Is Open Data Ready for Use by Enterprises?

Learnings from Corporate Registers

Pavel Krasikov

, Timo Obrecht, Christine Legner

and Markus Eurich

Department of Business and Economics, University of Lausanne, Lausanne, Switzerland

Keywords: Open Data, Corporate Registers, Open Corporate Data, Usability, Data Quality, Open Data Assessment.

Abstract: Open data initiatives have long focused on motivating governmental bodies to open up their data. The

number of open datasets is growing steadily, but their adoption is still lagging behind. An increasing

number of studies assess open data portals and open data quality to shed light on open data’s current state.

Since prior research addressed neither datasets’ content, nor whether it met enterprises’ data needs, our

study aims to address this gap by investigating the extent to which open data is ready for use in the

enterprise context. We focus on open corporate registers as an important segment of open government data

with high relevance for enterprises. Our findings confirm that open datasets are heterogeneous in terms of

access, licensing, and content, which makes them difficult to use in a business context. Our content analysis

reveals that less than 50% of analyzed registers provide companies’ full legal addresses, while only 10%

note their contact information. We conclude that open data in corporate registers has limited use to its lack

of required attributes and relevant business concepts for typical use cases.

https://orcid.org/0000-0002-6427-7055

https://orcid.org/0000-0001-8891-3813

https://orcid.org/0000-0003-2850-4684

1 INTRODUCTION

Open data can be defined as “data that is freely

available, and can be used as well as republished by

everyone without restrictions from copyright or

patents” (Braunschweig, Eberius, Thiele, & Lehner,

2012). It is widely believed to have great business

potential and to be linked to high expectations

quantified to be as much as $5.4 trillion (Manyika et

al., 2013). Open data is therefore hailed as “a new

goldmine” of business opportunities waiting to be

unearthed (The Economist, 2013). According to

previous research, open data could allow multiple

industry sectors to benefit and prosper, including

transportation, consumer products, electricity, oil

and gas, healthcare, consumer finance, agriculture,

urban development, and the social sector (Davies,

Walker, Rubenstien, & Perini, 2019; Deloitte

Analytics, 2012; Dinter & Kollwitz, 2016; Manyika

et al., 2013; Publications Office of the EU, 2020).

Given governmental bodies’ enforcing of open

data provision and policies to improve its readiness

and consumption have advanced significantly over

the last few years (European Data Portal, 2018).

However, although the number of open datasets is

growing steadily, their adoption is lagging behind

(Publications Office of the EU, 2020). Application

developers were the main users of the first wave of

open data, achieving only modest success (Bizer,

Heath, & Berners-Lee, 2009). Currently, the second

wave is facilitating open data’s wider adoption and

using it to create added value (Puha, Rinciog, &

Posea, 2018). Enterprises should, as part of this

effort, use open data to improve their business

processes and, ultimately, unveil new business

opportunities. However, the severe challenges that

enterprises face in order to find, access, select, and,

finally, use open data make them reluctant to even

try (Davies et al., 2019; Oliveira, Oliveira, Lima, &

Lóscio, 2016). In fact, multiple studies have shown

that users find open data’s lack of transparency,

unknown quality, and unclear licensing unsettling

challenges (Janssen, Charalabidis, & Zuiderwijk,

2012; Martin, Foulonneau, Turki, & Ihadjadene,

2013).

Krasikov, P., Obrecht, T., Legner, C. and Eurich, M.

Is Open Data Ready for Use by Enterprises? Learnings from Corporate Registers.

DOI: 10.5220/0009875801090120

In Proceedings of the 9th International Conference on Data Science, Technology and Applications (DATA 2020), pages 109-120

ISBN: 978-989-758-440-4

109

Although an increasing number of studies have

explored assessment methods of open data, most of

them have primarily focused on the metadata level

and proposed generic quality metrics. Enterprises’

use of open data was not specifically investigated,

despite its significant business potential.

Consequently, this paper addresses the research

question: “Is open data ready for use by

enterprises?”

We answer this question by focusing on open

corporate data (OCD), which is an important

segment of open government data. OCD, which

provides transparent and interoperable data about

companies, has a high potential for reuse in a

business setting (Koznov et al., 2016; Varytimou,

Loutas, & Peristeras, 2015). We analyzed data from

20 open corporate registers (also known as business

registries): first, by assessing the provided metadata

and, second, by examining registered business

entities’ specific attributes. We compared the

datasets’ content with the typical attributes

(intelligence and analytics, business processes, data

management) that specific use cases require for an

understanding of readiness for use. Our findings

confirm that open datasets are heterogeneous in

terms of access, licensing, and content, which make

them difficult to use in a business context. In

addition, our study shows that less than 50% of

analyzed registers provide companies’ full legal

addresses, while only 10% note their contact

information. We conclude that open corporate

datasets have only limited use for typical use cases

due to their lack of relevant business concepts. Our

study thereby draws attention to the need for

domain-specific semantic models that make open

data more usable for enterprises.

