Beyond Data Quality: Data Excellence Challenges from an Enterprise,

Research and City Perspective

Johannes Sautter

, Rebecca Litauer

, Rudolf Fischer

, Tina Klages

, Andrea Wuchner

Elena Müller

, Gretel Schaj

, Ekaterina Dobrokhotova

, Patrick Drews

and Stefan Riess

Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, 70569 Stuttgart, Germany

Fraunhofer Information Centre for Planning and Building IRB, Nobelstr. 12, 70569 Stuttgart, Germany

KPMG AG Wirtschaftsprüfungsgesellschaft, Barbarossaplatz 1A, 50674 Köln, Germany

Keywords:

Data Excellence, Data Quality, Operational Excellence, Compliance, Data Governance.

Abstract:

Researchers and practitioners widely agree on data quality as one of the major goals of data management.

However, data management departments in enterprises and organisations increasingly realise needs for data

availability, compliance, operational excellence with regard to the domain and other data-challenges. In raised

case studies in the enterprise, research and city domain, challenges regarding data availability, operational

integration, compliance and quality of data management processes are analysed. Based on the concept of

data quality, this paper argues for a similar concept with a broader scope for assessing an organisation’s data

suitability. Based on literature and case studies this paper proposes a deﬁnition of the term data excellence as

the capability of an organisation to reach its operational goals by ensuring the availability and integration of

suitable, transparent and compliant high quality data.

1 INTRODUCTION

In recent years, the concept of data quality (DQ) has

become a crucial goal for data and digitalisation ex-

perts in research as well as practice. DQ, deﬁned as

ﬁtness for use by data consumers (Wang and Strong,

1996), is the major goal organisations pursue when

launching DQ initiatives and establishing data mana-

gement departments. However, insufﬁcient DQ is not

the only shortcoming they deal with:

(1) Operational excellence issues, deﬁned as is-

sues hindering the "execution of the business strategy

more consistently and reliably than the competition"

(Soto, 2013), often arise.

(2) Regarding legal issues, data-related non-

compliance e.g. to the EU General Data Protection

Regulation (European Comission, 2016) or to Basel

III

for banks can lead to signiﬁcant ﬁnancial penal-

ties or even cause criminal liability.

(3) Insufﬁcient process quality issues are not di-

rectly measured by DQ, but have an indirect impact

e.g. on the DQ dimension believability (Piro, 2014).

There are various organisations aiming to shape

Recommendations on banking laws and regulations issued

by the Basel Committee on Banking Supervision

their future using existing or newly collected data. In

the digital economy, the paradigm industry 4.0 draws

a vision of fully wired shop ﬂoors producing intelli-

gent products. Experts recommend adequate DQ, po-

licies, culture change and a single source information

system architecture to enterprises (Schuh et al., 2017).

In research, Open Science is a basic principle that tar-

gets maximum access to scientiﬁc knowledge for re-

search, society and economy. Smart City projects aim

at creating or improving new data-based services for

citizens (Saujot and Erard, 2015).

When discussing organisational data challenges,

some essential deﬁnitions are needed. Master data are

the fundamental data of an organisation with a low

change-frequency (Otto et al., 2011). Metadata are

information on data which can be subdivided in three

subcategories: (1) descriptive metadata for identiﬁ-

cation purposes, (2) structural metadata on structure,

attributes and versioning as well as (3) administrative

metadata for methodological and technical aspects re-

lated to data creation as well as access rights (Zeng,

2004).

If an organisation strives to elaborate its ability of

dealing with data, it needs to hold and manage it in

a structured way. As data has been turned into value

Sautter, J., Litauer, R., Fischer, R., Klages, T., Wuchner, A., Müller, E., Schaj, G., Dobrokhotova, E., Drews, P. and Riess, S.

Beyond Data Quality: Data Excellence Challenges from an Enterprise, Research and City Perspective.

DOI: 10.5220/0006912902450252

In Proceedings of the 7th International Conference on Data Science, Technology and Applications (DATA 2018), pages 245-252

ISBN: 978-989-758-318-6

245

for particular resorts or departments, it should be in-

cluded as a new data domain

. However, employees

often think and work in their resort silos and use inde-

pendent non-integrated data sources. From a solution

perspective, data management concepts and methods

address such issues and propose to implement data

cleansing (Maletic and Marcus, 2005), master data

management (Otto et al., 2011; Scheuch et al., 2012),

data quality management (Otto and Österle, 2016;

Morbey, 2011) as well as data governance techniques

(Otto et al., 2011; Otto and Österle, 2016). However,

from a problem perspective, no framework is able to

explain or measure existing challenges beyond DQ.

