Expanding Data Fluency: Embracing Data Management in the Public Technological Landscape
The views expressed are those of the author and do not necessarily reflect the views of ASPA as an organization.
By Kiersten Farmer
July 21, 2023
Introduction
Traditionally, data fluency aims to improve data communication. A key goal of Data Fluency: Empowering Your Organization with Effective Data Communication is to help companies find better ways to help others understand. As Zach Gemignani, Chris Gemignani, Richard Galentino and Patrick Schuermann state in this book, creating “information experiences” that transform how audiences think about a subject and make better decisions is still effective. It involves skills such as data visualization, storytelling and presenting complex information in a clear and understandable manner. While these skills remain crucial, they represent only one facet of data fluency. In our current technological environment, there is a pressing need for a more comprehensive definition that encompasses the depth derived from the systemic data intimacy required to navigate this dynamic landscape.
This column aims to advocate for an expanded definition by exploring the distinctions between the revised and former definitions of data fluency. Additionally, it delves into the nuanced differences between data fluency and data literacy within this revised framework. By addressing these aspects, we can better understand the evolving nature of data fluency and its significance in the context of our rapidly changing technological world.
The Expanded Definition of Data Fluency
The volume and use of complex data are increasing exponentially as technology continues to advance. To navigate this data-driven landscape effectively, it is no longer sufficient to focus primarily on communication skills. The expanded definition of data fluency recognizes that fluency in data goes beyond effectively conveying insights. It also encompasses the familiarity derived from the ability to manage data effectively, ensuring its quality, security and compliance throughout its lifecycle. This means that data fluency is no longer just about being able to interpret data but is also about having the technical skills to prepare it, manipulate it and ensure it is secure and compliant with regulations. Effective data management practices facilitate our ability to comprehend the nuances of analysis better, ensuring that data is effectively utilized to drive meaningful outcomes based on derived insights. Thus, organizational data fluency must also address the skills necessary to manage data throughout its lifecycle.
The new definition of data fluency refers to the ability to not only communicate data insights clearly and compellingly but also engage deeply with data across departments and processes. It involves a holistic understanding of data interdependencies, recognizing patterns, relationships and dynamics to derive meaningful insights and make informed decisions. It goes beyond surface-level data literacy, emphasizing a comprehensive set of competencies that enable organizations to navigate the complexities of the technological landscape and maximize the transformative power of data-driven initiatives. As a result of expanding the definition of data fluency, we recognize the importance of developing competencies beyond communication, encompassing the data management skills necessary for individuals and organizations.
Further Distinguishing Data Fluency from Data Literacy
As with the former, it is essential to distinguish the expanded definition of data fluency from data literacy. Data literacy acts as a stepping stone toward data fluency. It primarily focuses on developing basic knowledge and understanding of data concepts, tools and analysis techniques. It provides individuals with the ability to read and comprehend data, interpret visualizations and understand fundamental data-related terminology.
On the other hand, data fluency is not a static achievement. It is an ongoing process that continually iterates beyond data literacy to improve over time. It involves continuous learning, exploration and adaptation to keep pace with advancements in data science, technology and the changing data landscape. While data literacy emphasizes the “what” and “how” of data, data fluency encompasses the “why” and “so what” aspects. It enables individuals to critically evaluate data sources, identify biases, ensure data quality and apply best practices for effective data management. As organizations progress along the data literacy-data fluency continuum, they refine their skills, deepen their understanding and become more adept at extracting meaningful insights from data through effective data management practices.
Conclusion
In the dynamic and ever-changing technological landscape, the definition of data fluency must evolve to encompass data management best practices alongside effective data communication. By expanding the definition, organizations can develop comprehensive competencies to navigate the complexities of data. Data fluency, with its emphasis on both communication and management, enables individuals to effectively analyze, interpret, communicate and manage data throughout its lifecycle. By embracing the expanded definition of data fluency, organizations can maximize the value of their data assets, mitigate risks and make informed decisions that drive positive outcomes in the data-driven era.
Author: Kiersten Farmer is a seasoned data professional and speaker dedicated to helping state and local governments thrive. With nearly 20 years of public administration experience, she currently serves as the City of Henderson Data Scientist, maximizing operational and strategic benefits through technology and information systems. Kiersten excels in developing comprehensive methodologies for evaluating operations, communicating insights, and analyzing administrative processes. She holds degrees from Florida Agricultural & Mechanical University and the University of Maryland and is a PhD Candidate at the University of Nevada, Las Vegas. Contact Kiersten at [email protected] or https://www.linkedin.com/in/kiersten-farmer/.
