Government Leaders: The Key Role in Data-Driven Transformation
The views expressed are those of the author and do not necessarily reflect the views of ASPA as an organization.
By Mauricio Covarrubias
September 8, 2023
According to Thomas M. Siebel, one of the primary responsibilities of leaders, whether in the corporate or government sectors, is to adeptly identify risks and seize opportunities. This involves recognizing the origins, assessing the magnitude and gauging the potential repercussions on vital goals. In today’s landscape, where digital transformation plays a central role, the stakes are notably high.
Empowering Transformation: A Top-Down Approach
Digital transformation is pervasive as it touches every part of the organization. Therefore, for digital transformation to be successful, writes Siebel, it must be driven from the top. In fact, digital transformation has completely reversed the technology adoption cycle that was prevalent in previous decades. Previously, new technologies often emerged from research labs, new companies were formed to commercialize the technologies and over time they were introduced to the industry through the IT organization.
Today, CEOs initiate and demand digital transformations. This is a big shift: it’s a whole new paradigm for innovative technology adoption, driven as much by the existential nature of the risk (transform or die) and the magnitude of the challenge. While the CEO was typically not involved in IT decisions and strategies, today he or she is the driving force.
Data-Driven Transformation
Accordingly, as decision makers and implementers of policies that affect the lives of the people they serve, leaders have an important role in embedding data science in the public sector. They can involve the entire organization in the use of data and discuss its importance to the organization’s mission. By doing so, they can establish a culture that fosters data literacy throughout the workforce of public sector organizations.
Specifically, one of these skills that leaders must have is related to the formulation of data science projects, since they provide the opportunity to apply theoretical concepts to real-world data and gain practical experience.
While it is true that leaders in the public sector may not be experts in data science, it is important that they have a basic understanding of data science concepts and tools in order to make informed decisions. Leaders must understand if it is viable to use this innovation tool to improve a certain service, product or process that contributes to generating public value and improving people’s quality of life.
The aim of the course ‘Formulation of Data Science Projects for Public Managers‘ at GobLab UAI is to equip leaders with the methodology needed to develop data science projects, assess their feasibility and execute them responsibly. Throughout this program, participants are guided through the process of defining challenges, setting objectives, mapping data and assessing its maturity, identifying the necessary analytical approaches and acknowledging potential ethical considerations.
Guidelines for Formulating a Data Science Project
The conceptualization and design of a data science project should be led by individuals in leadership roles, as they possess expert knowledge of the subject matter and a clear vision of the public policy being pursued. The Inter-American Development Bank manual outlines the following stages in the design of a data science project:
- Problem Definition: The initial step in any data science project is to precisely define the public policy issue that will be addressed through the implementation of an artificial intelligence (AI)-driven decision-making and/or support tool.
- Feasibility Analysis: At this stage, leaders must assess whether the organization has the legal authority to address the problem, if it possesses the necessary human and financial resources for project execution and whether sufficient data is available.
- Objective Setting: This involves establishing objectives along with their associated metrics or indicators, which will serve as benchmarks for measuring achievements. These metrics should reflect the anticipated impact on the target population, and their attainment should contribute to resolving the identified problem.
- Description of Actions: These encompass the activities undertaken by the organization to translate the public policy response into tangible actions for addressing the problem. While these actions typically exist independently of the AI system, the AI system should assist in transforming them to meet project goals.
- Data Mapping: An investigation should be conducted to ascertain the existence of the necessary and adequate data required for project implementation. This includes determining whether the organization has access to relevant databases or if agreements will be needed to obtain access. AI projects can be founded on both internal and external, public or private data sources.
- Analysis Definition: Identifying the type of analysis and the necessary tools to solve the problem is crucial. This serves as an initial approximation that will subsequently be refined in collaboration with the project’s technical team.
- Ethical and Legal Considerations: Clear awareness of potential ethical and legal challenges that may arise during implementation is imperative. This enables the organization to proactively address situations that might jeopardize the project and take mitigating measures when necessary.
In conclusion, the process of conceptualization and project design must be approached iteratively. Initially, the aim is to establish a clear problem scope, but this perspective can evolve. For instance, if the institution lacks the requisite capacity to address it, or if it is determined that the necessary data is unavailable or insufficient, it becomes imperative to revisit and adapt the approach. Flexibility and adaptability are key to the successful development of data science projects in the dynamic landscape of public sector initiatives.
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). 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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Government Leaders: The Key Role in Data-Driven Transformation
The views expressed are those of the author and do not necessarily reflect the views of ASPA as an organization.
By Mauricio Covarrubias
September 8, 2023
According to Thomas M. Siebel, one of the primary responsibilities of leaders, whether in the corporate or government sectors, is to adeptly identify risks and seize opportunities. This involves recognizing the origins, assessing the magnitude and gauging the potential repercussions on vital goals. In today’s landscape, where digital transformation plays a central role, the stakes are notably high.
Empowering Transformation: A Top-Down Approach
Digital transformation is pervasive as it touches every part of the organization. Therefore, for digital transformation to be successful, writes Siebel, it must be driven from the top. In fact, digital transformation has completely reversed the technology adoption cycle that was prevalent in previous decades. Previously, new technologies often emerged from research labs, new companies were formed to commercialize the technologies and over time they were introduced to the industry through the IT organization.
Today, CEOs initiate and demand digital transformations. This is a big shift: it’s a whole new paradigm for innovative technology adoption, driven as much by the existential nature of the risk (transform or die) and the magnitude of the challenge. While the CEO was typically not involved in IT decisions and strategies, today he or she is the driving force.
Data-Driven Transformation
Accordingly, as decision makers and implementers of policies that affect the lives of the people they serve, leaders have an important role in embedding data science in the public sector. They can involve the entire organization in the use of data and discuss its importance to the organization’s mission. By doing so, they can establish a culture that fosters data literacy throughout the workforce of public sector organizations.
Specifically, one of these skills that leaders must have is related to the formulation of data science projects, since they provide the opportunity to apply theoretical concepts to real-world data and gain practical experience.
While it is true that leaders in the public sector may not be experts in data science, it is important that they have a basic understanding of data science concepts and tools in order to make informed decisions. Leaders must understand if it is viable to use this innovation tool to improve a certain service, product or process that contributes to generating public value and improving people’s quality of life.
The aim of the course ‘Formulation of Data Science Projects for Public Managers‘ at GobLab UAI is to equip leaders with the methodology needed to develop data science projects, assess their feasibility and execute them responsibly. Throughout this program, participants are guided through the process of defining challenges, setting objectives, mapping data and assessing its maturity, identifying the necessary analytical approaches and acknowledging potential ethical considerations.
Guidelines for Formulating a Data Science Project
The conceptualization and design of a data science project should be led by individuals in leadership roles, as they possess expert knowledge of the subject matter and a clear vision of the public policy being pursued. The Inter-American Development Bank manual outlines the following stages in the design of a data science project:
In conclusion, the process of conceptualization and project design must be approached iteratively. Initially, the aim is to establish a clear problem scope, but this perspective can evolve. For instance, if the institution lacks the requisite capacity to address it, or if it is determined that the necessary data is unavailable or insufficient, it becomes imperative to revisit and adapt the approach. Flexibility and adaptability are key to the successful development of data science projects in the dynamic landscape of public sector initiatives.
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). 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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