You can find the up-to-date list of Andrea’s publications and citation count on Google Scholar and ResearchGate.
ORCID
0000-0001-9050-5018
SCOPUS 16244425400
2024
Andrea De Mauro; Michele Pacifico
The Financial Times Guide to Data-Driven Transformation: Maximise Business Value with Data Analytics Book
Pearson, 2024, ISBN: 9781292462141.
@book{DataTransformation,
title = {The Financial Times Guide to Data-Driven Transformation: Maximise Business Value with Data Analytics},
author = {Andrea De Mauro and Michele Pacifico},
url = {https://www.amazon.co.uk/Financial-Times-Guide-Data-Driven-Transformation/dp/1292462140/},
isbn = {9781292462141},
year = {2024},
date = {2024-08-28},
publisher = {Pearson},
abstract = {This book will help managers maximise the business value of data to their organisation. Despite data analytics and AI being recognised as critical value drivers for organisations, less than half of the companies report that they have created a data-driven organisation or established a data culture yet. Business leaders and analytics managers are currently struggling to move from theory to practice, and this book is all about solving this problem for them. This indispensable guide provides an actionable path filled with practical tools to navigate and accelerate the journey of data-driven transformation.
• Understand the underlying trends and models driving change and accelerate digital transformation in your company.
• Learn from real-world examples that show how companies of any size and industry have successfully navigated their data journeys.
• Utilise immediately applicable templates and frameworks that cover everything from data governance and technology enablers to organisational setup choices and AI deployment strategies.
• Benefit from straightforward advice and checklists, ensuring you can plan and lead data transformation effectively within your organisation.
Written by recognised experts in the field, this book simplifies complex topics such as artificial intelligence, machine learning, and data management into manageable segments. Whether you're a top manager needing to frame the right questions, a data leader aiming to link technical aspects to business value, or a practitioner looking to enhance your role, this book is designed to help you make a substantial impact on your organisation's data culture and capabilities.},
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This book will help managers maximise the business value of data to their organisation. Despite data analytics and AI being recognised as critical value drivers for organisations, less than half of the companies report that they have created a data-driven organisation or established a data culture yet. Business leaders and analytics managers are currently struggling to move from theory to practice, and this book is all about solving this problem for them. This indispensable guide provides an actionable path filled with practical tools to navigate and accelerate the journey of data-driven transformation.
• Understand the underlying trends and models driving change and accelerate digital transformation in your company.
• Learn from real-world examples that show how companies of any size and industry have successfully navigated their data journeys.
• Utilise immediately applicable templates and frameworks that cover everything from data governance and technology enablers to organisational setup choices and AI deployment strategies.
• Benefit from straightforward advice and checklists, ensuring you can plan and lead data transformation effectively within your organisation.
Written by recognised experts in the field, this book simplifies complex topics such as artificial intelligence, machine learning, and data management into manageable segments. Whether you’re a top manager needing to frame the right questions, a data leader aiming to link technical aspects to business value, or a practitioner looking to enhance your role, this book is designed to help you make a substantial impact on your organisation’s data culture and capabilities.
• Understand the underlying trends and models driving change and accelerate digital transformation in your company.
• Learn from real-world examples that show how companies of any size and industry have successfully navigated their data journeys.
• Utilise immediately applicable templates and frameworks that cover everything from data governance and technology enablers to organisational setup choices and AI deployment strategies.
• Benefit from straightforward advice and checklists, ensuring you can plan and lead data transformation effectively within your organisation.
Written by recognised experts in the field, this book simplifies complex topics such as artificial intelligence, machine learning, and data management into manageable segments. Whether you’re a top manager needing to frame the right questions, a data leader aiming to link technical aspects to business value, or a practitioner looking to enhance your role, this book is designed to help you make a substantial impact on your organisation’s data culture and capabilities.
Mohamad Almgerbi; Andrea De Mauro; Adham Kahlawi; Valentina Poggioni
Data analytics job skills dynamics: a preliminary longitudinal analysis Proceedings Article
In: Fabrizi, Elena; Giambona, Francesca Adele; Marini, Caterina; Marletta, Andrea; Rocca, Antonella (Ed.): Proceedings of ICES 2024 – 2nd Italian Conference on Economic Statistics, pp. 9-13, 2024, ISBN: 9788847629509.