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Expanding Data Fluency: Embracing Data Management in the Public Technological Landscape
The views expressed are those of the author and do not necessarily reflect the views of ASPA as an organization.
By Kiersten Farmer
July 21, 2023
Introduction
Traditionally, data fluency aims to improve data communication. A key goal of Data Fluency: Empowering Your Organization with Effective Data Communication is to help companies find better ways to help others understand. As Zach Gemignani, Chris Gemignani, Richard Galentino and Patrick Schuermann state in this book, creating “information experiences” that transform how audiences think about a subject and make better decisions is still effective. It involves skills such as data visualization, storytelling and presenting complex information in a clear and understandable manner. While these skills remain crucial, they represent only one facet of data fluency. In our current technological environment, there is a pressing need for a more comprehensive definition that encompasses the depth derived from the systemic data intimacy required to navigate this dynamic landscape.
This column aims to advocate for an expanded definition by exploring the distinctions between the revised and former definitions of data fluency. Additionally, it delves into the nuanced differences between data fluency and data literacy within this revised framework. By addressing these aspects, we can better understand the evolving nature of data fluency and its significance in the context of our rapidly changing technological world.
The Expanded Definition of Data Fluency
The volume and use of complex data are increasing exponentially as technology continues to advance. To navigate this data-driven landscape effectively, it is no longer sufficient to focus primarily on communication skills. The expanded definition of data fluency recognizes that fluency in data goes beyond effectively conveying insights. It also encompasses the familiarity derived from the ability to manage data effectively, ensuring its quality, security and compliance throughout its lifecycle. This means that data fluency is no longer just about being able to interpret data but is also about having the technical skills to prepare it, manipulate it and ensure it is secure and compliant with regulations. Effective data management practices facilitate our ability to comprehend the nuances of analysis better, ensuring that data is effectively utilized to drive meaningful outcomes based on derived insights. Thus, organizational data fluency must also address the skills necessary to manage data throughout its lifecycle.
The new definition of data fluency refers to the ability to not only communicate data insights clearly and compellingly but also engage deeply with data across departments and processes. It involves a holistic understanding of data interdependencies, recognizing patterns, relationships and dynamics to derive meaningful insights and make informed decisions. It goes beyond surface-level data literacy, emphasizing a comprehensive set of competencies that enable organizations to navigate the complexities of the technological landscape and maximize the transformative power of data-driven initiatives. As a result of expanding the definition of data fluency, we recognize the importance of developing competencies beyond communication, encompassing the data management skills necessary for individuals and organizations.
Further Distinguishing Data Fluency from Data Literacy
As with the former, it is essential to distinguish the expanded definition of data fluency from data literacy. Data literacy acts as a stepping stone toward data fluency. It primarily focuses on developing basic knowledge and understanding of data concepts, tools and analysis techniques. It provides individuals with the ability to read and comprehend data, interpret visualizations and understand fundamental data-related terminology.
On the other hand, data fluency is not a static achievement. It is an ongoing process that continually iterates beyond data literacy to improve over time. It involves continuous learning, exploration and adaptation to keep pace with advancements in data science, technology and the changing data landscape. While data literacy emphasizes the “what” and “how” of data, data fluency encompasses the “why” and “so what” aspects. It enables individuals to critically evaluate data sources, identify biases, ensure data quality and apply best practices for effective data management. As organizations progress along the data literacy-data fluency continuum, they refine their skills, deepen their understanding and become more adept at extracting meaningful insights from data through effective data management practices.
Conclusion
In the dynamic and ever-changing technological landscape, the definition of data fluency must evolve to encompass data management best practices alongside effective data communication. By expanding the definition, organizations can develop comprehensive competencies to navigate the complexities of data. Data fluency, with its emphasis on both communication and management, enables individuals to effectively analyze, interpret, communicate and manage data throughout its lifecycle. By embracing the expanded definition of data fluency, organizations can maximize the value of their data assets, mitigate risks and make informed decisions that drive positive outcomes in the data-driven era.
Author: Kiersten Farmer is a seasoned data professional and speaker dedicated to helping state and local governments thrive. With nearly 20 years of public administration experience, she currently serves as the City of Henderson Data Scientist, maximizing operational and strategic benefits through technology and information systems. Kiersten excels in developing comprehensive methodologies for evaluating operations, communicating insights, and analyzing administrative processes. She holds degrees from Florida Agricultural & Mechanical University and the University of Maryland and is a PhD Candidate at the University of Nevada, Las Vegas. Contact Kiersten at [email protected] or https://www.linkedin.com/in/kiersten-farmer/.
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