This paper examines organisational data excel-

lence by means of the very different organisation ty-

pes enterprise, research institute and city adminis-

tration. The following section initially explains our

mixed methods approach, which is then followed by

a brief literature review. The section on domain cases

provides more thorough insights in enterprises, rese-

arch institutes and cities facing data challenges. Be-

fore drawing conclusions and attempting an outlook,

the subsequent analysis derives a deeper understan-

ding of data excellence and provides a preliminary

deﬁnition.

2 METHOD

As a basis for this paper, we conducted a compre-

hensive literature review which revealed the research

gap of few existing frameworks for analysing and ca-

tegorising organisational data challenges, especially

for non-DQ issues. Four different challenge dimen-

sions related to data were identiﬁed from literature,

own project experience and case study results:

• Operational excellence (including internal com-

pliance and standards)

• Legal challenges (obligations anchored within

laws, external compliance)

• Data management process quality (data manage-

ment maturity)

• Data quality (ﬁtness for use by data consumers)

Further, we conducted a cross project analysis regar-

ding data challenges relying on selected case studies

available at our institute. In order to cover organisa-

tions with diverse purposes, we were able to group

the case studies according to their characteristics into

three domain cases: enterprises, research institutes

and cities.

Examples for data domains are product data (enterprise),

interview data (research) or ground water level data (city).

(1) In the context of enterprises, case studies of

data governance projects and related triggering chal-

lenges within four DAX 30-enterprises

were obser-

ved. In addition to four semi-structured interviews

with external data governance consultants, project ex-

perience lead to the presented results.

(2) For research institutes, a single case study

covers the status quo in an institute of the German

Fraunhofer Gesellschaft with about 600 employees.

By means of 19 half-standardized interviews with le-

ading personal, scientiﬁc employees and service team

staff, we were able to gain insights into current techni-

ques of handling research data.

(3) Major problems in city departments were iden-

tiﬁed in a workshop with 15 scientiﬁc employees wor-

king in various research and consultancy projects.

Another workshop with ten international strategic of-

ﬁcials and smart city leaders from European cities and

companies conﬁrmed and enhanced our information

collection.

Next, we assigned all empirically identiﬁed chal-

lenges to one of these categories and enhanced them

with further empirical ﬁndings if applicable (cf. ta-

bles 1–3 in chapter 4). Then we extracted main chal-

lenges from one or more empirical issue. Next, we

derived aspects in other dimensions if applicable, fol-

lowing own data management experience. Finally, in

a cross-domain analysis empirical issues and derived

aspects were clustered and assigned to the 4 challenge

dimensions. All items then were added to a bullet list,

tagged with the domain case they stem from and en-

hanced with sources from the literature review conﬁr-

ming the observed issue. All challenges considered as

strongly evident in all three domain cases were added

to the ﬁnal bullet list presented in section 5.

3 RELATED WORK

Wang et al. (1998) describe DQ as "information deli-

vered [as] a total product: it includes all the attributes

that in combination meet the information consumer’s

expectation" (Wang, 1998). Others deﬁne DQ as the

“suitability to fulﬁl determined requirements" (Paska-

leva et al., 2017) or as “a measure for the suitability of

data for certain requirements in the business proces-

ses, where it is used" (Otto and Österle, 2016). Ho-

wever, few scientiﬁc approaches exist describing furt-

her organisational data characteristics beyond quality

such as compliance, availibility for operational excel-

lence and process quality.

30 major German companies listed in the Deutscher Ak-

tienindex (German stock index)

DATA 2018 - 7th International Conference on Data Science, Technology and Applications

246

Pentek et al. (2017) suggest data excellence as

"the impact of data management on the data itself,

ﬁrst and foremost with regard to data quality [...],

but also with regard to additional data related aspects,

such as data compliance, data security and privacy"

(Pentek et al., 2017). Although data governance is

already identiﬁed as a possible solution for tackling

data excellence challenges, many approaches avoid

the initial step of identifying fundamental problems

and providing a classiﬁcation framework. Otto provi-

des a literature-based overview on goals of data go-

vernance. He identiﬁes compliance with legal requi-

rements as the most frequent trigger (Otto, 2011).