The remainder of the paper is structured as

follows: In Section 2, we review relevant literature

on the barriers to open data adoption and assessment

techniques, which clarifies the research gap. In

Section 3, we explain the research methodology.

Section 4 presents the study results in detail. We

conclude with a discussion of our findings, the

study’s limitations, and provide an outlook on future

research.

2 LITERATURE REVIEW

Open data initiatives focused on motivating

governments to open their data for a long time

(Zuiderwijk, Janssen, Choenni, Meijer, & Alibaks,

2012). Different organizations have started making

their data available, but open data consumers still

experience difficulties with using open data.

Consequently, researchers studied the barriers to

open data adoption, proposing various assessment

methods, which we review in this section.

2.1 Adoption Barriers of Open Data

Table 1 summarizes prior studies on the barriers to

open data adoption. These studies integrate

academic literature and practical insights,

differentiating between open data consumption and

supply (marked with an X in Table 1). They reveal

that the barriers are related to the way open data is

provided and to its condition, which make it difficult

to use. Although the barriers are associated with

either consumption or supply, there is a strong

interdependency between the two: the way the data

is published impacts how it is used. (Zuiderwijk et

al., 2012, fig. 1).

Studies investigating open data provisioning

identify several common issues: the risk of excessive

costs, an unclear purpose, as well as litigation and

differing licensing standards and documentation

complicating open data suppliers’ release process

(Martin et al., 2013; Barry & Bannister, 2014;

Conradie & Choenni, 2014; Beno, Figl, Umbrich, &

Polleres, 2017). Studies addressing consumption

barriers tend to emphasize the user perspective,

claiming that these setbacks are not strictly technical

(Beno et al., 2017; Martin et al., 2013; Zuiderwijk et

al., 2012). Conversely, a lack of understanding of

the contents and insufficient domain knowledge

commonly hinder open data use (Beno et al., 2017;

Janssen et al., 2012; Zuiderwijk et al., 2012). In fact,

the absence of information describing an open

dataset is often associated with poor metadata

documentation (Zuiderwijk et al., 2012). The latter

generally refers to technical barriers, demonstrating

the interdependence of the impediments’

consumption and supply sides.

The existing studies suggest that challenges with

open data use relate mainly to three aspects: first,

there is a lack of transparency about datasets’

availability and their usefulness for the end user

(Janssen et al., 2012). Second, open datasets’

heterogeneity in terms of licensing conditions,

available formats, and access to information

complicates the integration efforts (Martin et al.,

2013). Third, the quality of open data remains

unknown and uncertain in terms of typical

assessment criteria (Zuiderwijk et al., 2012). Finally,

our review also points to a lack of research in the

enterprise context, as only two studies examined

enterprises as consumers of open data.

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Table 1: Overview of prior literature on adoption barriers of open data.

Source and topic Method Adoption barriers Open data

Consumption Supply

(Janssen et al., 2012)

Gap between the

benefits of and

barriers to open data

adoption

Group session (n=9), findings

were discussed during

interviews (n= 14)

6 categories: institutional, task

complexity, use and

participation, legislation,

information quality, technical.

Categories are exemplified by a

total of 57 exam

les of barriers

(Generic)

(Zuiderwijk et al.,

2012)

Open data users’

perspective on

identified

impediments

Literature review (n=37)

Interviews (n=6)

Workshops (n=4)

A total of 118 socio-technical

impediments in 3 categories:

data access, data use, and data

deposition.

10 sub-categories: availability

and access, findability, usability,

understandability, quality,

linking and combining data,

comparability and compatibility,

metadata, interaction with data

provider, and opening and

loadin

(Generic)

(Martin et al., 2013)

Risks for re-users of

public data differ from

those for open data

providers

Analysis of open data

platforms (n=3)

Typology of barriers comprising

7 categories: governance,

economic issues, licenses and

legal frameworks, data

characteristics, metadata, access,

and skills

(Business)

(Conradie & Choenni,

2014)

Release processes of

government open data

Participatory action research:

Exploratory workshop (n=5).

Questionnaire answered by a

consortium (n=14).

Questionnaire answered by

other civil servants (n=50).

In-depth interviews (n=18).

Workshop with data users

(n=8). Plenary session

discussion (n=21). Follow-up

meeting with decision makers

(n=2). Experiences with data

release

(

n=4

)

4 categories of barriers: fear of

false conclusions, financial

effects, opaque ownership and

unknown data locations, and

priority

(Barry & Bannister,

2014)

Implications of

opening up the data

Case studies (n=2), inductive

approach to the analysis of

collected data

6 types of barriers: economic,

technical, cultural, legal,

administrative, and task related.

