drive positive progress on at least three levels.

1. The single organization. Analytics is ultimately about creating actionable knowledge from data and then combining that knowledge with newly available data to create further actionable knowledge. And so on so forth in a systemic series of iterations. Attempting to drive analytics with the sole effort of analytics experts is equivalent to starting that loop of knowledge and data from scratch, as if an organization had no history and as if its people had no domain expertise. With data literacy democratization, multifunctional teams can build value with a common dictionary together.