@inproceedings{DataJobs_2024,
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editor = {Elena Fabrizi and Francesca Adele Giambona and Caterina Marini and Andrea Marletta and Antonella Rocca},
url = {https://www.ademauro.com/wp-content/uploads/2024/08/Data-analytics-job-skills-dynamics-a-preliminary-longitudinal-analysis-2024-ALMGERBI-DE-MAURO-ET-AL.pdf},
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abstract = {This study investigates the evolving role of Data Analytics between 2019 and 2023, analyzing the shifting job market demands for professionals in this field. Utilizing a corpus of job postings, we employed Latent Dirichlet Allocation (LDA) for topic modeling to discern patterns in required skills and expertise. Our findings indicate a marked increase in data analytics roles specialization and an increasing prominence of Machine Learning.},
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This study investigates the evolving role of Data Analytics between 2019 and 2023, analyzing the shifting job market demands for professionals in this field. Utilizing a corpus of job postings, we employed Latent Dirichlet Allocation (LDA) for topic modeling to discern patterns in required skills and expertise. Our findings indicate a marked increase in data analytics roles specialization and an increasing prominence of Machine Learning.
2023
Simone Malacaria; Michele Grimaldi; Marco Greco; Andrea De Mauro
Business Talk: Harnessing Generative AI with Data Analytics Maturity Journal Article
In: International Journal on Cybernetics & Informatics, vol. 12, no. 7, pp. 1-10, 2023, ISSN: 2277-548X.
@article{Malacaria_2023,
title = {Business Talk: Harnessing Generative AI with Data Analytics Maturity},
author = {Simone Malacaria and Michele Grimaldi and Marco Greco and Andrea De Mauro},
url = {https://ijcionline.com/paper/12/12723ijci01.pdf},
doi = {10.5121/ijci.2023.120701},
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year = {2023},
date = {2023-12-30},
urldate = {2023-12-30},
journal = {International Journal on Cybernetics & Informatics},
volume = {12},
number = {7},
pages = {1-10},
abstract = {Generative AI applications offer transformative potential for business operations, yet their adoption introduces substantial challenges. This paper utilizes the CBDAS data maturity model to pinpoint pivotal success factors for seamless generative AI integration in businesses. Through a comprehensive analysis of these factors, we underscore the essentials of generative AI deployment: cohesive architecture, robust data governance, and a data-centric corporate ethos. The study also highlights the hurdles and facilitators influencing its implementation. Key findings suggest that fostering a data-friendly culture, combined with structured governance, optimizes generative AI adoption. The paper culminates in presenting the practical implications of these insights, urging further exploration into the real-world efficacy of the proposed recommendations.},
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Generative AI applications offer transformative potential for business operations, yet their adoption introduces substantial challenges. This paper utilizes the CBDAS data maturity model to pinpoint pivotal success factors for seamless generative AI integration in businesses. Through a comprehensive analysis of these factors, we underscore the essentials of generative AI deployment: cohesive architecture, robust data governance, and a data-centric corporate ethos. The study also highlights the hurdles and facilitators influencing its implementation. Key findings suggest that fostering a data-friendly culture, combined with structured governance, optimizes generative AI adoption. The paper culminates in presenting the practical implications of these insights, urging further exploration into the real-world efficacy of the proposed recommendations.
Andrea Sestino; Adham Kahlawi; Andrea De Mauro
Decoding the data economy: A literature review of its impact on business, society and digital transformation Journal Article
In: European Journal of Innovation Management, 2023, ISSN: 1460-1060.
@article{sestino2023decoding,
title = {Decoding the data economy: A literature review of its impact on business, society and digital transformation},
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year = {2023},
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abstract = {Purpose
The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.
Design/methodology/approach
The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.
Findings
The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.
Practical implications
Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.
Social implications
Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.
Originality/value
This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.},
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Purpose
The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.
Design/methodology/approach
The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.
Findings
The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.
Practical implications
Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.
Social implications
Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.
Originality/value
This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.
The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.
Design/methodology/approach
The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.
Findings
The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.