A total of 20 barriers to open

data’s release

(Beno et al., 2017)

Practitioners using

and providing open

data in Austria

Literature review (n=17)

Survey (n=110)

3 major groups: user specific,

provider specific, and both users

and providers with a total of 54

arriers

(Enterprises,

Academia,

Public sector

)

2.2 Open Data Assessment

The barriers to open data adoption have motivated

researchers to investigate open data portals’

assessment, focusing specifically on the data quality.

Table 2 summarizes the ways prior studies assessed

open data and categorizes two crucial aspects: (1)

whether the analysis only considered the metadata or

the dataset content as well, and (2) the methods

used. This summary allows us to conclude that the

majority of the open data assessment studies focused

almost exclusively on the metadata quality. Only

three authors investigated the contents of the

underlying datasets. As mentioned by Vetrò et al.

Is Open Data Ready for Use by Enterprises? Learnings from Corporate Registers

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(2016), “poor data quality can be widespread, and

potentially hamper an efficient reuse of open data.”

Interestingly, the three studies propose generic

quality assessment methods according to typical data

quality dimensions, such as completeness, accuracy,

or timeliness, but do not consider specific data

requirements or the use contexts. This means that the

reviewed literature largely ignores the actual user's

perspective and the data domain knowledge, which

has found to be crucial for overcoming the barriers

(Section 2.1).

As a final point, open data’s

usefulness is only

partially addressed in the dataset assessment context.

To our knowledge, only Osagie et al. (2017)

specifically address the usability of open data

platforms’ features for specific use cases. In this

regard, by omitting the datasets’ contents, the

abovementioned assessment methods cannot address

the usability aspect and its relevance.

2.3 Research Gap

The existence of barriers in the open data landscape

shows a clear “lack of insight into the user’s

perspective” (Janssen et al., 2012). There is a need

to understand the particularities of open data access,

publishing, licensing, and content, as well as the

extent to which they meet the requirements for a

Table 2: Overview of open data assessment in literature.

Source Scope Method Results

(Bogdanović-Dinić,

Veljković, &

Stoimenov, 2014)

Metadata Case study: application of “data

openness” model to 7 open data portals

Data openness index score based

on eight open data principles

(Open Government Working

Group, 2007)

(Reiche, Höfig, &

Schieferdecker, 2014)

Metadata Case study: assessment of metadata

quality of 10 open government data

portals

Ranking of open data repositories

with the average score computed

by means of quality metrics

(Umbrich, Neumaier, &

Polleres, 2015)

Metadata Open data quality and monitoring

assessment framework

Analysis of 82 CKAN portals by

means of 6 quality dimensions

(Neumaier, Umbrich, &

Polleres, 2016)

Metadata Metadata quality assessment framework Assessment of 260 open data

portals to highlight common issues

(Vetrò et al., 2016) Metadata

and dataset

Quality framework supported by data

quality models from the literature

Assessment of 11 datasets’ quality

according to 6 dimensions and 14

metrics

(Máchová & Lněnička,

2017)

Metadata Benchmarking framework for evaluating

open data portals’ quality

Quality evaluation of 67 open data

portals according to 12 general

characteristics and 16 metrics

(Welle Donker & van

Loenen, 2017)

Metadata Holistic open data assessment framework

with 3 main levels: open data supply,

open data governance, and open data user

characteristics.

Assessment of 20 “most wanted”

datasets in Netherlands addressing

open data quality on 3 levels

(Osagie et al., 2017) Platform

features

Usability evaluation with ROUTE-TO-PA

and QUIN criteria

Scoring and testing of 4 functions

in 5 datasets according to12

usability criteria

(Bicevskis, Bicevska,

Nikiforova, & Oditis,

2018)

Dataset Three-part data quality model: definition

of a data object, data object quality

specifications, and implementation

Syntax analysis of data from 4

company registers for 11 attributes

(Kubler, Robert,

Neumaier, Umbrich, &

Le Traon, 2018)

Metadata Open data portal quality (ODPQ)

framework

Comparison of more than 250

open data portals according to 17

quality dimensions

(Stróżyna et al., 2018) Metadata Quality-based selection, assessment, and

retrieval method

Attribution of quality scores based

on “ranking type Delphi” and 6

quality dimensions to 59 data

sources

(Zhang, Indulska, &

Sadiq, 2019)

Metadata

and dataset

Design science research and a systematic

approach to repurposed datasets’ quality

Discovery of data quality

problems in 20 datasets using the

LANG approach and according to

10 dimensions

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specific usage scenario. Existing efforts regarding

open data’s use focus on open data platforms

(Osagie et al., 2017) or refer to open data supply and

its underlying technical impediments evoking users’

behavioral intentions (Weerakkody, Irani, Kapoor,

Sivarajah, & Dwivedi, 2017). Vetrò et al (2016)

emphasize that barriers are therefore mostly studied

on the platform level, rather than on the dataset

level.