The term DQ is furthermore widely understood as

a concept containing several dimensions. Morbey dis-

tinguishes the non-machine measurable criteria retrie-

vability and normative consistency from the machine-

measurable criteria horizontal completeness, syntacti-

cal correctness, consistency, accuracy, freedom of re-

petition, integrity and vertical completeness (Morbey,

2011, p. 26–27). According to the most common ap-

proach there are 15 DQ criteria, divided into the four

categories Intrinsic DQ, Contextual DQ, Representa-

tional DQ and Accessibility DQ (Wang and Strong,

1996).

According to Piro et al. (2014), most of the afore-

mentioned 15 dimensions by Wang and Strong (1996)

are objectively valuable by direct measurement or ob-

jective checklists. The dimension believability for in-

stance is objectively valuable as it is of higher va-

lue when an organizational data management process

is established (Piro, 2014). Thus, data management

process quality is a constituent of DQ but formula-

tes different requirements. High process quality the-

reby has a direct impact on DQ (Glowalla and Suny-

aev, 2013). For the measurement of data manage-

ment process quality maturity models can by applied.

They mostly provide a quantitative maturity index in

combination with expert recommendations on organi-

sational steps for achieving the next respective level

(Mosley, 2008; Otto and Österle, 2016; Pentek et al.,

2017).

Turning to other data challenges, Morbey (2011)

identiﬁes data owners, and not the DQ team, in charge

of content accuracy investigations (Morbey, 2011, p.

28–30). Also, not all domain needs regarding data

are transferable to computer measurable criteria (Piro,

2014).

The concept of operational excellence, understood

as "the consequence of an enterprise-wide practice of

ideal behaviours based on the correct principles" (Ru-

sev and Salonitis, 2016), is a common framework for

ideal business performance across all domains. Com-

pliance requires the observance of "rules and regulati-

ons imposed by any regulatory bodies to which a ﬁrm

is subject" (Edwards and Wolfe, 2005). For organiza-

tions, it embodies the inﬂuence of the law in opera-

tional activity and includes internal business policies

designed to counteract against possible quality losses

and to achieve operational excellence. With an ideal

compliance management function, whenever a new

regulatory requirement is enacted, an enterprise is ca-

pable of predicting its impact on operational proces-

ses. The concept of compliance management consists

of the functions risk minimization, harm reduction,

liability obligation, and corporate efﬁciency increase

(Wecker and Ohl, 2013).

The aspect of long-term archiving is becoming

more and more important, as "historical" media from

the beginning of the digital age are not easily readable

any more (Ferle and Spath, 2012).

4 DOMAIN CASES

The following subsections present domain-speciﬁc

thematic introductions describing the organisation’s

characteristics and purpose, what operational excel-

lence means to them as well as current developments

and legal requirements. Empirical results (highlighted

in grey) and derived data management implications

within the previously introduced challenge dimensi-

ons are presented in table 1–3.

4.1 Enterprises

In contrary to research institutes and cities, enterpri-

ses stand out due to their organisation type. While all

three consist of several different departments, enter-

prises nevertheless are viewed as more homogeneous

and act in accordance to the joint objective of provi-

ding services, infrastructure or products.

In today’s information society, high quality data

are the most important raw materials for operational

excellence and thus the economic success of an enter-

prise. The volume of data which permanently increa-

ses triggers completely new challenges among enter-

prises. While so far, they have only focused on efﬁ-

ciency and ability to compete, today and in the future,

networking and collaboration of enterprises and orga-

nisations are of greater importance also with regard to

economic proﬁt.

With the fourth industrial revolution, corporate

and manufacturing decisions will be increasingly ba-

sed on results from data analytics. Industry 4.0 ex-

perts recommend data governance structures in order

to overcome management, compliance as well as ope-

rational challenges (Schuh et al., 2017).

Beyond Data Quality: Data Excellence Challenges from an Enterprise, Research and City Perspective

247

The empirical results of four case studies descri-

bed in table 1 were the main causes for an implemen-

tation of data governance organisations in each of the

globally operating DAX 30 companies in the automo-

tive, bank, chemistry and energy sector. Analysed ca-

ses may be a good representation for the situation of

large corporations. However, they do not reﬂect small

and medium enterprises playing a key part in many

economies.

Speciﬁc legal regulations for enterprises demand

contract transparency as well as explicitly data gover-

nance, IT infrastructure and risk data reports in order

to ensure an accurate risk management. There was

only one case study in which other then legal challen-

ges were the main trigger for subsequently realizing

a data governance function. Double data entries and

inaccurate data as well as inefﬁcient steering and re-

gulatory risks were further main challenges.