Practical implications
Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.
Social implications
Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.
Originality/value
This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.
Simone Malacaria; Andrea De Mauro; Marco Greco; Michele Grimaldi
An Application of the Analytic Hierarchy Process to the Evaluation of Companies’ Data Maturity Journal Article
In: SN Computer Science, vol. 4, no. 5, pp. 696, 2023.
@article{malacaria2023application,
title = {An Application of the Analytic Hierarchy Process to the Evaluation of Companies’ Data Maturity},
author = {Simone Malacaria and Andrea De Mauro and Marco Greco and Michele Grimaldi},
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abstract = {The aim of this study is to evaluate data maturity of a sample of Italian firms of different sectors and sizes, obtained through an online assessment submitted to 261 professionals and entrepreneurs operating in the data/IT domain. The paper's objective is to assess the relative importance of the factors that determine the success of big data initiatives, according to the company structure and managerial perspective. The questionnaire was digitally submitted to IT professionals and decision-makers in Italy through the LinkedIn platform. The assessment was divided into two sections: the first focused on the assessment of 8 critical success factors for big data, whereas the second assigned weights based on an application of the analytic hierarchy process. The result of this process is a weighted-scores system that reflects the relative importance that managers and employees give to different domains. Respondents agreed to the importance of integrated architecture, data-friendly corporate culture, and integrated organization domains. Once the results consider the weights from the AHP, data friendliness becomes the most sought-after characteristic. The findings provide direction for further development of this assessment system.},
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The aim of this study is to evaluate data maturity of a sample of Italian firms of different sectors and sizes, obtained through an online assessment submitted to 261 professionals and entrepreneurs operating in the data/IT domain. The paper’s objective is to assess the relative importance of the factors that determine the success of big data initiatives, according to the company structure and managerial perspective. The questionnaire was digitally submitted to IT professionals and decision-makers in Italy through the LinkedIn platform. The assessment was divided into two sections: the first focused on the assessment of 8 critical success factors for big data, whereas the second assigned weights based on an application of the analytic hierarchy process. The result of this process is a weighted-scores system that reflects the relative importance that managers and employees give to different domains. Respondents agreed to the importance of integrated architecture, data-friendly corporate culture, and integrated organization domains. Once the results consider the weights from the AHP, data friendliness becomes the most sought-after characteristic. The findings provide direction for further development of this assessment system.
2022
Andrea De Mauro; Andrea Sestino; Andrea Bacconi
Machine learning and artificial intelligence use in marketing: a general taxonomy Journal Article
In: Italian Journal of Marketing, vol. 2022, no. 4, pp. 439–457, 2022.
@article{de2022machine,
title = {Machine learning and artificial intelligence use in marketing: a general taxonomy},
author = {Andrea De Mauro and Andrea Sestino and Andrea Bacconi},
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abstract = {The emergence of consumer-generated data and the growing availability of Machine Learning (ML) techniques are revolutionizing marketing practices. Marketers and researchers are far from having a thorough understanding of the broad range of opportunities ML applications offer in creating and maintaining a competitive business advantage. In this paper, we propose a taxonomy of ML use cases in marketing based on a systematic review of academic and business literature. We have identified 11 recurring use cases, organized in 4 homogeneous families which correspond to the fundamentals leverage areas of ML in marketing, namely: shopper fundamentals, consumption experience, decision making, and financial impact. We discuss the recurring patterns identified in the taxonomy and provide a conceptual framework for its interpretation and extension, highlighting practical implications for marketers and researchers.},
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The emergence of consumer-generated data and the growing availability of Machine Learning (ML) techniques are revolutionizing marketing practices. Marketers and researchers are far from having a thorough understanding of the broad range of opportunities ML applications offer in creating and maintaining a competitive business advantage. In this paper, we propose a taxonomy of ML use cases in marketing based on a systematic review of academic and business literature. We have identified 11 recurring use cases, organized in 4 homogeneous families which correspond to the fundamentals leverage areas of ML in marketing, namely: shopper fundamentals, consumption experience, decision making, and financial impact. We discuss the recurring patterns identified in the taxonomy and provide a conceptual framework for its interpretation and extension, highlighting practical implications for marketers and researchers.
