Justin Longo and Kathleen McNutt
- Published in print:
- 2018
- Published Online:
- January 2019
- ISBN:
- 9781447334910
- eISBN:
- 9781447334934
- Item type:
- chapter
- Publisher:
- Policy Press
- DOI:
- 10.1332/policypress/9781447334910.003.0018
- Subject:
- Political Science, Comparative Politics
Policy analysis relies on data collected at discrete intervals along the policy cycle, from problem identification through evaluation. Policy analytics, in contrast, represents the combination of ...
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Policy analysis relies on data collected at discrete intervals along the policy cycle, from problem identification through evaluation. Policy analytics, in contrast, represents the combination of new, ubiquitous, and continuous data sources—from Internet search and social media to mobile smartphones, Internet of Everything (IoE) devices, and electronic transaction cards—with new data analytics techniques for informing and directing policy choices. New technology platforms also offer the possibility of small-scale policy experiments that can be piloted with their effects precisely observed in real-time. This big data + analytics + real-time experiments approach offers a significant change to the traditional practice of policy analysis. This chapter describes the movement from policy analysis to policy analytics, discusses emergent examples and potential applications, and concludes with questions that can guide the appropriate adoption of policy analytics for supporting policymaking.Less
Policy analysis relies on data collected at discrete intervals along the policy cycle, from problem identification through evaluation. Policy analytics, in contrast, represents the combination of new, ubiquitous, and continuous data sources—from Internet search and social media to mobile smartphones, Internet of Everything (IoE) devices, and electronic transaction cards—with new data analytics techniques for informing and directing policy choices. New technology platforms also offer the possibility of small-scale policy experiments that can be piloted with their effects precisely observed in real-time. This big data + analytics + real-time experiments approach offers a significant change to the traditional practice of policy analysis. This chapter describes the movement from policy analysis to policy analytics, discusses emergent examples and potential applications, and concludes with questions that can guide the appropriate adoption of policy analytics for supporting policymaking.
Jennifer Stromer-Galley
- Published in print:
- 2019
- Published Online:
- August 2019
- ISBN:
- 9780190694043
- eISBN:
- 9780190694081
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190694043.003.0007
- Subject:
- Political Science, American Politics, Democratization
The quest for data-driven campaigning in 2012—creating massive databases of voter information for more effective micro-targeting—found greater efficacy and new controversy in 2016. The Trump campaign ...
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The quest for data-driven campaigning in 2012—creating massive databases of voter information for more effective micro-targeting—found greater efficacy and new controversy in 2016. The Trump campaign capitalized on the power of digital advertising to reach the public to engage in unprecedented mass-targeted campaigning. His campaign spent substantially more on Facebook and other digital media paid ads than Clinton. Yet, the company that Trump worked with, Cambridge Analytica, closed up shop in 2018 under a cloud of controversy about corrupt officials and voter manipulation in several countries, as well as ill-begotten data of Facebook users that drove their micro-targeting practices. The Clinton campaign modeled itself on data-driven successes of the Obama campaign, yet the algorithms that drove their decision making were flawed, thereby leading her campaign to underperform in essential swing states. Similar to the Romney campaign’s Narwhal challenges on Election Day when the campaign effectively was flying blind on get-out-the-vote numbers, the Clinton plane was flying on bad coordinates, ultimately causing her campaign to crash in critical swing states.Less
The quest for data-driven campaigning in 2012—creating massive databases of voter information for more effective micro-targeting—found greater efficacy and new controversy in 2016. The Trump campaign capitalized on the power of digital advertising to reach the public to engage in unprecedented mass-targeted campaigning. His campaign spent substantially more on Facebook and other digital media paid ads than Clinton. Yet, the company that Trump worked with, Cambridge Analytica, closed up shop in 2018 under a cloud of controversy about corrupt officials and voter manipulation in several countries, as well as ill-begotten data of Facebook users that drove their micro-targeting practices. The Clinton campaign modeled itself on data-driven successes of the Obama campaign, yet the algorithms that drove their decision making were flawed, thereby leading her campaign to underperform in essential swing states. Similar to the Romney campaign’s Narwhal challenges on Election Day when the campaign effectively was flying blind on get-out-the-vote numbers, the Clinton plane was flying on bad coordinates, ultimately causing her campaign to crash in critical swing states.
Alexis A. Fink and Evan F. Sinar
- Published in print:
- 2019
- Published Online:
- April 2019
- ISBN:
- 9780190879860
- eISBN:
- 9780190051075
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190879860.003.0027
- Subject:
- Psychology, Social Psychology
Advanced analytical methodologies and data visualizations are transforming how human resource data are used in organizations. Through the application of these methods, 360 Feedback can be enhanced; ...
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Advanced analytical methodologies and data visualizations are transforming how human resource data are used in organizations. Through the application of these methods, 360 Feedback can be enhanced; conversely, a variety of talent management objectives can be enhanced by including 360 Feedback. This chapter discusses how analytics can help support organizations and individuals as they tackle these systematic questions. The chapter begins with an overview of major talent management functions (selection, promotion, learning and development, succession and workforce planning, analytics for measuring progress), showing how each can incorporate 360 Feedback data. Attention is devoted to multirater feedback as a target of inquiry itself, discussing how new analytic approaches (unstructured data, linkage analysis, network analysis, reporting, and visualization) can enhance traditional practice. We close with a discussion of key considerations about foundational issues such as data privacy.Less
Advanced analytical methodologies and data visualizations are transforming how human resource data are used in organizations. Through the application of these methods, 360 Feedback can be enhanced; conversely, a variety of talent management objectives can be enhanced by including 360 Feedback. This chapter discusses how analytics can help support organizations and individuals as they tackle these systematic questions. The chapter begins with an overview of major talent management functions (selection, promotion, learning and development, succession and workforce planning, analytics for measuring progress), showing how each can incorporate 360 Feedback data. Attention is devoted to multirater feedback as a target of inquiry itself, discussing how new analytic approaches (unstructured data, linkage analysis, network analysis, reporting, and visualization) can enhance traditional practice. We close with a discussion of key considerations about foundational issues such as data privacy.
