Governance and Digitalisation
Week 1: Introduction to Digitalisation and Governance
Vydra & Klievink, 2019 + Morrow, J., 2019
Vydra & Klievink, 2019: Techno-optimism and Policy-pessimism in the
public sector big data debate
The adoption of big data appears to be a slow and uneven process that takes
different forms and happens at different speeds based on the institutional and
policy context.
There seem to be two archetypical narratives present in the existing literature.
First, a narrative focused on the study of big data analytics as a technological
phenomenon, focusing on its comparative (dis)advantages to how ‘traditional’
data is created, handled and analysed, often rooted in engineering and computer
science disciplines. Second, a narrative focusing on decision-making and the
study of how quantitative evidence and the advent of big data interacts with
political and bureaucratic decision-making, often rooted in public administration
and organisational decision-making disciplines.
We could argue that the first narrative is optimist and the latter is pessimist with
regards to the impact of big data on policymaking. We attribute this difference to
the fact that technology evolves and is adopted very rapidly compared to how
slowly political and governance practices change, making the technical narrative
optimistic and the policy and decision-making narrative pessimistic about the
magnitude of change big data will have on public sector and governance in
general.
- Techno-optimism and policy-pessimism.
The most common big data definition uses a set of V’s (volume, variety, velocity,
veracity (variability, visualisation, value)) attributes along which big data differs
from ‘normal’ data.
- This way of defining big data itself seems to be rather techno-optimist, as
the attributes are primarily technical and describe the nature of the data
itself.
The policy-pessimist definitions of big data revolve around the social change big
data motivates, especially in terms of changes to decision-making processes
necessary to make use of big data.
Techno-optimism focuses on data and analytical output whereas policy-pessimism
focuses on humans turning data into insight and humans making decisions in
bureaucratic structures.
It is difficult to disentangle the techno-optimist narrative into key arguments and
assumptions because the benefits and shortcomings of big data articulated in the
literature are numerous and how these should be aggregated into key arguments
and assumptions is not obvious.
A fundamental argument of a techno-optimist narrative is that big data will
provide better information and that this better information will in turn facilitate
better decisions. The argument essentially claims that the more quality and
accurate information is available, the better the decisions will be.
,Taking accuracy and uncertainty as two aspects of data quality highlighted by the
techno-optimist argument, it is important to point out that not all big data sets by
default allow for more accurate insights: firstly, big data sources often struggle
with substantial representativeness problems that have been described both
empirically and conceptually, making the resulting insight skewed and thus
inaccurate in that sense. Secondly, big data often contain much more noise than
signal and this noise has to be removed to arrive at reliable conclusions, which in
itself presents an analytical challenge that introduces inaccuracy.
More importantly, accuracy is not the only attribute of data for policymaking.
When it comes to big data, quality is composed of several elements, such as
accuracy, reliability, relevance or timeliness. Reliability refers to the trust
policymakers have in a specific indicator, which is established by having a good
track record of accuracy and relevance for policy questions.
There are broadly speaking four reasons for why data backrun is crucial for data
quality.
1. Better temporal coverage of a data set allows traditional statistical
methods to generate better inferential leverage.
2. It provides crucial contextualization to any data insight.
3. (most important) as crucial indicators build up a reliable backdrop they get
institutionalized into domestic and international policymaking practices.
4. This institutionalization also achieves international comparability, which
big data sources struggle with as they vary greatly from country to country
and cannot really be controlled by a statistical institution since much of big
data is privately owned.
Data are objects of knowledge but also power, meaning they cannot be
universally ‘better’ in a non-partisan way.
The idea that better information leads to better decisions assumes a rather linear
view of policy making, where information only enters at certain places, often
represented in terms of a policy cycle. Rather, they are the product of multiple,
interacting actors, that are interdependent and are hard to commit to a common
problem, solution or even the value of facts. The result is a complex policy battle
in which decision-making often takes place through small, incremental steps and
consist of several iterations between processes, making it a plate of spaghetti
rather than a cycle.
- In the case of big data, the process between information and decisions is
subject to politicization in at least two distinct ways.
o Firstly, transforming big data into information and insights is not a
politically neutral process much of which depends on who decides
what data is worth, what is included, what is excluded, how data are
aggregated.
o Secondly, there are political decisions to be made not only in
interpreting the data, but also in gathering it.
Given the political nature of collecting and interpreting data, the more data there
are, the more political choices will have to be made by those deriving meaning
from the data.
The techno-optimist narrative also maintains that big data analytics produce
faster information which in turn leads to faster decisions. This argument rests on
two assumptions:
, - That it is possible to generate relevant real-time data to inform policy
decisions.
- That policy making can adapt to the speed of this data.
Public administrations do not work along clearly demarcated inductive and
deductive lines and are often open to doing what works regardless of the
epistemological implications.
In a way, a techno-optimist view of big data analytics makes it very difficult to
engage with the issue of privacy: if we speak of data and the patterns they
contain as something that is inherently objective and meaningful, out data sets
need to mirror social reality as closely as possible. However, in order to avoid
privacy breaches, we need to distort our data.
- This dilemma is at the root of the trade-off between privacy protection and
the validity of empirical inferences one can derive from a dataset.
Key issue: quality of big data insight and how that translates into quality of
decisions:
Techno-optimist narrative:
- Big data provides more information which means better insight and better
predictive capabilities, which then translates into better informed (and thus
generally better) policy decision.
Policy-pessimist narrative:
- On important quality dimensions big data is not better for policymaking
than traditional data. Politicians will always cherry-pick data that suits their
agenda – more data will diffuse the meaning of evidence based and result
in more political strategizing.
Key issue: speed of big data analysis and how that translates to speed of
decisions:
Techno-optimist narrative:
- Real-time data streams provide more up-to-date information faster than
currently available data, meaning that policy decisions can be made faster,
making policy more agile.
Policy-pessimist narrative:
- Decision-making will not adapt to the speed of data, as negotiations of the
data by humans is a crucial part of the process. Faster data is not available
for most policy questions.
Key issue: epistemology of big data analysis:
Techno-optimist narrative:
- A more inductive approach based on correlation and prediction rather than
causation as long as the dataset is of sufficient size.
Policy-pessimist narrative:
- No substantial change to scientific method, muting the effect big data
analytics will have as they are tailored for inductive exploration and not
deductive testing.
Key issue: connection between big data analysis and fundamental concerns
related to it:
Techno-optimist narrative:
, - Privacy and other fundamental issues with big data matter, but can be
overcome down the line with more advanced technological solutions or
policy interventions.
Policy-pessimist narrative:
- Privacy and other fundamental big data issues are crucial and cannot be
(fully) reconciled with big data analytics. To avoid them we should stop or
limit big data analytics.
Morrow, J., 2019: Why everyone should be data literate
- How do we take information and make a smart, informed decision. -> skill:
data-literacy
Data literacy: the ability to read, work with, analyse, and argue with data (it is not
data science)
1. Step 1: read the given information/data and comprehend it.
2. Step 2: being comfortable with information when it is presented to you
(work with data -> determine of it is a hoax)
3. Step 3: analyse data: so that we can take it in and find the insight to make
the right decision.
4. Step 4: arguing with data: 1. interrogate the information as it is presented
to you, 2. Ability to put a position forward and back it up with information.
How to become better at data-literacy? Two C’s:
1. Become curious
2. Creativity
Week 2: E-government: the impact of digital transformation
Dunleavy et al., 2006 + Meijer & Thaens, 2021 + Piperal, 2019
Dunleavy et al., 2006: New Public Management is Dead-long live Digital-
Era Governance
The New Public Management (NPM) wave in public sector organizational change
was founded on themes of disaggregation, competition, and incentivization.
Although its effects are still working through in countries new to NMP, this wave
has now largely stalled or been reversed in some key ‘leading-edge’ countries.
The overall movement incorporating these new shifts is toward ‘digital-era
governance’ (DEG), which involves reintegrating functions into the governmental
sphere, adopting holistic and needs-oriented structures, and progressing
digitalization of administrative processes. DEG offers a perhaps unique
opportunity to create self-sustaining change, in a broad range of closely
connected technological, organizational, cultural, and social effects.
Key parts of the NMP reform message have been reversed because they lead to
policy disaster, and other large parts are stalled.
There is a scattering of proposals for characterizing the post-NPM wave of
management changes that is currently underway. Many seem overly optimistic,
looking forward to banishing bureaucracy or achieving a post-bureaucratic
administration.
Highlighting the central importance of information technology (IT) based changes
in management systems and in methods of interacting with citizens and other
service-users in civil society in the underpinning and integrating of current
Week 1: Introduction to Digitalisation and Governance
Vydra & Klievink, 2019 + Morrow, J., 2019
Vydra & Klievink, 2019: Techno-optimism and Policy-pessimism in the
public sector big data debate
The adoption of big data appears to be a slow and uneven process that takes
different forms and happens at different speeds based on the institutional and
policy context.
There seem to be two archetypical narratives present in the existing literature.
First, a narrative focused on the study of big data analytics as a technological
phenomenon, focusing on its comparative (dis)advantages to how ‘traditional’
data is created, handled and analysed, often rooted in engineering and computer
science disciplines. Second, a narrative focusing on decision-making and the
study of how quantitative evidence and the advent of big data interacts with
political and bureaucratic decision-making, often rooted in public administration
and organisational decision-making disciplines.
We could argue that the first narrative is optimist and the latter is pessimist with
regards to the impact of big data on policymaking. We attribute this difference to
the fact that technology evolves and is adopted very rapidly compared to how
slowly political and governance practices change, making the technical narrative
optimistic and the policy and decision-making narrative pessimistic about the
magnitude of change big data will have on public sector and governance in
general.
- Techno-optimism and policy-pessimism.
The most common big data definition uses a set of V’s (volume, variety, velocity,
veracity (variability, visualisation, value)) attributes along which big data differs
from ‘normal’ data.
- This way of defining big data itself seems to be rather techno-optimist, as
the attributes are primarily technical and describe the nature of the data
itself.
The policy-pessimist definitions of big data revolve around the social change big
data motivates, especially in terms of changes to decision-making processes
necessary to make use of big data.
Techno-optimism focuses on data and analytical output whereas policy-pessimism
focuses on humans turning data into insight and humans making decisions in
bureaucratic structures.
It is difficult to disentangle the techno-optimist narrative into key arguments and
assumptions because the benefits and shortcomings of big data articulated in the
literature are numerous and how these should be aggregated into key arguments
and assumptions is not obvious.
A fundamental argument of a techno-optimist narrative is that big data will
provide better information and that this better information will in turn facilitate
better decisions. The argument essentially claims that the more quality and
accurate information is available, the better the decisions will be.
,Taking accuracy and uncertainty as two aspects of data quality highlighted by the
techno-optimist argument, it is important to point out that not all big data sets by
default allow for more accurate insights: firstly, big data sources often struggle
with substantial representativeness problems that have been described both
empirically and conceptually, making the resulting insight skewed and thus
inaccurate in that sense. Secondly, big data often contain much more noise than
signal and this noise has to be removed to arrive at reliable conclusions, which in
itself presents an analytical challenge that introduces inaccuracy.
More importantly, accuracy is not the only attribute of data for policymaking.
When it comes to big data, quality is composed of several elements, such as
accuracy, reliability, relevance or timeliness. Reliability refers to the trust
policymakers have in a specific indicator, which is established by having a good
track record of accuracy and relevance for policy questions.
There are broadly speaking four reasons for why data backrun is crucial for data
quality.
1. Better temporal coverage of a data set allows traditional statistical
methods to generate better inferential leverage.
2. It provides crucial contextualization to any data insight.
3. (most important) as crucial indicators build up a reliable backdrop they get
institutionalized into domestic and international policymaking practices.
4. This institutionalization also achieves international comparability, which
big data sources struggle with as they vary greatly from country to country
and cannot really be controlled by a statistical institution since much of big
data is privately owned.
Data are objects of knowledge but also power, meaning they cannot be
universally ‘better’ in a non-partisan way.
The idea that better information leads to better decisions assumes a rather linear
view of policy making, where information only enters at certain places, often
represented in terms of a policy cycle. Rather, they are the product of multiple,
interacting actors, that are interdependent and are hard to commit to a common
problem, solution or even the value of facts. The result is a complex policy battle
in which decision-making often takes place through small, incremental steps and
consist of several iterations between processes, making it a plate of spaghetti
rather than a cycle.
- In the case of big data, the process between information and decisions is
subject to politicization in at least two distinct ways.
o Firstly, transforming big data into information and insights is not a
politically neutral process much of which depends on who decides
what data is worth, what is included, what is excluded, how data are
aggregated.
o Secondly, there are political decisions to be made not only in
interpreting the data, but also in gathering it.