Simone Malacaria; Andrea De Mauro; Marco Greco; Michele Grimaldi; Benito Mignacca
Toward the implementation of a Consensual Maturity Model for Big Data in Consumer Goods companies Proceedings Article
In: Proceedings IFKAD 2022, Lugano, Switzerland 20-22 June 2022, Knowledge Drivers for Resilience and Transformation, pp. 2380-2402, 2022, ISBN: 9788896687154.
@inproceedings{Malacaria_2022_conf,
title = {Toward the implementation of a Consensual Maturity Model for Big Data in Consumer Goods companies},
author = {Simone Malacaria and Andrea De Mauro and Marco Greco and Michele Grimaldi and Benito Mignacca},
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abstract = {This paper presents the Consensual Big Data Maturity Assessment System (CBDAS) implementation in a multinational company leader in the Consumer Goods sector. The business case illustrates the objective and the approach which has been taken with the CBDAS initiative. The paper aims to justify the assessment system as a dynamic and flexible system for enterprises operating in the Consumer Good sector. It can be leveraged to understand the maturity stage in the big data domain and guide organizations about their status of advancement in proposing successful big data initiatives. Some results of the first cycle of evaluation by the Senior Managers and IT decision-makers of Procter & Gamble Company are pinpointed to illustrate the advantages and the exchange of good practices following the evaluation.
The paper introduces the CBDAS initiative, implemented on a web application, organized in eight business-relevant domains, comprehensively covering all aspects impacting big data initiatives' success. The assessment contains weights to evaluate the corresponding relevance of a certain domain within the organization's reality.
Company data activities generate value in synergy with other assets. Therefore, to estimate whether it is a priority to intervene, i.e., on the technologies, data strategies, or organizational culture, we isolate the processes and flows deriving from data initiatives in the company, mapping two exemplary processes to intercept priority actions of intervention. Therefore, by determining the type of interventions on processes and maturity levels in each data maturity domain, we derived concrete actions to bridge the existing maturity gap in higher priority areas.},
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This paper presents the Consensual Big Data Maturity Assessment System (CBDAS) implementation in a multinational company leader in the Consumer Goods sector. The business case illustrates the objective and the approach which has been taken with the CBDAS initiative. The paper aims to justify the assessment system as a dynamic and flexible system for enterprises operating in the Consumer Good sector. It can be leveraged to understand the maturity stage in the big data domain and guide organizations about their status of advancement in proposing successful big data initiatives. Some results of the first cycle of evaluation by the Senior Managers and IT decision-makers of Procter & Gamble Company are pinpointed to illustrate the advantages and the exchange of good practices following the evaluation.
The paper introduces the CBDAS initiative, implemented on a web application, organized in eight business-relevant domains, comprehensively covering all aspects impacting big data initiatives’ success. The assessment contains weights to evaluate the corresponding relevance of a certain domain within the organization’s reality.
Company data activities generate value in synergy with other assets. Therefore, to estimate whether it is a priority to intervene, i.e., on the technologies, data strategies, or organizational culture, we isolate the processes and flows deriving from data initiatives in the company, mapping two exemplary processes to intercept priority actions of intervention. Therefore, by determining the type of interventions on processes and maturity levels in each data maturity domain, we derived concrete actions to bridge the existing maturity gap in higher priority areas.
The paper introduces the CBDAS initiative, implemented on a web application, organized in eight business-relevant domains, comprehensively covering all aspects impacting big data initiatives’ success. The assessment contains weights to evaluate the corresponding relevance of a certain domain within the organization’s reality.
Company data activities generate value in synergy with other assets. Therefore, to estimate whether it is a priority to intervene, i.e., on the technologies, data strategies, or organizational culture, we isolate the processes and flows deriving from data initiatives in the company, mapping two exemplary processes to intercept priority actions of intervention. Therefore, by determining the type of interventions on processes and maturity levels in each data maturity domain, we derived concrete actions to bridge the existing maturity gap in higher priority areas.
Mohamad Almgerbi; Andrea De Mauro; Adham Kahlawi; Valentina Poggioni
A systematic review of data analytics job requirements and online-courses Journal Article
In: Journal of Computer Information Systems, vol. 62, no. 2, pp. 422–434, 2022.