Geoffrey Rockwell and Stéfan Sinclair
- Published in print:
- 2016
- Published Online:
- January 2017
- ISBN:
- 9780262034357
- eISBN:
- 9780262332064
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262034357.003.0007
- Subject:
- Society and Culture, Cultural Studies
What is big data, and what does it have to do with the humanities? The Snowden revelations have drawn attention to the opportunities and dangers to the gathering of large collections of data, ...
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What is big data, and what does it have to do with the humanities? The Snowden revelations have drawn attention to the opportunities and dangers to the gathering of large collections of data, including the collecting of text messages and email. Techniques that digital humanists have used in the study of individual texts are now being scaled up to study large collections. The digital humanities have a valuable historical and ethical perspective on big data analytics. Questions about what to do with too much information go back to Plato. Questions about the completeness of data, the usefulness of metadata, and the value of analytics can help us understand what big data can and cannot do. In particular we need to be careful of false positives, or false predictions based on data too large to check with other methods.Less
What is big data, and what does it have to do with the humanities? The Snowden revelations have drawn attention to the opportunities and dangers to the gathering of large collections of data, including the collecting of text messages and email. Techniques that digital humanists have used in the study of individual texts are now being scaled up to study large collections. The digital humanities have a valuable historical and ethical perspective on big data analytics. Questions about what to do with too much information go back to Plato. Questions about the completeness of data, the usefulness of metadata, and the value of analytics can help us understand what big data can and cannot do. In particular we need to be careful of false positives, or false predictions based on data too large to check with other methods.
Maurizio Borghi and Stavroula Karapapa
- Published in print:
- 2013
- Published Online:
- May 2013
- ISBN:
- 9780199664559
- eISBN:
- 9780191758409
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199664559.003.0003
- Subject:
- Law, Intellectual Property, IT, and Media Law
One of the most prominent features of mass digitization is the automated processing of works for various research-related and commercial purposes. This includes text mining or linguistic analysis ...
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One of the most prominent features of mass digitization is the automated processing of works for various research-related and commercial purposes. This includes text mining or linguistic analysis over masses of texts, image analysis, information extraction, automatic translation, data mining for behavioural profiling, and so on. With a few exceptions, copying for automated processing or computational analysis does not feature in statutory language and its status remains uncertain. While, historically, ‘machine-reading’ has challenged copyright norms at some instances, there is currently need for a careful consideration of the parameters of its permissibility.Less
One of the most prominent features of mass digitization is the automated processing of works for various research-related and commercial purposes. This includes text mining or linguistic analysis over masses of texts, image analysis, information extraction, automatic translation, data mining for behavioural profiling, and so on. With a few exceptions, copying for automated processing or computational analysis does not feature in statutory language and its status remains uncertain. While, historically, ‘machine-reading’ has challenged copyright norms at some instances, there is currently need for a careful consideration of the parameters of its permissibility.
Jennifer Stromer-Galley
- Published in print:
- 2019
- Published Online:
- August 2019
- ISBN:
- 9780190694043
- eISBN:
- 9780190694081
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190694043.003.0006
- Subject:
- Political Science, American Politics, Democratization
The 2012 presidential candidates refined proven practices and ran the most data-driven campaigns in history. The candidates deployed social media, strategic online ad buys, and used their websites as ...
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The 2012 presidential candidates refined proven practices and ran the most data-driven campaigns in history. The candidates deployed social media, strategic online ad buys, and used their websites as the cornerstones of their campaign practices in the increasingly complex, hybrid media environment. Obama’s and Romney’s campaigns produced a variety of tactics to interact with supporters in a way that suggested that controlled interactivity had been perfected. They built massive voter files to target usual demographic groups while expanding to new groups typically unreached by campaigns and conducted careful message testing to yield maximum effect. Yet, for the carefully scripted work to structure interactivity between supporters and the campaign and among supporters to greatest advantage for the candidate, a substantial challenge remained: how to manage messaging in the complex, hybrid media environment where gaffes and opposition discourse can be amplified in ways unintended and with unknown consequences for campaigns.Less
The 2012 presidential candidates refined proven practices and ran the most data-driven campaigns in history. The candidates deployed social media, strategic online ad buys, and used their websites as the cornerstones of their campaign practices in the increasingly complex, hybrid media environment. Obama’s and Romney’s campaigns produced a variety of tactics to interact with supporters in a way that suggested that controlled interactivity had been perfected. They built massive voter files to target usual demographic groups while expanding to new groups typically unreached by campaigns and conducted careful message testing to yield maximum effect. Yet, for the carefully scripted work to structure interactivity between supporters and the campaign and among supporters to greatest advantage for the candidate, a substantial challenge remained: how to manage messaging in the complex, hybrid media environment where gaffes and opposition discourse can be amplified in ways unintended and with unknown consequences for campaigns.
William B. Rouse
- Published in print:
- 2019
- Published Online:
- September 2019
- ISBN:
- 9780198846420
- eISBN:
- 9780191881589
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198846420.001.0001
- Subject:
- Mathematics, Logic / Computer Science / Mathematical Philosophy
This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers ...
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This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.Less
This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.