Moreover, the literature does not specifically

cover open data’s use in the business context. Users’

perception of data availability (the way data is

proposed and can be consumed), generally covers

the usability concept’s technical part (Osagie et al.,

2017; Weerakkody et al., 2017), leaving a gap

between the dataset’s content and the user

perspective. Furthermore, Welle Donker et al (2017)

provided a more user-centric definition of open

data’s usability as “usable for the intended purpose

of the user.” In fact, being manageable is one of the

indicators that the authors introduce in the same

work, which implies that a “user should be able to

use it (open data) for the goal the user had in mind”

(Welle Donker & van Loenen, 2017).

The abovementioned gaps motivate our research

aimed at answering the question whether open data

is ready for use by enterprises.

3 RESEARCH METHODOLOGY

We address this research gap by focusing on open

data in a specific domain and use context. We

selected open corporate data (OCD), which is an

important segment of open government data and

has a confirmed reuse potential (Varytimou et al.,

2015). OCD can be defined as data on companies

that business registers, in keeping with local laws,

usually collect. The resulting data is not only

valuable for public authorities, but also for the

businesses and researchers.

We conducted three types of research activities

over a period of two years to understand how ready

open corporate data is for use: a literature analysis

to understand open data’s current state and its

adoption barriers; focus groups with practitioners

to identify the use context; the in-depth assessment

of open corporate sources and datasets.

Figure 1 summarizes the key phases of the

research process (the numbers refer to the

corresponding sections in this paper presenting the

results).

Figure 1: Research process.

Use Context: Open Data Domain and Use Cases

Focus groups (Bryman & Bell, 2007, p. 511;

Creswell, 2009, p. 181) were formed with

practitioners as a part of the broader analysis of open

data use cases and addressed how OCD can be used

in a business context. The focus group comprised

seven Swiss-based data management experts

representing transportation, consumption goods, and

telecommunication industries. All the participants

were knowledgeable about open data use cases and

had been involved in the generation and

documentation processes of OCD scenarios. The

focus group first met during a Web conference

during which it defined three high level use cases

based on a structured use case generation framework

(Krasikov, Harbich, Legner, & Eurich, 2019).

Afterwards, the focus group met physically for a

workshop that validated open corporate data use

cases. Additional individual sessions were

conducted with companies to refine the relevant use

cases and obtain further insights. These focus group

activities resulted in three core use case types and

relevant business concepts (Section 4.1).

Identification of Data Sources and Datasets

So far, 223 countries have recorded 721 official

corporate registers by following the global entity

identifier foundation (GLEIF, 2017) procedure,

which allows them to confirm their official status.

However, only a small number of these registers are

available as open data sources. For this paper, we

selected 20 corporate registers that official state

bodies provide and which are either available in full

open access form or as open access with registration

(Stróżyna et al., 2018). All of the registers are

recorded within GLEIF and therefore have an

assigned registry code. These registers cover open

data initiatives in the United States, Europe, and

other countries, but with different geographical

granularity. Although we only wanted to consider

registers that provide full access to the data (API or

bulk download), we realized, during the course of

Is Open Data Ready for Use by Enterprises? Learnings from Corporate Registers

113

the analysis, that some only allow restricted access

to the datasets, for example, the Indian, Danish,

Belgian, Swiss, and Austrian business registers.

Metadata

As outlined in Section 2.2, most of the open data

assessment methods focus on metadata. In fact, the

primary insights into whether the desired data is

usable or not are obtained through the metadata

published at the source. We relied on previous

literature (Table 1) when dealing with corporate

registers and collected five categories of open data

information: its identification, access, licensing,

publisher, and basic information about the

underlying datasets’ content. Two researchers

collected and reconciled the metadata of the selected

20 corporate registers (see Appendix B).

Content Assessment of Open Corporate Datasets

Following the research process, a comprehensive

content analysis of the corporate registers was

undertaken to assess its readiness for use. Two

researchers conducted a bottom-up analysis to

understand the similarities between the attributes

that the registers provide. Based on the focus group

participants’ input, we examined the corporate

registers to ascertain the presence of attributes

related to the use cases’ relevant business concepts.

Moreover, we took existing efforts regarding the

OCD semantics’ standardization into consideration

for this analysis. Section 4.1 elaborates these efforts.

The understanding we gained of the existing domain

ontologies and the content analysis of the relevant

business registers allowed us to derive 21 typically

used attributes (see Appendix A). We assessed all 20

registers on the basis of this attribute list.

4 RESULTS

This section compares the use context (4.1), i.e. the

relevant use cases for open corporate data that we

collected from practitioners, with the datasets that

the corporate registers provided (4.2) in terms of

metadata documentation (4.3) and its content (4.4).