@article{almgerbi2022systematic,
title = {A systematic review of data analytics job requirements and online-courses},
author = {Mohamad Almgerbi and Andrea De Mauro and Adham Kahlawi and Valentina Poggioni},
url = {https://www.ademauro.com/wp-content/uploads/2024/07/PREPRINT-A-systematic-review-of-Data-Analytics-Job-Requirements-and-Online-Courses.pdf},
doi = {10.1080/08874417.2021.1971579},
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abstract = {Data analytics’ growing importance in modern business has left many organizations unprepared in terms of human talent. This study sheds light on the intersection between the analytics job skills currently in demand and the offer of massive online open courses for developing them. We have scraped from the web the description of more than 14,000 job posts and 3,600 Data Analytics online courses to systematically capture the need for data skills and available learning opportunities. By using an original combination of topic modeling and text mining algorithms, we provide a systematic mapping of educational offers with business needs, quantifying their presence and identifying gaps. Our study enables both educational providers to improve their offering on Data Analytics and Human Resources professionals to identify skill development opportunities. Additionally, we introduce a general methodology able to produce systematic mappings of job skills and learning opportunities in any domain.},
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Data analytics’ growing importance in modern business has left many organizations unprepared in terms of human talent. This study sheds light on the intersection between the analytics job skills currently in demand and the offer of massive online open courses for developing them. We have scraped from the web the description of more than 14,000 job posts and 3,600 Data Analytics online courses to systematically capture the need for data skills and available learning opportunities. By using an original combination of topic modeling and text mining algorithms, we provide a systematic mapping of educational offers with business needs, quantifying their presence and identifying gaps. Our study enables both educational providers to improve their offering on Data Analytics and Human Resources professionals to identify skill development opportunities. Additionally, we introduce a general methodology able to produce systematic mappings of job skills and learning opportunities in any domain.
Andrea De Mauro
Data analytics per tutti. Imparare ad analizzare, visualizzare e raccontare i dati Book
Apogeo, 2022, ISBN: 9788850335947.
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Simone Malacaria.; Andrea De Mauro.; Marco Greco.; Michele Grimaldi.
An Application of the Analytic Hierarchy Process to the Evaluation of Companies’ Data Maturity Proceedings Article
In: Proceedings of the 24th International Conference on Enterprise Information Systems – Volume 1: ICEIS, pp. 50-61, INSTICC SciTePress, 2022, ISSN: 2184-4992.
@inproceedings{iceis22,
title = {An Application of the Analytic Hierarchy Process to the Evaluation of Companies’ Data Maturity},
author = {Simone Malacaria. and Andrea De Mauro. and Marco Greco. and Michele Grimaldi.},
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abstract = { The study reports the data maturity evaluation on a sample of Italian firms of different sectors and sizes, retrieved through an online assessment made by 261 professionals and entrepreneurs operating in the data domain. The paper's objective is to derive the relative importance of the critical factors to impact successful big data initiatives, according to organization reality and manager perspective. The questionnaire was distributed among IT professionals and decision-makers in Italy using the LinkedIn platform. The assessment was divided into two sections: the 1st one contained the assessment of 8 critical success factors for big data, whereas the 2nd section assessed weights based on an application of the analytic hierarchy process. The result of this process is a scoring system that includes the characteristics a company "must-have" to become data-oriented and make data-driven decisions. The application of the weights allows giving more importance to the domains that managers think are more important in a data-driven company. Respondents agreed to the importance of integrated architecture, data-friendly corporate culture, and integrated organization domains. Once the results consider the weights from the AHP, data friendliness becomes the most sought-after characteristic. The findings provide direction for further development of the assessment system.},
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The study reports the data maturity evaluation on a sample of Italian firms of different sectors and sizes, retrieved through an online assessment made by 261 professionals and entrepreneurs operating in the data domain. The paper’s objective is to derive the relative importance of the critical factors to impact successful big data initiatives, according to organization reality and manager perspective. The questionnaire was distributed among IT professionals and decision-makers in Italy using the LinkedIn platform. The assessment was divided into two sections: the 1st one contained the assessment of 8 critical success factors for big data, whereas the 2nd section assessed weights based on an application of the analytic hierarchy process. The result of this process is a scoring system that includes the characteristics a company "must-have" to become data-oriented and make data-driven decisions. The application of the weights allows giving more importance to the domains that managers think are more important in a data-driven company. Respondents agreed to the importance of integrated architecture, data-friendly corporate culture, and integrated organization domains. Once the results consider the weights from the AHP, data friendliness becomes the most sought-after characteristic. The findings provide direction for further development of the assessment system.