Given the political nature of collecting and interpreting data, the more data there
are, the more political choices will have to be made by those deriving meaning
from the data.
The techno-optimist narrative also maintains that big data analytics produce
faster information which in turn leads to faster decisions. This argument rests on
two assumptions:
, - That it is possible to generate relevant real-time data to inform policy
decisions.
- That policy making can adapt to the speed of this data.
Public administrations do not work along clearly demarcated inductive and
deductive lines and are often open to doing what works regardless of the
epistemological implications.
In a way, a techno-optimist view of big data analytics makes it very difficult to
engage with the issue of privacy: if we speak of data and the patterns they
contain as something that is inherently objective and meaningful, out data sets
need to mirror social reality as closely as possible. However, in order to avoid
privacy breaches, we need to distort our data.
- This dilemma is at the root of the trade-off between privacy protection and
the validity of empirical inferences one can derive from a dataset.
Key issue: quality of big data insight and how that translates into quality of
decisions:
Techno-optimist narrative:
- Big data provides more information which means better insight and better
predictive capabilities, which then translates into better informed (and thus
generally better) policy decision.
Policy-pessimist narrative:
- On important quality dimensions big data is not better for policymaking
than traditional data. Politicians will always cherry-pick data that suits their
agenda – more data will diffuse the meaning of evidence based and result
in more political strategizing.
Key issue: speed of big data analysis and how that translates to speed of
decisions:
Techno-optimist narrative:
- Real-time data streams provide more up-to-date information faster than
currently available data, meaning that policy decisions can be made faster,
making policy more agile.
Policy-pessimist narrative:
- Decision-making will not adapt to the speed of data, as negotiations of the
data by humans is a crucial part of the process. Faster data is not available
for most policy questions.
Key issue: epistemology of big data analysis:
Techno-optimist narrative:
- A more inductive approach based on correlation and prediction rather than
causation as long as the dataset is of sufficient size.
Policy-pessimist narrative:
- No substantial change to scientific method, muting the effect big data
analytics will have as they are tailored for inductive exploration and not
deductive testing.
Key issue: connection between big data analysis and fundamental concerns
related to it:
Techno-optimist narrative:
, - Privacy and other fundamental issues with big data matter, but can be
overcome down the line with more advanced technological solutions or
policy interventions.
Policy-pessimist narrative:
- Privacy and other fundamental big data issues are crucial and cannot be
(fully) reconciled with big data analytics. To avoid them we should stop or
limit big data analytics.
Morrow, J., 2019: Why everyone should be data literate
- How do we take information and make a smart, informed decision. -> skill:
data-literacy
Data literacy: the ability to read, work with, analyse, and argue with data (it is not
data science)
1. Step 1: read the given information/data and comprehend it.
2. Step 2: being comfortable with information when it is presented to you
(work with data -> determine of it is a hoax)
3. Step 3: analyse data: so that we can take it in and find the insight to make
the right decision.
4. Step 4: arguing with data: 1. interrogate the information as it is presented
to you, 2. Ability to put a position forward and back it up with information.
How to become better at data-literacy? Two C’s:
1. Become curious
2. Creativity
Week 2: E-government: the impact of digital transformation
Dunleavy et al., 2006 + Meijer & Thaens, 2021 + Piperal, 2019
Dunleavy et al., 2006: New Public Management is Dead-long live Digital-
Era Governance
The New Public Management (NPM) wave in public sector organizational change
was founded on themes of disaggregation, competition, and incentivization.
Although its effects are still working through in countries new to NMP, this wave
has now largely stalled or been reversed in some key ‘leading-edge’ countries.
The overall movement incorporating these new shifts is toward ‘digital-era
governance’ (DEG), which involves reintegrating functions into the governmental
sphere, adopting holistic and needs-oriented structures, and progressing
digitalization of administrative processes. DEG offers a perhaps unique
opportunity to create self-sustaining change, in a broad range of closely
connected technological, organizational, cultural, and social effects.
Key parts of the NMP reform message have been reversed because they lead to
policy disaster, and other large parts are stalled.
There is a scattering of proposals for characterizing the post-NPM wave of
management changes that is currently underway. Many seem overly optimistic,
looking forward to banishing bureaucracy or achieving a post-bureaucratic
administration.
Highlighting the central importance of information technology (IT) based changes
in management systems and in methods of interacting with citizens and other
service-users in civil society in the underpinning and integrating of current