4.2 Research Institutes

Departments and researchers within research institu-

tes often complete their tasks independently. For the

single researcher, freedom of action is crucial.

Digitalisation and globalisation lead to far-

reaching changes in the ﬁeld of science. Currently, a

paradigm shift towards Open Science is in progress,

representing a new approach based on cooperative

work (European Commission, 2016). In institutes,

currently often cross-department collaboration plays

a minor role, as is not necessary for fulﬁlling opera-

tional project objectives. In future, operational excel-

lence will imply the digital interchange of data, ideas

and results. For reaching out the data’s full economic

potential, research data have to be managed and des-

cribed in ways that make them FAIR (ﬁndable, acces-

sible, interoperable and reusable) (Wilkinson and ot-

her, 2016).

Due to the wide range of discipline-speciﬁc rese-

arch data and corresponding requirements for metho-

dology, research design and interpretation, it is not

possible to deﬁne universal criteria for operational ex-

cellence. The subject communities themselves must

negotiate whether criteria are fulﬁlled or not (Kind-

ling, 2013). As established for scientiﬁc publications,

peer-review processes as well as a professional cita-

tion practice based on citation indexes could evolve

for data, as soon as detailed documentation and good

practice collection and archiving methods exist as pre-

Table 1: Enterprise Data Excellence Challenges ( empirics and theoretical derivations).

Main Challenges Operational

Excellence Challenges

Legal Challenges Data Management

Process Challenges

Data Quality Chal-

lenges

Unreliable

contract data

Lack in transparency

whether contract

contents have been

entered and approved

correctly (automotive

OEM

procurement)

AO, HGB, IFRS

2015 and 2016

demand for contract

transparency

Unclear

responsibilities, no

clear contract master

data creation

processes

Inconsistent contract

details (especially be-

lievability, complete-

ness, concise repre-

sentation)

No reliable risk

management

No identiﬁcation of

business risks from

bank data

BCBS 239

iii

and

MaRisk

demand

data governance &

IT infrastructure,

aggregation and

report of risk data

Unclear and

unreliable data

management

processes (bank)

Low data quality in all

dimensions

Insufﬁcient

business

performance

Insufﬁcient

performance in

procurement and sales

Dangerous goods

remain at customs

ofﬁce

No data governance

as basis for clear data

ownership

No reliable supplier

and customer master

data (chemical com-

pany)

Regulatory risks Inefﬁcient steering,

risks for inaccurate

cable excavations,

customer complaints

(grid operator)

Regulatory risks

regarding network

performance and

maintenance

No clear

responsibilities

Double data entries,

accuracy

Original equipment manufacturer

German tax code (AO §97 (1), §147), German commercial code (HGB §238, §242),

Int. Financial Reporting Standards 2015 and 2016

iii

Basel Committee on Banking Supervision’s standard number 239

Minimum requirements for risk management by the German Federal Financial Supervisory Authority sources

German

Gefahrgutbeförderungsgesetz (Hazardous Goods Transportation Act)

DATA 2018 - 7th International Conference on Data Science, Technology and Applications

248

requisites (OECD, 2007). Both, the German Rese-

arch Foundation (DFG) (DFG, 2015) and the OECD

(OECD, 2007) consider institutions and research as-

sociations responsible for deﬁning professional stan-

dards for the management of research data.

Since the beginning of the European Union’s Re-

search Programme H2020 in 2014, beneﬁciaries are

obligated to make their research data available unless

contrary to privacy, security or exploitation interests

(European Comission, 2016).

The results presented in table 2 stem from an in-

stitute of the Fraunhofer Gesellschaft for applied re-

search where scientists conduct research not only in

projects commissioned e.g. by the government but

also by clients from the industrial sector. The institute

is quite heterogeneous regarding research ﬁelds and

disciplines of employees. Therefore, the case may be

a good representation for the Fraunhofer Gesellschaft

as a whole (69 institutes, 25.000 employees). Ho-

wever, the widespread national and international re-

search landscape is not reﬂected.

When taking the previously introduced new way

of collaborative research as a measure of success, the

illustrated main challenges internal exchange, data

standards, awareness of legal requirements, shortage

of incentives and methodological potentials arise. As

e.g. funding bodies just begin to demand open data re-

gulations from their beneﬁciaries, there is some time

to go until the digitalisation and open data age rea-

ches research practice. On the long term, research

landscape can learn from enterprises that implement

a higher maturity in data management.