Andrea De Mauro
Defining Big Data Book Section
In: Reuter, Martin; Montag, Christian (Ed.): Digital Phenotyping and Mobile Sensing: New Developments in Psychoinformatics, pp. 443–446, Springer International Publishing Cham, 2022, ISSN: 2196-6613.
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title = {Defining Big Data},
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abstract = {‘Big Data’ has shown to be a trending catchphrase in modern Information Technology. However, it is hard to define a sharp cut between the classic notion of data and the novelties introduced by the arrival of Big Data. This short paper defines the seven structural elements underlying the concept of Big Data, highlighting the features that make it truly different from conventional data.},
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‘Big Data’ has shown to be a trending catchphrase in modern Information Technology. However, it is hard to define a sharp cut between the classic notion of data and the novelties introduced by the arrival of Big Data. This short paper defines the seven structural elements underlying the concept of Big Data, highlighting the features that make it truly different from conventional data.
Andrea Sestino; Andrea De Mauro
Leveraging artificial intelligence in business: Implications, applications and methods Journal Article
In: Technology Analysis & Strategic Management, vol. 34, no. 1, pp. 16–29, 2022.
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title = {Leveraging artificial intelligence in business: Implications, applications and methods},
author = {Andrea Sestino and Andrea De Mauro},
url = {https://www.researchgate.net/profile/Andrea-De-Mauro-2/publication/349110234_Leveraging_Artificial_Intelligence_in_Business_Implications_Applications_and_Methods/links/602275744585158939907f66/Leveraging-Artificial-Intelligence-in-Business-Implications-Applications-and-Methods.pdf},
doi = {10.1080/09537325.2021.1883583},
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publisher = {Routledge},
abstract = {The concept of Artificial Intelligence (AI) as a business-disruptive technology has developed in academic and professional literature in a chaotic and unstructured manner. This study aims to provide clarity over the phenomenon of business activation of AI by means of a comprehensive and systematic literature review, aimed at suggesting a clear description of what Artificial Intelligence is today. The study analyses a corpus of 3780 contributions through an original combination of two established machine learning algorithms (LDA and hierarchical clustering). The review produced a structured classification of the various streams of current research and a list of promising emerging trends. Results have shed light on six topics attributable to three different themes, namely Implications, Applications and Methods (IAM model). Our analysis could provide researchers and practitioners with a meaningful overview of the body of knowledge and research agenda, to exploit AI as an effective enabler to drive business value.},
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The concept of Artificial Intelligence (AI) as a business-disruptive technology has developed in academic and professional literature in a chaotic and unstructured manner. This study aims to provide clarity over the phenomenon of business activation of AI by means of a comprehensive and systematic literature review, aimed at suggesting a clear description of what Artificial Intelligence is today. The study analyses a corpus of 3780 contributions through an original combination of two established machine learning algorithms (LDA and hierarchical clustering). The review produced a structured classification of the various streams of current research and a list of promising emerging trends. Results have shed light on six topics attributable to three different themes, namely Implications, Applications and Methods (IAM model). Our analysis could provide researchers and practitioners with a meaningful overview of the body of knowledge and research agenda, to exploit AI as an effective enabler to drive business value.
2021
Andrea De Mauro
Packt Publishing, 2021, ISBN: 9781801074155.
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abstract = {Data Analytics Made Easy is an accessible beginner’s guide for anyone working with data. The book interweaves four key elements:
Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don’t worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience.
Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You’ll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components.
Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You’ll not only be able to understand data scientists’ machine learning models; you’ll be able to challenge them and build your own.