4.1 Use Cases for Open Corporate

Data

There is very little academic literature on OCD,

which means that online sources are the main

providers of insights. The working sessions

described in Section 3 provided practitioners’

insights into how OCD can be used in the business

environment. These sessions identified, discussed,

and validated the following scenarios, as well as

providing a summary of the relevant business

concepts (see Table 3):

Intelligence and analytics: OCD can be used to

gain insights into customers, partners, and

competitors. Moreover, it is possible to identify a

particular enterprise with a unique identifier, which

helps to prevent confusion due to similar company

names. OCD can also support investigations into

corruption, abuse of power, and violations of cartel

laws (Varytimou et al., 2015).

Business processes: OCD can be used in

procurement processes to verify a given enterprise’s

shipping or billing addresses. In turn, this leads to

lower return rates and overall acceleration of

procurement activities. Additionally, OCD helps to

identify potential clients in particular industries and

to target marketing campaigns at them. In this case,

it is crucial to have up-to-date information about

their activities and their initial contact information.

Table 3: Summary of the use cases for open corporate data.

Use case type Relevant business concepts

1. Intelligence and analytics Identification: Company Name, Identifier

Organizational Information: Legal Form, Status, Date of Incorporation, Management

Information, Financial Information, Number of Employees

Address: Country, Post Code, Thoroughfare, Identifying Name

Organizational and Management Information, Financial Statement, Number of

Employees, Legal Form, Industry Classification Type, Incorporation Status

2. Business processes Billing / Shipping Address: Country, Administrative area, Administrative Area,

Locality, Post Code, Premise, Thoroughfare, Identifying Name

Identification: Company Name

Organizational Information: Status, Industry Classification

Contact: Website, Postal Delivery Point, Phone Number, E-mail

3. Data management Identification: Company Name, Identifier, Tax Number. (VAT)

Address: Country, Administrative area, Locality, Post Code, Premise, Thoroughfare

Organizational Information: Date of Incorporation, Status, Legal Form

Contact: Website, Postal Delivery Point, Phone Number, E-mail

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Data management: The maintenance of business

partner data within a company’s IT systems is one

example of an OCD application. OCD can help to

ensure the data quality by removing duplicates,

reconciling concepts representing the same real-

world object, enriching the data with new entries,

and ensuring its completeness and accuracy by

adding up-to-date information from authoritative

sources.

Each of these use cases can be related to business

concepts, for example, the billing details for

procurement process, which are similar to the

attributes usually found in corporate registers (see

Appendix A). It is interesting that “Address” is an

overarching concept in all the use cases, while other

concepts (identifica-tion numbers, organizational

information, and contact details) are only relevant for

selected use cases.

4.2 Identification of Data Sources

Corporate registers are usually assigned to a country

or an administrative area and cover local business

entities that need to undergo a local registration

procedure. Aggregated lists of existing company

registers are available online per country

(Wikipedia, 2019), although there is no assessment

process that confirms this sources’ accuracy. The

abovementioned GLEIF has an attribution procedure

by means of a legal entity identifier (LEI), and

maintains a catalogue with accredited official

business registers, which provides initial insights

into the available OCD (GLEIF, 2018).

Nevertheless, the register’s presence on this list does

not guarantee that the provided data is open.

Our analysis considered 20 sources, i.e. business

registers covering the United States, Europe, and

other countries, but with different geographical

granularity, as listed in Table 4.

4.3 Metadata Analysis

The analysis of the collected metadata provides first

insights into the sources (see Appendix B). The blue

color denotes registers with open access, whereas

those colored green refer to the ones with limited

access to the download or a restrictive license

policy. The registers present identification informa-

tion regarding the relevant countries and registry

codes, as well as the webpage to locate them.

Metadata regarding access revealed many

interesting insights. CSV, JSON, and XML file

formats, which are machine readable and suitable for

processing, are of the most frequently used formats

for downloadable business register files. Five

registers required a login procedure in order to

access the data, which also complicated the latter. In

terms of licensing, most registers operated under an

open license, a Creative Commons one or a national

equivalent, whereas six registers provided access to

the data without a license. Moreover, with the

exception of one, all of the registers offered a free

lookup service to query the data. Only 12 of the

business registers provided a publishing date, which

was after 2013. The data’s update frequency varied

from daily or weekly to monthly or even yearly.

Finally, these attributes’ importance should not be

underestimated (Kampars, Zdravkovic, Stirna, &

Grabis, 2020) as the enterprises’ specific needs drive

this (see Section 4.1).

Metadata regarding the content revealed an

important difference between the registers’ sizes,

which ranged from 75,985 to 11,100,000 entry

points. Their geographical coverage explains this, as

larger registers have a national level of granularity

(France, UK), while smaller ones cover states (US)

or administrative areas. Almost all the registers are

available in English, even though the country of

origin has a different national language or more than

one, which demonstrates the efforts taken to make

data available. On average, the registers provide 33

distinct attributes. While this information allows first

insights into the data, we, as shown in the following

section, conducted a thorough analysis of the

contents to specifically identify use cases.