4.3 Cities

Within city administration departments tasks are also

completed independently. However, staff follows

strict governmental guidelines. A city authority‘s pur-

pose is to offer services to citizens or other adminis-

trative bodies. In contrary to enterprises, municipali-

ties meet the structure of not a single but a conglome-

rate of organisations. This is based on the bureaucra-

tic model (Weber and Weber, 1980) that ensures con-

trol and regulation of governmental bodies in order to

Table 2: Data Excellence Challenges at a Research Institute ( empirics and theoretical derivations).

Main Challenges Operational

Excellence

Challenges

Legal Challenges Data Management

Process Challenges

Data Quality Chal-

lenges

Internal

competition

Not existing

exchange post-usage

hinders research

excellence

– Competitive thinking

between scientists

Lack of accessibility

for excellent research

No internal

exchange

No efﬁcient

collaboration possible

– Lack of

communication

between scientists

within research ﬁeld

Lack of representati-

onal data quality and

accessibility data qua-

lity

Effort-beneﬁt

balance

Value for effort in

metadata structure is

not seen

– Easy and low-effort

processes for metadata

standards are missing

Lack of representatio-

nal and intrinsic data

quality

Inattention on

duties to funder

No provision of data

to research

community

Lack of knowledge

on funder

compliance

Not sufﬁcient

knowledge

management

–

No process

standards

Scientists collect their

data in different ways

– Lack of tools and

processes guaranteeing

more standardized

collection of data

Insufﬁcient standardi-

zation of data and

meta data (representa-

tional data quality)

No data standards There is no minimum

standard for metadata

and data storage

Ensure that institute

/community standard

complies to legal

requirements

Lack of process for

agreement of scientists

and metadata standard

coordination

Lack of representatio-

nal consistency

Shortage of

scientist

development

Little methodological

expertise

– Provision of little

methodological support

in form of

workshops/consultancy

services

Lack of data quality in

all dimensions

Beyond Data Quality: Data Excellence Challenges from an Enterprise, Research and City Perspective

249

protect citizens.

The digital transformation at city administrations

for a long time foremost took place in e-government

topics and especially in Germany lacked of essential

progress (Akkaya et al., 2011). Due to the increa-

sing implementation of smart city projects, city go-

vernments are now facing the challenge of managing

new and large amounts of data. In addition, in the

smart city context, the sources of data are varied and

owned by different stakeholders (Saujot and Erard,

2015). This challenges the administration as a whole

as the usage of data in order to support city duties tou-

ches many different aspects. Operational excellence

means providing services to citizens and make well-

informed decisions. Currently the role of data in order

to fulﬁl these goals increases as new available data al-

lows for better performance. On the one hand, there

are standards allowing different administrative bodies

and different cities to exchange information (KoSIT,

2018). On the other hand, standards for the imple-

mentation of smart city solutions exist (DIN, 2017).

Beyond that, there are recommendations on how to

successfully carry out the transition towards digitali-

sation (BBSR, 2017).

However, "at the moment no [established] stan-

dard for administrative structures exists [..]. Many

municipalities have troubles handling overlapping re-

sponsibilities because of a predominant and outdated

silo mentality" (Pfau-Weller and Radecki, 2018). Re-

garding the availability of data, the European Com-

mission requests the reuse of public sector informa-

tion and the opening of governmental data (European

Comission, 2003).

The problems in city departments summarized in

table 3 stem from a workshops with scientiﬁc employ-

ees and a workshop with strategic smart city leaders.

The cases reﬂect the situation for European and inter-

national medium-size and big cities.

When taking a cross-city view, a lack of overview

over existing data is a critical data challenge. The

usage of different data types, standards, units or met-

hods creates problems as well as the addition of smart

city data to existing administrational data which ne-

cessitates cross-department data analysis and struc-

tured data management of the few valuable data set.

Cities may also learn from enterprises regarding data

management maturity.

Table 3: City Data Excellence Challenges ( empirics and theoretical derivations).

Main Challenges Operational

Excellence

Challenges

Legal Challenges Data Management

Process Challenges

Data Quality Chal-

lenges

No Data

Distribution

Different responsible

resorts (E.g. City

Planning: Building of

nature-based corridor)

Strict legal Limits for

data exchange

between departments

No central data access,

no single data source

(e.g. water pipe hinders

tree planting)

Different data sour-

ces, no overview

over existing data

Inattention of

regulations

– No implementation of

regulations

– –

No consistent

handling of data

No cross-resort data

analysis (use and mix

existing data for better

performance)

Heterogeneous usage

regulations dependent

on ownership and

licensing

Effortful extraction of

information, Distinct

processes dependent on

ownership

No concise repre-

sentation and no

accessability

Shortage of

methodical

knowledge

Unclear deﬁnition of

methods (indicators,

workshops)

– Unclear standards and

processes,

uncoordinated

collection of data

Data is wrong

(Accuracy)

Old data Data not operationally

usable e.g. for safety

and security issues

– Irregular or

non-structured data

collection

Data is out-dated

(Timeliness)

No data standards Extra data processing

effort (e.g. increase

urban climate

resilience)

– No standard format

deﬁned or practically

used

Different data for-

mat dependent on

source and supplier

(interpretability)

No process

standards

Comparison of

mobility data of

different cities

– Different standards,

units, or methods in

different cities

No concise repre-

sentation, no inter-

pretability

DATA 2018 - 7th International Conference on Data Science, Technology and Applications

250

5 DATA EXCELLENCE

As different as the three analysed organization types

may seem, they all share common data challenges that

stand in the triangle of tension between strived ope-

rational excellence, restricting legal frameworks and

enabling data management. Next to the data’s quality,

its compliance, organisational availability and opera-

tional integration is crucial. Also including non-DQ

aspects in problem analysis is currently hardly sup-

ported by scientiﬁc frameworks. However, it is ne-

cessary as a crucial supplement to data management

and data governance concepts addressing the solution

view.

Some of the identiﬁed organisational challenges

in the domain cases were clearly assignable to DQ.

The other revealed challenges were not clearly related

to previously described concepts. When aggregating

challenges evident across all domain cases, the follo-

wing major constituents of data excellence occur:

• Operational excellence challenges

– Operational efﬁciency (Otto, 2011)

– Exchange and collaboration

– Data availability (Panian, 2010)

– Operational integration and interoperability

(Otto, 2011)

• Legal challenges

– Operational legal requirements (Otto, 2011)

– Awareness of regulations

• Data management process quality challenges

– Clear responsibilities, processes and guidelines

– Data transparency and auditability (Panian,

2010)

– Central data acces

• Data quality challenges (Wang, 1998)

– Intrinsic data quality

– Accessibility

– Contextual data quality

– Representational data quality

Following the concept of operational excellence

(Soto, 2013), we deﬁne data excellence as capability

of an organisation to execute its strategy consistently

and reliably with a suitable, transparent and compli-

ant availability and integration of high quality data.

The concepts compliance and operational excellence

hereby are not part of data excellence, but reach its

borders. In contrary, we regard data management and

data governance as a possible solution to data excel-

lence challenges.

6 CONCLUSION AND OUTLOOK

Regardless if deciders in organisations grasp for pro-

ﬁt, scientiﬁc excellence or citizen’s welfare, they all

need to take new information from internal and ex-

ternal available data sources into account. Data ex-

cellence challenges such as data availability, operati-

onal integration, data transparency and awareness of

regulations from an enterprise, research and city per-

spective were assigned to four challenge dimensions

including DQ. In general, for data challenges in orga-

nisations two sides of the coin exist: the problem side

and the solution side. While for the solution side well-

settled concepts exist, the problem side beyond DQ

and its criteria remained disregarded up to now. Ho-

wever, researchers and practitioners need to be able

to assess an organisation’s data suitability. This paper

provides a ﬁrst overview of data excellence as well as

a preliminary deﬁnition.

Further research could do a structured literature

review on non-DQ topics and examine data challen-

ges on a more representative and broader empirical

basis. Also, profound, sound and generally valid cri-

teria are needed for non-DQ challenges. Coming to

the organisations itself, departments on compliance,

data management and operational departments need

to cooperate more efﬁciently in order to address inter-

linked data challenges.

ACKNOWLEDGEMENTS

A special thanks to Anette Weisbecker for her valu-

able structuring feedback. We kindly acknowledge

the works of Eva Graf and Caren Schelling, who

examined the enterprise case studies. We further

thank our colleagues for their valuable time and in-

put. The HEFE project, funded by the German Fe-

deral Ministry of Education and Research (funding

code 16FDM027) and supervised by VDI/VDE, aims

at elaborating a concept for data governance for rese-

arch data in the Urban Systems Engineering depart-

ment of Fraunhofer IAO.

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