Creating interactive dashboards – Follow the book’s simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results.},
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Data Analytics Made Easy is an accessible beginner’s guide for anyone working with data. The book interweaves four key elements:
Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don’t worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience.
Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You’ll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components.
Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You’ll not only be able to understand data scientists’ machine learning models; you’ll be able to challenge them and build your own.
Creating interactive dashboards – Follow the book’s simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results.
Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don’t worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience.
Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You’ll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components.
Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You’ll not only be able to understand data scientists’ machine learning models; you’ll be able to challenge them and build your own.
Creating interactive dashboards – Follow the book’s simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results.
Mohamad Almgerbi; Andrea De Mauro; Adham Kahlawi; Valentina Poggioni
Improving Topic Modeling Performance through N-gram Removal Proceedings Article
In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 162–169, 2021.
@inproceedings{almgerbi2021improving,
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abstract = {In recent years, topic modeling has been increasingly adopted for finding conceptual patterns in large corpora of digital documents to organize them accordingly. In order to enhance the performance of topic modeling algorithms, such as Latent Dirichlet Allocation (LDA), multiple preprocessing steps have been proposed. In this paper, we introduce N-gram Removal, a novel preprocessing procedure based on the systematic elimination of a dynamic number of repeated words in text documents. We have evaluated the effects of the utilization of N-gram Removal through four different performance metrics: we concluded that its application is effective at improving the performance of LDA and enhances the human interpretation of topics models.},
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In recent years, topic modeling has been increasingly adopted for finding conceptual patterns in large corpora of digital documents to organize them accordingly. In order to enhance the performance of topic modeling algorithms, such as Latent Dirichlet Allocation (LDA), multiple preprocessing steps have been proposed. In this paper, we introduce N-gram Removal, a novel preprocessing procedure based on the systematic elimination of a dynamic number of repeated words in text documents. We have evaluated the effects of the utilization of N-gram Removal through four different performance metrics: we concluded that its application is effective at improving the performance of LDA and enhances the human interpretation of topics models.
2020
Andrea De Mauro
Big Data per il Business. Guida strategica per manager alle prese con la trasformazione digitale Book
Apogeo, 2020, ISBN: 9788850335367.
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Paola Demartini; Andrea De Mauro
The Impact of Big Data on Board Level Decision Making Proceedings Article
In: International Forum on Knowledge Asset Dynamics (IFKAD) 2020, pp. 1712–1722, 2020, ISSN: 2280-787X.
@inproceedings{demartini2020impact,
title = {The Impact of Big Data on Board Level Decision Making},
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2019
Andrea De Mauro
Big Data Analytics. Analizzare e interpretare dati con il machine learning Book
Apogeo, 2019, ISBN: 9788850334780.
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title = {Big Data Analytics. Analizzare e interpretare dati con il machine learning},
author = {Andrea De Mauro},
url = {https://www.google.it/books/edition/Big_Data_Analytics/FiCWDwAAQBAJ?hl=en&gbpv=1&dq=big+data+analytics&printsec=frontcover},
isbn = {9788850334780},
year = {2019},
date = {2019-03-21},
urldate = {2019-01-01},
journal = {Milano: Apogeo},
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Andrea De Mauro; Marco Greco; Michele Grimaldi
Understanding Big Data through a Systematic Literature Review: the ITMI model Journal Article
In: International Journal of Information Technology & Decision Making, 2019.
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title = {Understanding Big Data through a Systematic Literature Review: the ITMI model},
author = {Andrea De Mauro and Marco Greco and Michele Grimaldi},
year = {2019},
date = {2019-01-01},
journal = {International Journal of Information Technology & Decision Making},
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2018
Andrea De Mauro; Marco Greco; Michele Grimaldi; Paavo Ritala
In (Big) Data we trust: Value creation in knowledge organizations-Introduction to the special issue Miscellaneous
2018.
@misc{de2018big,
title = {In (Big) Data we trust: Value creation in knowledge organizations-Introduction to the special issue},
author = {Andrea De Mauro and Marco Greco and Michele Grimaldi and Paavo Ritala},
year = {2018},
date = {2018-01-01},
journal = {Information Processing & Management},
volume = {54},
number = {5},
pages = {755–757},
publisher = {Pergamon},
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Andrea De Mauro; Marco Greco; Michele Grimaldi; Paavo Ritala
Human resources for Big Data professions: A systematic classification of job roles and required skill sets Journal Article
In: Information Processing & Management, vol. 54, no. 5, pp. 807–817, 2018.