Table 4: Analyzed corporate registers.

(1) Alaska Business Entity Register

(2) Canada Corporate Register

(3) Colorado Business Entity Register

(4) Florida Business Entity Register

(5) France Register of Companies

(6) Iowa Business Entity Register

(7) Ireland Companies Register

(8) Japanese National Tax Agency

(9) New York Business Entity

(10) Norway Register of Business

Enterprises

(11) Oregon Business Entity Register

(12) Washington Business Entity

(13) Wyoming Business Entity

(14) Companies House UK

(15) Australian Business Register

(16) Indian Business Register

(17) Danish Company Register

(18) KBO Central Belgium Company

Database

(19) Swiss UID Register

(20) Austrian Corporate Register

Is Open Data Ready for Use by Enterprises? Learnings from Corporate Registers

115

Figure 2: Visualization of content analysis of open corporate registers.

4.4 Dataset Content Analysis

We conducted a content analysis for the previously

identified registers with regards to the required

attributes (see Table 3). Figure 2 summarizes the

attribute’s presence in the dataset (with a green tick).

On average, 12 of the 20 identified attributes were

present; however, the French register shows 17

attributes present, followed by the Belgian and

Swiss registers. Interestingly the US state registers

do not provide the same attributes although they are

part of the same country.

Companies’ address information and their

identification concepts are present in the majority of

the assessed corporate registers. “Address” is one of

the central concepts for the considered use cases and

available in all of the registers, with an exception of

the “Premise” and “Identifying Name” attributes.

Only seven registers provide all the address-related

attributes, although the most evident attributes

(“Administrative Area,” “Locality,” and “Post

Code”) are all present.

The corporate registers present “Organizational

information” only infrequently, with “Contact”

details appearing least. The intelligence and

analytics use case suffers from a lack of

“Organizational information” attributes. Even

though a number of registers do contain

management and financial information, this is too

little to be useful. For instance, only the registers of

Denmark, Austria, and the state of Iowa provide the

full set of attributes in this category. Business

processes-related use cases, i.e. marketing

campaigns, suffer from a lack of contact

information, which is also relatively scarce in all of

the corporate registers. Data management use cases

aim at maintaining the most accurate version of the

data in the company’s internal systems;

consequently, “Address” and “Identification” play a

key role and are widely present in the registers.

5 CONCLUSION

Despite governments, NGOs, and companies’

enormous efforts to open their data and the open

data movement’s decade of evolution, the condition

and contents of the provided open data still do not

Attribute / Register

Alaska

Canada

Colorado

Florida

France

Iowa

Ireland

Japan

New York

Norway

Oregon

Washington

Wyoming

Australia

India

Denmark

Belgium

Switzerland

Austria

∑ (Attribute)

Company Name 20

I denti f i er 20

Tax

№

. (VAT)  5

Country    17

Administrative Area 20

Locality 20

Post Code 20

P remi s e     13

T horoughfare  19

Identifying Name    9

Legal Form  19

Type Classification (SIC)     11

Status    16

Date of I ncorporati on  18

Management Informati on  5

F i nanci al Informati on 4

Nr. of E mpl oy ees  3

Website  1

Postal Delivery Point     9

Phone Number  2

E-Mail  2

∑ (Register) 12141213171211101212 9 101413121316161312

IDA d d r essOrganizational infoContact

DATA 2020 - 9th International Conference on Data Science, Technology and Applications

116

meet expectations. Open data is generally still

assessed on the platform level and such assessments

are almost exclusively aimed at the metadata quality.

We aimed to address this gap by means of a use

case-driven analysis of open corporate registers,

which considered both the metadata and the content.

Our additional content analysis of 20 corporate

registers revealed that open corporate datasets have

limited use for typical use cases, because they do not

cover relevant business concepts. Legally required

information about companies, such as their

addresses and identification, is mostly available, but

not always complete, while many other attributes are

only partially available.

Our study contributes to the emerging stream of

research on the use of open data and addresses the

“lack of insight into the user’s perspective,” which

Janssen et al. (2012) mentioned. We propose a use

case-driven approach comprising four steps: (1) the

definition of the use context, (2) the identification of

the open data sources, (3) a metadata analysis, and

(4) a content analysis of the datasets. This approach

goes beyond the existing assessments of open data

quality by also integrating a content analysis. To the

best of our knowledge, the conducted analysis is one

of the first to provide insights into open data’s

readiness for use from an enterprise perspective.

A limitation of this work is that, given the total

number of existing business registers, the number of

analyzed sources does not allow us to make conclus-

ions about the domain as a whole. Nevertheless, the

selected registers represent countries considered as

advanced with regard to open data provision

(Publications Office of the EU, 2020). As mentioned,

we identified the use cases by means of a focus group,

but others could be potentially discovered.

An implication from our study is that we find that

the proposed open data assessment methods require

amendments. Domain- and use case-specific content

analysis need to complement these methods in order

to assess open data’s usability. Future research in this

direction could lead to the analysis being generalized

into a method. As a first step, we plan to integrate the

proposed approach further into the OCD domain by

means of existing assessment methods, which we

might generalize to other existing open domains by

following this work’s usability assessment. Further

links to open data quality should be explored with

regard to usability (Bicevskis et al., 2018; Vetrò et al.,

2016). In order to thoroughly address the data quality

aspects, future research could embed the assessment

techniques with metrics along the data quality

dimensions in the content analysis part (e.g.

timeliness, accuracy, and completeness).

Our study also underlines the need for domain

ontologies, such as the euBusinessGraph (2019)

common semantic model for company data, which

could be a basis to provide more consistent and

compatible open datasets across different open data

portals and providers.

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APPENDIX

Appendix A – Definition of Attributes

Source Information

Registry Code Unique identification of legal entities by GLEIF (2018)

Country Defines a country to which the register refers

Access

Webpage The webpage of the business register

Resource Format Describes the format of the published data, e.g. XML, JSON, CSV

Access Login Mentions whether access to the dataset requires an account

Free lookup Service Indicates whether the register has a free company lookup service

License License under which the data is provisioned

Publisher

Publisher Entity responsible for providing the data

Publishing Date Date when the register originally published the dataset

Update Cycle Describes the frequency of the data update in days.

Content

Resource Language Mentions the language(s) in which the data is published.

Geographic Coverage Defines the scope of the publishing institution, either on a state or national level.

# of Diverse Attributes Counts the different attributes that the register reports.

#of Records Estimate of the total number of entries in a register.

Content Information

Company Name Defines the entity’s name in a local language.

Identifier A unique identifier assigned to the relevant register.

Tax № (VAT) The tax number of the entity (VAT).

Address

Country A geopolitical area, typically a nation

Administrative Area A top-level geographical or political area division in a country.

Locality A more granular level of an administrative area’s geographical division.

Post Code A country-specific code for a certain address component.

Premise An area of land and its adjacent buildings.

Thoroughfare A form of the access route of the address: a street, road, avenue, etc.

Organizational information

Identifying Name A name assigned to an address, e.g. the legal representative.

Legal Form The type of entity with respect to the local corporate law.

Type Classification (SIC) Classification of entities and their respective industries.

Status The entity’s status, e.g. active, bankrupt.

Date of Incorporation Date of the entry in the register.

Management Information Information about the company’s organizational structure.

Financial Information Usually financial reports on corporate figures.

Number of Employees The entity’s number of employees.

Contact

Website The entity’s website.

Postal Delivery Point A single mailbox or other place at which postal mail is delivered.

Phone Number The entity’s phone number.

E-Mail The e-mail address of the entity.

Is Open Data Ready for Use by Enterprises? Learnings from Corporate Registers

119

Appendix B – Metadata Documentation

Alaska Business

Entity Regis ter

Canada Cor pora te

Regis try

Colorado

Business Entity

Regis ter

Florida Bus iness

Entity Regi s ter

France Register of

Companies

Iowa B us ines s

Entity Reg is ter

Ireland Companies

Regis ter

Japanese National

Tax Agency

New York

Business Entity

Regis ter

Norway Register

of Business

Enterprises

12345678910

Regis try Code

RA000594 RA000072 RA000599 RA000603 RA000189 RA000606 RA000402 RA000413 RA000628 RA000472

Country

United States Canada United States United States France United States Ireland Japan United States Norway

We bpag e

http://commerce.ala

ska.gov/CBP/DBD

ownloads/Corporat

ion s Download.CSV

https://open.canad

a.ca/data/en/datase

t/0032ce54-c5dd-

4b66-99a0-

320a7b5e99f2

https://data.colorad

o.gov/Business/Bu

siness-Entities-in-

Colorado /4ykn -

tg5h

http://dos.myflorid

a.com/sunbiz/other

-services/data-

downloads/corpora

te-data-file/

https://www.data.g

ouv.fr/en/datasets/

base-sirene-des-

entreprises-et-de-

leurs -

etablissements-

https://data.iowa.g

ov/Regulation/Acti

ve-Iowa-Business-

Entities/ez5t-3qay

https://services.cro

.ie/

http://www.houjin-

bangou.nta.go.jp/p

c/download/zenken

https://data.ny.gov

/Economic-

Development/Activ

e-Corporations-

Beginning-

1800/n 9v6-

http://data.brreg.no

/oppslag/enhetsreg

isteret/enheter.xhtm

Resource

For mat

CSV XML

CSV, RDF, RSS,

TSV, XML, REST

TXT CSV

CSV, RDF, RSS,

TSV, XML, SODA

API

REST API

XML, CSV

(Shift_JIS), CSV

(Unicode)

CSV, RDF, RSS,

TSV, XML

CSV, JSON, XML,

REST API

Access Login

no no no no no no no no no no

Free lookup

Service

available available available available not available available available available available available

License

Open Government

License

Open Government

License

N/A N/A Open Licens e V2.0

Creative Commons

Attribution 4.0

Open License Open License

Open Government

Lic en s e

Norwegian License

for Open

Government Data

(NLOD)

Publisher

State of Alaska

Department of

co mmerc e

Innovation,

Science and

Development

Canada

Colorado

Department of

State

Division of

Corporation Florida

National Institute

of Statistics and

Economic Studies

Secretary of State

Iowa

Companie s

Registration Office

Ireland

Finan cial Service

Agency

New York

Department of

State

The Central

Coordinating

Entities

Publis hing

Date

N/A February 18, 2014 M arch 19, 2014 N/A December 27, 2016 November 10, 2014 N/A N/A February 14, 2013 N/A

Update Cyc l e

N/A 7d 1d 1d 1d 30d N/A 30d 30d N/A

Resource

Language

English English, French English English French English English Japanese, English English Norwegian

Geog raphi c

Coverage

State National State State National State National National State National

# of Records

75,985 930,000 1,716,403 N/ A 11,100,000 230,117 N/A N/A 2,776,167 1,103,302

#of Diverse

Attributes

35 25 35 45 118 19 18 19 30 42

IDContent Publisher Access

Oregon Business

Entity Regis ter

Washington

Business Entity

Regis ter

Wyoming

Business Entity

Regis ter

Companies Hous e

Aus tralian

Business Register

Indi an Bus i ne s s

Regis ter

Danish Company

Regis ter (CVR)

KB O Ce ntr al

Belgium Company

Database

Swiss UID-

Regis ter

Aus trian

Corporate

Regis ter

11 12 13 14 15 16 17 18 19 20

Regis try Code

RA000631 RA000641 RA000644 RA000585 RA000013 RA000394 RA000170 RA000025 RA000548 RA000017

Country

United States United States United States United Kingdom Australia India Denmark Belgium Switzerland Austria

We bpag e

http://sos.oregon.g

ov/business/Pages

/temp-business-

search.aspx

https://www.s os .w

a.gov/corps/alldata

.as px

https://wyobiz.wy.

gov/business/data

base.aspx

https://developer.c

ompanieshouse.go

v.uk/api/docs/

https://data.gov.au

/data/dataset/abn-

bulk-extract

https://data.gov.in/

catalog/company-

mas t er-d at a

http://datahub.virk.

dk/dataset/system-

til-system-adgang-

til-cvr-data

http://kbopub.econ

omie.fgov.be/kbop

ub/zoeknummerfor

m.h tml

https:// www.uid.ad

min.ch/Search.aspx

https://www.jus tiz.

gv.at/web2013/html

/defau lt/2c94848523

08c2a601240b693e1

c0860.de.h tml

Resource

For mat

CSV, RDF, RSS,

JSON, XML,

SODA API

XML, JSON, Text CSV CSV, REST XML, SOAP API CSV REST API PDF WebGUI PDF

Access Login

no no no no no yes yes yes no yes

Free lookup

Service

available available available available available not available available available available available

License

N/A N/A N/A

Free, Open

Government

License v3.0

Creative Commons

Attribution 3.0

Au s tralia

National Data

Sharing and

Accessibility

Po licy (NDSAP)

India

N/A

restricted to

queries

res tricted to

queries

restricted access

Publisher

Secretary of State

Oregon

Secretary of State

Washington

Secretary pf State

Wyoming

Companies House

Australian

Bus ines s Regis ter

Ministry of

Corporate Affairs

India

Danish Business

Authority

Ministry of

Economy Belgium

Swiss Federal

Statistical Office

Federal Minis try of

Justice Austria

Publis hing

Date

May 19, 2016 N/A March 19, 2014 December 11, 2013 September 5, 2014 N/A June 10, 2015 N/A December 11, 2013 Jan uary 10, 2014

Update Cyc l e

7d 1d N/A 7d 1d 365d 1d 7d 1d N/A

Resource

Language

English English English English English English

Danish, English,

Kalaallisut

English, French,

Dutch, German

English, French,

Italian, German

Deuts ch

Geog raphi c

Coverage

State State State National National State National National National National

# of Records

442,012 1,080,251 208,723 10,216,253 18,000,000 N/ A N/A 1,300,000 1,716,662 570`000

#of Diverse

Attributes

17 16 63 55 22 17 35 22 25 16

IDPublisherContent Access

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