WEEK 1
Vydra, S., & Klievink, B. (2019). Techno-optimism and policy-pessimism in the public
sector big data debate. Government Information Quarterly, 36(4), 101383. [Digital
version available through Leiden University Library]
Main Focus
The paper explores two contrasting perspectives on the use of big data in public sector
decision-making:
1. Techno-optimism: Big data is seen as transformative, providing better insights,
faster decisions, and solving complex policy problems.
2. Policy-pessimism: Political and bureaucratic constraints limit big data's potential,
questioning its real-world impact.
Key Arguments
1. "Better" Data Doesn't Always Mean Better Decisions
○ Techno-optimists argue big data improves accuracy and decision-making.
○ However, issues such as data quality, representativeness, and noise
challenge its reliability.
○ Translating insights into policies is complex due to political dynamics and
cherry-picking of evidence.
2. The Illusion of Real-Time Decisions
○Big data can provide faster insights, but policymaking cannot always adapt to
this speed.
○ Long-term policy issues (e.g., education, health) cannot rely solely on
real-time data, and decision-making remains slow due to institutional
constraints.
3. Epistemological Concerns
○Techno-optimists emphasize correlation and prediction over understanding
causality.
○ In policymaking, understanding the "why" (causality) is often more critical
than the "what" (correlation).
4. Overlooking Critical Issues (Privacy)
○ Privacy concerns are often downplayed as "future problems" to be solved
later.
○ There is an inherent trade-off between protecting privacy and maintaining
data accuracy, with no perfect solution in sight.
Conclusion
, ● Techno-optimism assumes that technical advancements will overcome policy
challenges.
● Policy-pessimism highlights the political, institutional, and ethical barriers to fully
leveraging big data.
● The authors argue for a realist approach: evaluating big data's impact in specific
contexts, balancing its benefits and limitations.
Morrow, J. (2019). Why everyone should be data literate. TEDxBoise. Available at
https://www.youtube.com/watch?v=8ovyQZ_Z8Xs
In his TEDxBoise talk, "Why Everyone Should Be Data Literate," Jordan Morrow emphasizes
the critical importance of data literacy in today's digital era, often referred to as the Fourth
Industrial Revolution.
He defines data literacy as the ability to read, work with, analyze, and argue with data—skills
essential for making informed decisions in both personal and professional contexts.
Morrow outlines four key competencies of data literacy:
1. Reading Data: Understanding and interpreting data presented in various forms.
2. Working with Data: Comfortably engaging with data to extract meaningful insights.
3. Analyzing Data: Delving deeper to comprehend the underlying reasons behind data
trends.
4. Arguing with Data: Critically evaluating data and constructing well-supported arguments
based on it.
He also highlights the significance of curiosity and creativity in enhancing data literacy,
encouraging individuals to ask questions and think innovatively when interacting with data.
Morrow asserts that data literacy is not limited to data scientists; rather, it is a vital skill for
everyone to navigate the information-rich world effectively.
By developing these competencies, individuals can better discern truth from misinformation
and make smarter decisions in the digital age.
WEEK 2
Dunleavy, P., Margetts, H., Bastow, S., & Tinkler, J. (2006). New public management
is dead— long live digital-era governance. Journal of public administration research
and theory, 16(3), 467- 494
Main Argument
The authors argue that New Public Management (NPM), which dominated public sector
reforms for decades, is now outdated. NPM emphasized disaggregation, competition, and
, incentivization but led to unintended consequences like fragmentation, inefficiency, and
increased policy complexity. A new wave of reform, termed Digital-Era Governance (DEG),
is emerging, driven by digitalization and IT advancements.
Key Criticisms of NPM
1. Disaggregation:
○NPM fragmented public sector organizations into smaller units, causing
inefficiencies and coordination problems.
○ Example: Over 300 small ministries in New Zealand created policy silos.
2. Competition:
○ Market-based approaches (e.g., outsourcing and quasi-markets) often failed
to improve quality or reduce costs.
○ Example: UK healthcare saw increased administrative costs in quasi-market
systems.
3. Incentivization:
○ Performance-based pay and privatization disrupted the public service ethos
and created perverse incentives.
○ Critics argued it reduced collaboration and caused short-term thinking.
4. Policy Complexity:
○ Fragmentation under NPM increased institutional complexity and reduced
citizens' ability to interact with government effectively.
Features of Digital-Era Governance (DEG)
The authors propose DEG as a successor to NPM. It focuses on three key themes:
1. Reintegration:
○ Reverse fragmentation by merging agencies and simplifying networks.
○ Examples: UK’s joined-up government and integration of tax and welfare
services.
2. Needs-Based Holism:
○ Shift to citizen-centered, needs-based services with holistic, streamlined
processes.
○ Features:
■ One-stop provision: Centralized access to multiple services.
■ Data warehousing: Integrating data systems for proactive service
delivery.
■ End-to-end reengineering: Simplifying administrative processes.
