Towards a More Comprehensive Public Innovation With Data Science—Part I
The views expressed are those of the author and do not necessarily reflect the views of ASPA as an organization.
By Mauricio Covarrubias
August 12, 2022
During the 21st century, society has entered a period of profound and unprecedented change. During this time, we have been forced, individually and collectively, to adopt new ways of thinking, perceiving and behaving not only to be able to endure, but more importantly to carry out the transformations that are required to face the problems that affect our welfare and threaten the future of society. These are systemic problems, which means that they are interconnected and interdependent, therefore they cannot be addressed in isolation.
This places us in front of the urgent need for a fundamental change of vision in science, education, institutions and government. The magnitude of current social problems has forced us to scale the perspectives of analysis and respond in a conceptual, spatial and temporal manner in order to effectively understand and face such disturbing, persistent and elusive problems.
These are issues that have revealed the limits of many of the concepts, action plans, recipes and beliefs used in previous decades. Substantive changes need to be made to the references that have guided the government’s actions and innovation. The nature of social problems demands the mobilization of social energies from a broader perspective. This implies, among other things, leaving behind the incremental route to proceed in a more comprehensive manner.
Rittel and Webber point out in Dilemmas in a general theory of planning, that complex problems involve the interaction of numerous social, economic, political and technical factors. The general behavior of this type of problem cannot be explained considering each one of its parts separately, which makes it difficult to design specific interventions that correct the undesirable behavior of the system (see Kling and Ketter et al.). Furthermore, any intervention in complex social systems requires careful consideration of system-level consequences, including potential negative social effects.
In many ways, the pressure to cushion the effects of the problems has justified incremental action. From democratic experimentalism gradual approaches have been supported under the argument that they give rise to more legitimate, innovative and effective solutions. However, although not all problems require a broad or comprehensive solution, there are increasing issues where gradual or incremental responses are not only ineffective, but counterproductive. Today more than ever, it is essential to recover the macro level to innovate and design structural and integrative solutions based on the general interest and collective objectives. Structural innovation refers to system change rather than change within the system.
In terms of strategic thinking—for the European Commission—behind research and innovation strategies, although the academic world is increasingly turning away a linear model of innovation and despite the increasingly complexities R&I policies are expected to perform, the linear model of innovation still tend to dominate policymaking. This may be starting to change as in recent years there has been an increasing interest in ‘broad-based innovation policies’ and ‘systemic innovation policies.
Increasingly, public authorities need comprehensive logics that give sense and meaning to the data, as opposed to the fragmented and micro-contextualized analyzes of traditional management techniques. Many important challenges of our time can only be fully understood through the discovery and analysis of a wide variety of data at many levels of detail. (Ketter et al. 2016).
According to the European Commission’s “Horizon 2020. EU framework programme for research and innovation” report, such problems require interrelated advances in data discovery and analysis, theory development and design, best delivered by interdisciplinary research communities. Therefore, there is a need for a continuing focus on ICT as the critical infrastructure, especially to handle the increasingly large data sets in research and innovation.
Thus, with advances in high-performance computing infrastructures that make it possible to process large volumes of data and perform complex analyses, data science has become an essential innovation tool for the public sector, understood in line with the OECD’s Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, as novel ideas that are implemented and produce value for society.
In The Challenge of Big Data and Data Science, Henry E. Brady mentions “The volume, velocity, variety and veracity of data being generated by and available to governments, armies, businesses, nonprofits and people have combined with the enormous increases in computing power and improvements in data science methods to change society in fundamental ways”. He also remarks that burgeoning data and innovative methods facilitate answering previously hard-to-tackle questions about social issues by offering new ways to form concepts from data, to do descriptive inference, to make causal inferences and to generate predictions.
In this order of ideas, the question is: how can data science improve the government’s capacity to move towards a more comprehensive understanding of complex social problems and, consequently, act and innovate in a more systemic way?
To be continue.
Author: Mauricio Covarrubias is Professor at the National Institute of Public Administration in Mexico. He is co-founder of the International Academy of Political-Administrative Sciences (IAPAS). He is the founder and Editor of the International Journal of Studies on Educational Systems (RIESED). Coordinator in Mexico of the TOGIVE Project: Transatlantic Open Government Virtual Education, of the ERASMUS + Program of the European Union. Member of the National System of Researchers of CONACYT. He received his Ph.D. from the National Autonomous University of Mexico. He can be reached at [email protected] and followed on Twitter @OMCovarrubias
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Towards a More Comprehensive Public Innovation With Data Science—Part I
The views expressed are those of the author and do not necessarily reflect the views of ASPA as an organization.
By Mauricio Covarrubias
August 12, 2022
During the 21st century, society has entered a period of profound and unprecedented change. During this time, we have been forced, individually and collectively, to adopt new ways of thinking, perceiving and behaving not only to be able to endure, but more importantly to carry out the transformations that are required to face the problems that affect our welfare and threaten the future of society. These are systemic problems, which means that they are interconnected and interdependent, therefore they cannot be addressed in isolation.
This places us in front of the urgent need for a fundamental change of vision in science, education, institutions and government. The magnitude of current social problems has forced us to scale the perspectives of analysis and respond in a conceptual, spatial and temporal manner in order to effectively understand and face such disturbing, persistent and elusive problems.
These are issues that have revealed the limits of many of the concepts, action plans, recipes and beliefs used in previous decades. Substantive changes need to be made to the references that have guided the government’s actions and innovation. The nature of social problems demands the mobilization of social energies from a broader perspective. This implies, among other things, leaving behind the incremental route to proceed in a more comprehensive manner.
Rittel and Webber point out in Dilemmas in a general theory of planning, that complex problems involve the interaction of numerous social, economic, political and technical factors. The general behavior of this type of problem cannot be explained considering each one of its parts separately, which makes it difficult to design specific interventions that correct the undesirable behavior of the system (see Kling and Ketter et al.). Furthermore, any intervention in complex social systems requires careful consideration of system-level consequences, including potential negative social effects.
In many ways, the pressure to cushion the effects of the problems has justified incremental action. From democratic experimentalism gradual approaches have been supported under the argument that they give rise to more legitimate, innovative and effective solutions. However, although not all problems require a broad or comprehensive solution, there are increasing issues where gradual or incremental responses are not only ineffective, but counterproductive. Today more than ever, it is essential to recover the macro level to innovate and design structural and integrative solutions based on the general interest and collective objectives. Structural innovation refers to system change rather than change within the system.
In terms of strategic thinking—for the European Commission—behind research and innovation strategies, although the academic world is increasingly turning away a linear model of innovation and despite the increasingly complexities R&I policies are expected to perform, the linear model of innovation still tend to dominate policymaking. This may be starting to change as in recent years there has been an increasing interest in ‘broad-based innovation policies’ and ‘systemic innovation policies.
Increasingly, public authorities need comprehensive logics that give sense and meaning to the data, as opposed to the fragmented and micro-contextualized analyzes of traditional management techniques. Many important challenges of our time can only be fully understood through the discovery and analysis of a wide variety of data at many levels of detail. (Ketter et al. 2016).
According to the European Commission’s “Horizon 2020. EU framework programme for research and innovation” report, such problems require interrelated advances in data discovery and analysis, theory development and design, best delivered by interdisciplinary research communities. Therefore, there is a need for a continuing focus on ICT as the critical infrastructure, especially to handle the increasingly large data sets in research and innovation.
Thus, with advances in high-performance computing infrastructures that make it possible to process large volumes of data and perform complex analyses, data science has become an essential innovation tool for the public sector, understood in line with the OECD’s Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, as novel ideas that are implemented and produce value for society.
In The Challenge of Big Data and Data Science, Henry E. Brady mentions “The volume, velocity, variety and veracity of data being generated by and available to governments, armies, businesses, nonprofits and people have combined with the enormous increases in computing power and improvements in data science methods to change society in fundamental ways”. He also remarks that burgeoning data and innovative methods facilitate answering previously hard-to-tackle questions about social issues by offering new ways to form concepts from data, to do descriptive inference, to make causal inferences and to generate predictions.
In this order of ideas, the question is: how can data science improve the government’s capacity to move towards a more comprehensive understanding of complex social problems and, consequently, act and innovate in a more systemic way?
To be continue.
Author: Mauricio Covarrubias is Professor at the National Institute of Public Administration in Mexico. He is co-founder of the International Academy of Political-Administrative Sciences (IAPAS). He is the founder and Editor of the International Journal of Studies on Educational Systems (RIESED). Coordinator in Mexico of the TOGIVE Project: Transatlantic Open Government Virtual Education, of the ERASMUS + Program of the European Union. Member of the National System of Researchers of CONACYT. He received his Ph.D. from the National Autonomous University of Mexico. He can be reached at [email protected] and followed on Twitter @OMCovarrubias
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