@article{de2018human,
title = {Human resources for Big Data professions: A systematic classification of job roles and required skill sets},
author = {Andrea De Mauro and Marco Greco and Michele Grimaldi and Paavo Ritala},
year = {2018},
date = {2018-01-01},
journal = {Information Processing & Management},
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2017
Michele Grimaldi; Vincenzo Corvello; Andrea De Mauro; Emanuela Scarmozzino
A systematic literature review on intangible assets and open innovation Journal Article
In: Knowledge Management Research & Practice, vol. 15, no. 1, pp. 90–100, 2017.
@article{grimaldi2017systematic,
title = {A systematic literature review on intangible assets and open innovation},
author = {Michele Grimaldi and Vincenzo Corvello and Andrea De Mauro and Emanuela Scarmozzino},
year = {2017},
date = {2017-01-01},
journal = {Knowledge Management Research & Practice},
volume = {15},
number = {1},
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publisher = {Palgrave Macmillan UK},
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2016
Andrea De Mauro; Marco Greco; Michele Grimaldi; Giacomo Nobili
Beyond Data Scientists: a Review of Big Data Skills and Job Families Proceedings Article
In: 11th International Forum on Knowledge Assets Dynamics – IFKAD 2016, 2016.
@inproceedings{de2016beyond,
title = {Beyond Data Scientists: a Review of Big Data Skills and Job Families},
author = {Andrea De Mauro and Marco Greco and Michele Grimaldi and Giacomo Nobili},
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Andrea De Mauro; Marco Greco; Michele Grimaldi
A formal definition of Big Data based on its essential features Journal Article
In: Library review, vol. 65, no. 3, pp. 122–135, 2016.
@article{de2016formal,
title = {A formal definition of Big Data based on its essential features},
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year = {2016},
date = {2016-01-01},
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2015
Vincenzo Corvello; Andrea De Mauro; Michele Grimaldi; Emanuela Scarmozzino
The role of intangible assets in open innovation processes: a literature review Proceedings Article
In: 10th International Forum on Knowledge Assets Dynamics – IFKAD 2015, 2015.
@inproceedings{corvello2015role,
title = {The role of intangible assets in open innovation processes: a literature review},
author = {Vincenzo Corvello and Andrea De Mauro and Michele Grimaldi and Emanuela Scarmozzino},
year = {2015},
date = {2015-01-01},
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}
Andrea De Mauro; Marco Greco; Michele Grimaldi
What is big data? A consensual definition and a review of key research topics Proceedings Article
In: AIP conference proceedings, pp. 97–104, American Institute of Physics 2015.
@inproceedings{de2015big,
title = {What is big data? A consensual definition and a review of key research topics},
author = {Andrea De Mauro and Marco Greco and Michele Grimaldi},
year = {2015},
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booktitle = {AIP conference proceedings},
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2006
Andrea De Mauro
A Peer-to-peer Network for Real-time Multiple-description Video Communications Using Multiple Paths PhD Thesis
University of Illinois at Chicago, 2006.
@phdthesis{de2006peer,
title = {A Peer-to-peer Network for Real-time Multiple-description Video Communications Using Multiple Paths},
author = {Andrea De Mauro},
year = {2006},
date = {2006-01-01},
booktitle = {2006 IEEE International Conference on Multimedia and Expo},
pages = {921–924},
organization = {IEEE},
school = {University of Illinois at Chicago},
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Andrea De Mauro; Claudio Rossi
A Secure Multiple Path Real-Time Framework for Video Communication over the Internet Proceedings Article
In: SCRA 2006-FIM XIII-Thirteenth International Conference of the Forum for Interdisciplinary Mathematics on Interdisciplinary Mathematical and Statistical Techniques, At Lisbon-Tomar, Portugal, 2006.
@inproceedings{de2006secure,
title = {A Secure Multiple Path Real-Time Framework for Video Communication over the Internet},
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date = {2006-01-01},
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