3. Digitization:
Vydra, S., & Klievink, B. (2019). Techno-optimism and policy-pessimism in the public
sector big data debate. Government Information Quarterly, 36(4), 101383. [Digital
version available through Leiden University Library]
Main Focus
The paper explores two contrasting perspectives on the use of big data in public sector
decision-making:
1. Techno-optimism: Big data is seen as transformative, providing better insights,
faster decisions, and solving complex policy problems.
2. Policy-pessimism: Political and bureaucratic constraints limit big data's potential,
questioning its real-world impact.
Key Arguments
1. "Better" Data Doesn't Always Mean Better Decisions
○ Techno-optimists argue big data improves accuracy and decision-making.
○ However, issues such as data quality, representativeness, and noise
challenge its reliability.
○ Translating insights into policies is complex due to political dynamics and
cherry-picking of evidence.
2. The Illusion of Real-Time Decisions
○Big data can provide faster insights, but policymaking cannot always adapt to
this speed.
○ Long-term policy issues (e.g., education, health) cannot rely solely on
real-time data, and decision-making remains slow due to institutional
constraints.
3. Epistemological Concerns
○Techno-optimists emphasize correlation and prediction over understanding
causality.
○ In policymaking, understanding the "why" (causality) is often more critical
than the "what" (correlation).
4. Overlooking Critical Issues (Privacy)
○ Privacy concerns are often downplayed as "future problems" to be solved
later.
○ There is an inherent trade-off between protecting privacy and maintaining
data accuracy, with no perfect solution in sight.
Conclusion
, ● Techno-optimism assumes that technical advancements will overcome policy
challenges.
● Policy-pessimism highlights the political, institutional, and ethical barriers to fully
leveraging big data.
● The authors argue for a realist approach: evaluating big data's impact in specific
contexts, balancing its benefits and limitations.
Morrow, J. (2019). Why everyone should be data literate. TEDxBoise. Available at
https://www.youtube.com/watch?v=8ovyQZ_Z8Xs
In his TEDxBoise talk, "Why Everyone Should Be Data Literate," Jordan Morrow emphasizes
the critical importance of data literacy in today's digital era, often referred to as the Fourth
Industrial Revolution.
He defines data literacy as the ability to read, work with, analyze, and argue with data—skills
essential for making informed decisions in both personal and professional contexts.
Morrow outlines four key competencies of data literacy:
1. Reading Data: Understanding and interpreting data presented in various forms.
2. Working with Data: Comfortably engaging with data to extract meaningful insights.
3. Analyzing Data: Delving deeper to comprehend the underlying reasons behind data
trends.
4. Arguing with Data: Critically evaluating data and constructing well-supported arguments
based on it.
He also highlights the significance of curiosity and creativity in enhancing data literacy,
encouraging individuals to ask questions and think innovatively when interacting with data.
Morrow asserts that data literacy is not limited to data scientists; rather, it is a vital skill for
everyone to navigate the information-rich world effectively.
By developing these competencies, individuals can better discern truth from misinformation
and make smarter decisions in the digital age.
WEEK 2
Dunleavy, P., Margetts, H., Bastow, S., & Tinkler, J. (2006). New public management
is dead— long live digital-era governance. Journal of public administration research
and theory, 16(3), 467- 494
Main Argument
The authors argue that New Public Management (NPM), which dominated public sector
reforms for decades, is now outdated. NPM emphasized disaggregation, competition, and
, incentivization but led to unintended consequences like fragmentation, inefficiency, and
increased policy complexity. A new wave of reform, termed Digital-Era Governance (DEG),
is emerging, driven by digitalization and IT advancements.
Key Criticisms of NPM
1. Disaggregation:
○NPM fragmented public sector organizations into smaller units, causing
inefficiencies and coordination problems.
○ Example: Over 300 small ministries in New Zealand created policy silos.
2. Competition:
○ Market-based approaches (e.g., outsourcing and quasi-markets) often failed
to improve quality or reduce costs.
○ Example: UK healthcare saw increased administrative costs in quasi-market
systems.
3. Incentivization:
○ Performance-based pay and privatization disrupted the public service ethos
and created perverse incentives.
○ Critics argued it reduced collaboration and caused short-term thinking.
4. Policy Complexity:
○ Fragmentation under NPM increased institutional complexity and reduced
citizens' ability to interact with government effectively.
Features of Digital-Era Governance (DEG)
The authors propose DEG as a successor to NPM. It focuses on three key themes:
1. Reintegration:
○ Reverse fragmentation by merging agencies and simplifying networks.
○ Examples: UK’s joined-up government and integration of tax and welfare
services.
2. Needs-Based Holism:
○ Shift to citizen-centered, needs-based services with holistic, streamlined
processes.
○ Features:
■ One-stop provision: Centralized access to multiple services.
■ Data warehousing: Integrating data systems for proactive service
delivery.
■ End-to-end reengineering: Simplifying administrative processes.
3. Digitization:
