Benefits, Adoption Barriers and

Myths of Open Data and Open

Government Presenter: Fatemeh Ahmadi Zeleti

E-Gov Unit

Based on: M. Janssen, Y. Charalabidis & A. Zuiderwijk (2012). Benefits,

Adoption Barriers and Myths of Open Data and Open Government.

Information Systems Management (ISM), vol. 29, no.4, pp. 258-268

May 11, 2016

Overview

Data is defined as non-privacy restricted and non-

confidential data which is produced by public money and

is made available without any restrictions on its usage and

distribution

Public bodies are the largest data creators and collectors

Opening of data reinforce existing structures instead of

changing them

Opening data leads to two assumptions about government Readiness of public agencies for an opening process

Government is to give up control

May 11, 2016

Problem and the Paper

Research on open data is limited

No paper (by 2012) focus on analyzing the benefits and

barriers of open data

This paper analyzes the benefits and barriers of open data by

synthesizing people’s experiences with open data

May 11, 2016

The paper

Open data benefits

Adoption barriers of open data

Myths of open data

Approach

Analyzing the literature

Literature was used as a background for organizing a group session

Session with 9 participants was held in June 2011

Findings from the session were refined based on the interviews with 14

key persons

The interviews were aimed at better understanding the nature of the

benefits and barriers and were used to determine the myths of open

data.

May 11, 2016

Potential Benefits

The benefits are clustered into 3 groups:

1) Political and social (public value: transparency and

accountability)*

2) Economic

3) Operational and technical

* Political and social benefits are viewed as the most important category and are the overarching arguments for stimulating open data

May 11, 2016

Potential Benefits – Category 1

Political and social More transparency

Democratic accountability

More participation and self-empowerment of citizens (users)

Creation of trust in government

Public engagement

Scrutinization of data

Equal access to data

New governmental services for citizens

Improvement of citizen services

Improvement of citizen satisfaction

Improvement of policy-making processes

More visibility for the data provider

Stimulation of knowledge developments

Creation of new insights in the public sector

New (innovative) social services May 11, 2016

Economic Economic growth and stimulation of competitiveness

Stimulation of innovation

Contribution toward the improvement of processes, products and/or

services

Development of new products and services

Use of the wisdom of the crowds: tapping into the intelligence of the

collective

Creation of a new sector adding value to the economy

Availability of information for investors and companies

Potential Benefits- Category 2

May 11, 2016

Operational and technical The ability to reuse data / not having to collect the same data again and

counteracting unnecessary duplication and associated costs (also by

other public institutions)

Optimization of administrative processes

Improvement of public policies

Access to external problem solving capacity

Fair decision-making by enabling comparison

Easier access to data and discovery of data

Creation of new data based on combining data

External quality checks of data (validation)

Sustainability of data (no data loss)

The ability to merge, integrate and mesh public and private data

Potential Benefits- Category 3

May 11, 2016

Some Insights from the Interviewees

“effective use of public sector data is vital to the growth of our

knowledge economy”

“if data is used for policy-making a high level of confidence in the

data and in the veracity of the interpretation is given. Therefore

policymakers should be prepared to share their data”

Most of the interviewees expect that open data can strengthen

accountability, build trust and improve citizen satisfaction

One of the main benefits of opening a system is the ability to tap

into the collective intelligence of the public. The key idea is that

under the right circumstances, groups can generate better

alternatives and make better decisions than even the smartest

people can do on their own

May 11, 2016

Barriers

Barriers are identified and categorized into the ff 6 levels:

1) Institutional level -> Data provider’s perspective

2) Task complexity of handling the data -> User’s

perspective

3) Use of open data and participation in the open data

process -> User’s perspective

4) Legislation -> Relevant for both providers and users

5) Information quality -> Relevant for both providers and

users

6) Technical -> Relevant for both providers and users

May 11, 2016

Barriers Category 1

Institutional Level Barriers

Emphasis of barriers and neglect of opportunities

Unclear trade-off between public values (transparency vs. privacy

values)

Risk-averse culture (no entrepreneurship culture)

No uniform policy for publicizing data

Making public only non-value-adding data

No resources with which to publicize data (especially small agencies)

Revenue system is based on creating income from data

Fostering local organizations’ interests at the expense of citizen

interests

No process for dealing with user input (no adequate feedback system)

Debatable quality of user input

May 11, 2016

Barriers Category 2 Task Complexity

Lack of ability to discover the appropriate data

No access to the original data (only processed data)

No explanation of the meaning of data

No information about the quality of the open data (see category “Information

Quality”)

Duplication of data, data available in various forms or before/after processing

resulting in discussions about what the source is

Difficulty in searching and browsing due to no index or other means to ensure

easy search for finding the right data

Even if data can be found, users might not be aware of its potential uses

Data formats and data sets are too complex to handle and use easily

No tooling support or help desk

Focus is on making use of single data sets, whereas the real value might come

from combining various data sets

Contradicting outcomes based on the use of the same data (right? wrong?)

Invalid conclusions May 11, 2016

Barriers Category 3

Use and participation No incentives for the users

Public organizations do not react to user input

Frustration at there being too many data initiatives

No time to delve into the details or no time at all

Having to pay a fee for the data

Registration required before being able to download the data

Unexpected escalated costs

No time to make use of the open data

Lack of knowledge to make use of or to make sense of data

Lack of the necessary capability to use the information

No statistical knowledge or understanding of the potential and limitations

of statistics

Thread of lawsuits or other violations

May 11, 2016

Barriers Category 4

Legislation Privacy violation

Security

No (generally accepted/understood) license for using data

Limited conditions for using data

Dispute and litigations (the process of taking legal action)

Prior written permission required to gain access to and reproduce data

Reuse of contracts/agreements

May 11, 2016

Barriers Category 5

Information Quality Lack of information (e.g. Time period in which the data was collected)

Lack of accuracy of the information

Incomplete information, only part of the total picture shown or only a

certain range

Obsolete and non-valid data

Unclear value: information may appear to be irrelevant when viewed in

isolation, but when linked and analyzed, it can result in new insights

Too much information to process and not sure what to look at

(Essential) Information is missing

Similar data stored in different systems yields different results

May 11, 2016

Barriers Category 6

Technical Data must be in a well-defined format that is easily accessible: while the

format of data is arbitrary, the format of data definitions needs to be

rigorously defined

No understanding of standards (country open data standards (e.g.

handbook) is not well documented)

No central portal or internationally/nationally accepted architecture

No support for making data available

Lack of understanding of metadata standards

No standard software for processing open data/ different approach exist

Legacy systems that complicate the publicizing of data

May 11, 2016

Some Insights from the Interviewees

“only data that is relatively safe is publicized and dropped in

the large pile … some of them even hope that the data will

not be discovered in this pile … there are no mechanisms

for gaining any feedback about its use”

“The reinforcement of the existing structure was

strengthened due to the current budget cuts.

Reinforcement also originate from the lack of systematic

analysis about which type of data should be allowed to be

publicized and what users expect from open data. This

provides arguments for not publishing data.”

“Use is limited to the happy few, those who are educated and

have time to explore new business opportunities”

May 11, 2016

What is Myth and Why to Formulate it

What: A myth is a traditional or legendary story without a determinable basis of

fact or evidence. The essence of a myth is that its existence is fictional

or unproven.

Why: Oftentimes a conceptually simplistic look at open data is taken (by different

stakeholders)

May 11, 2016

The paradox

Myths play an important role in policy-making – The one

sided advantage

“Our politicians are not aware of what is necessary. First they

push to publicize data, next, they complain about the

transparency and ask us to react to the questions asked …

transparency seems to be desirable only for others rather

than for their own activities”

May 11, 2016

The Five Myths of Open Data

Based on the interviewees the authors identify five myths

which proved not to be supported by evidence but were

often found at the heart of policy-making

1) The publicizing of data will automatically yield benefits

2) All information should be unrestrictedly publicized

3) It is a matter of simply publishing public data

4) Every constituent can make use of open data

5) Open data will result in open government

May 11, 2016

Myth 1 The publicizing of data will automatically yield benefits

Open data on its own has little value; the value is created by its

use. Therefore, supporting use should not be viewed as

secondary to publicizing data

The publicizing of data needs to be accompanied by an

infrastructure which is able to handle the data in an easy-to-use

way to lower the user threshold

It cannot be expected that the public has the same amount of

knowledge and capabilities as researchers do. Lowering the

knowledge level required for use is key to large-scale

dissemination

The creation of a huge pool of information might result in

difficulty in finding the right information. The more data there is,

the more difficult it is to analyze and draw meaningful

conclusions

May 11, 2016

Myth 2

All information should be unrestrictedly publicized

Data that can be traced back to the individual may not be

publicized due to privacy legislation

Limited resources for publishing, especially if the publishing of

open data does not bring benefits, can be another reason for not

opening data to the public

The complexity of data structures and difficulty to understand

the data might make it less attractive to publicize it

Law might prevent the publication of certain data

Data sets receive income for some public organizations and they

can ask for fee (Fee-base business model)

The arbitrary opening of some data might result into a biased

picture of the situation

May 11, 2016

Myth 3

It is a matter of simply publishing public data

This myth challenges that data can be made available without additional

activities

Not true myth -> Meta-data (publisher, authors, timeliness) is necessary

to overcome barriers like searching, interpretation and so on

Governments that publish data sets face substantial criticism such as

poor usability, lack of data feedback and improvement mechanisms, and

inadequate meta-data

=> Standardization of methods and the development of robust meta-data

can increase data access

**Data users want answers to their questions, services and other added

value that can be created from open data. Processing open data is

simply the hoop they have to jump through to gain answers to their

questions**

May 11, 2016

Myth 4

Every constituent can make use of open data

This myth assumes that open data users have the resources,

expertise and capabilities to make use of the data

Working with data requires capabilities (statistical techniques,

understanding underlying data and data relationships)

Only people with required capabilities is able to make sense out

of data

Data visualization and guide on how to use data can help non-

experienced users

Automating the annotation and classification of data can help in

simplifying searches , analyze relationship and etc.

May 11, 2016

Myth 5

Open data will result in open government

Myth suggest that disclosure of public data results in an

accountable and transparent government BUT this might have an

opposite effect AS

Experience and tacit knowledge might be lacking among those

who should be able to make sense of the open data

In addition, more information to public can result in less

understanding, more confusion and less trust

Therefore, it is easier to not publish data than to introduce

mechanisms to seek feedback and questions from the public

May 11, 2016

Conclusion

There are benefits and barriers in opening data and using data

More research into the specific benefits, barriers and value of open data is necessary.

The long list of benefits presented in this paper can be used in determining the

benefits on a case by case basis

Data are said to be publicized in name but they are still private in practice

Data providers must provide easy-to- understand data rather than publicizing data

Arguments in favor of open data are based on a rather simplistic and idealized view

Being able to use data and find patterns and trends remains a significant challenge

therefore there is a need for having good structures and support for handling data

The myths are:

Convincing data providers to open their data for public to use

While the user view is largely ignored and that use of data is not easy

May 11, 2016

Q&A