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Expert Journal of Marketing, Volume 10, Issue 2, pp.124-142, 2022
© 2022 The Authors. Published by Sprint Investify. ISSN 2344-6781
http://Marketing.ExpertJournals.com
124
Data Governance Issues in Digital Marketing:
A Marketer’s Perspective
Matthias SCHMUCK
*
Alexandru Ioan Cuza University of Iaşi, Romania
To perform under the conditions of digitalization, marketers need understandable,
accurate, complete, trust-worthy, secure and discoverable data. In this respect, Data
Governance is a solution approach to greater data literacy, data intelligence and
data management. This research aims to explore the current academic state-of-art of
Data Governance in the Marketing field. Selected items were evaluated using a
descriptive statistical approach to identify investigation trends on this topic. The
research found that Marketing can benefit from Data Governance focusing data
standard settings, data sources coordination and data management, data quality and
data security. This requires a strategy, coordinated processes and their meaningful
support by people and information technology. The originality of this research is that
the approach is unique at this point, and the assimilation of Data Governance is a
clear sign of manifestation for Marketing under conditions of increasing
digitalization.
Keywords: digital marketing, data governance, systematic review
JEL Classification: M31
1. Introduction
Today’s social and economic environment is subject to permanent and increasingly rapid change.
Numerous factors - including the digital revolution of recent years, advancing globalisation, but also a con-
stant change in social values - make it necessary for companies and organisations today to flexibly adapt their
business processes to new framework conditions.
One of these core processes is Marketing. As the end interface between the company and its
environment, it is usually the first to be affected by new developments, because they deal with customers more
than other business functions (Kotler et al., 2020). In business research, the term “Marketing” is ambiguous or
not exact, which due to its historical development. It ranges from the set of terms
"advertising/sales/distribution" to the "concept for market-oriented corporate management" (Meffert, 2000;
Olbricht, 2022). In the context of this article, Marketing is understood as an information-processing corporate
func-tion at the interface with corporate’s environment, which first and foremost has the task of "... researching
the - both latent and manifest - needs of potential demanders and, if necessary, influencing them in the sense
of corporate goals." (Olbrich, 2022, p. 10). Marketing must ensure the provision of the information necessary
*
Corresponding Author:
Matthias Schmuck, Faculty of Economics and Business Administration, Alexandru Ioan Cuza University of Iaşi, Iaşi, Romania
Article History:
Received 5 November 2022 | Accepted 6 December 2022 | Available Online 27 December 2022
Cite Reference:
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
125
for planning and systematically proceeding with supply activities, in order to plan, implement and control the
use of the market policy means necessary for achieving cooperatives objectives (Meffert, 2000). Put simply,
the actions of Marketing are based on information (and thus data).
But what are relevant data according to Marketing? Marketers are interested in customer, financial,
and operational data (Kotler et al., 2017; Kotler et al., 2020). These data help the business to understand what
and when costumer are buying, what steps the customer take in the sales process and among others. Typically
obtained from different (public and/or private) sources and stored in different locations such data helps
marketers to understand their target audience by identifying ideal customers, crafting compelling con-tent and
building more effective campaigns or promotions (Meffert, 2000).
Under the conditions of increasing digitalization and automation using information and
communication technologies (ICT) - also through the increased use of Artificial Intelligence (AI) - Marketing
is becoming more and more digital (Kumar et al., 2013; Salesforce, 2021; Deloitte Insights, 2021; Bünte,
2021): as the customers go digital, Marketing steps up and data is becoming a central asset. “Digital
Marketing”, also known as “Online Marketing” or “Internet Marketing” and first coined in the 1990s
(Prajapati, 2020), re-lates to marketing of any products and services in digital form using digital devices, e.g.,
smartphones, tablets and others, and electronically interactive technology like mails, forums, newsgroups and
others (Kotler and Armstrong, 2009; Wirtz et al., 2014). The major advantage of Digital Marketing is that a
business can sell his products and/or services 24 hours and 365 days, or in other words “around the clock”
(Kotler, 2000). In this manner, Marketing and ICT become an important partnership in a proper combination
as a socio-technical system for decades (Graesch et al., 2020). Integrated ICT supports all Marketing operations
of a company and diversifies the marketing process (Bayraktutan et al., 2009), showing effects such as
providing opportunities for advertising anywhere and at any time and increasing the overall potential of
advertising (Hamidi and Safabakhsh, 2011).
In this context a holistic approach to manage data as an asset becomes a topic for Marketing. Data
Governance with over 15 years of attendance in research (Jagels et al., 2021) is one of such approaches that
within the practitioners’ community and among information systems (IS) researchers is attracting growing
attention. Software vendors (e.g. Kramer and Wilson, 2020), consulting companies (e.g. Petzold et al., 2020)
and analysts (e.g. Bitterer and Newman, 2007; Newman and Logan, 2006; Mohan and Maguire, 2019) have
emerged and give recommendations on how to establish Data Governance in business organizations. They
have proposed frameworks for Data Governance (Newman and Logan, 2006; Khatari and Brown, 2010; Otto
et al., 2007; Gartner, 2008; Sridharan, 2022; IBM 2007; Oracle 2011; Microsoft, 2020) and have analysed
influencing factors (Weber et al., 2009) as well as the current status of implementation (Blanton et al., 1992;
Otto, 2011b). Designing clear cross-functional processes and Governance are one of the most important
challenges for Marketing in the digital age (Leeflang et al., 2014).
Such as the “Marketing” term “Data Governance” is also ambiguous or not exact in the scientific field.
At the beginning of the research, Data Governance was seen as a further development of IT-Governance
(Weill, 2004; Weill and Ross, 2004; IT Governance Institute, 2007), which in turn evolved from Corporate
Governance (Correia and Água, 2021). Topics, such as data quality, data management, business intelligence
and analytics, big data, cloud computing, data protection, trust and security, expanded the research field of
Data Governance, but also led to a lack of differentiation from other research disciplines. So, Data Governance
is currently defined in a different and heterogeneous manner. The business or management-oriented research
group focused on handling data assets on the base of decision rights, e.g. by Otto (Otto, 2011a; Otto, 2011b;
Otto, 2012; Otto, 2013) or Weber (Weber et al., 2009; Weber, 2009). The technical oriented group (e.g. Lee
et al., 2018; Lee et al., 2019) focuses on technical implementation using commercially available software and
there are researchers that addresses both camps (e.g. Al-Ruithe et al., 2016; Al-Ruithe and Benkhelifa, 2017;
Ruithe and Benkhelifa, 2018).
Our work follows the interpretation of both camps and adapted Data Governance as an enterprise-wide
framework with the key components (in other words: layer) of strategy, processes and resources (people and
machines) ensures that enterprise data, especially for Marketing, is reliable and consistent, so that it can be
used with confidence for operational (Marketing) processes and (Marketing) decisions now and in the future
(Figure 1).
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
126
Marketing
Ressource(s)Process(es)Strategy
Figure 1. Key components of Data Governance
Source: Processing by the author
Within the strategy layer, Data Governance providing a framework that connects people to process
and technology, and create standards, processes and documentations structures for how a company will collect,
use and manage their (Marketing) data. Within the process layer, Data Governance establishes, implements
and evaluates policies and procedures, and monitors effort and results using KPIs and state-of-art reporting.
Within the resource layer, Data Governance implement structural and process organisation (people) in
cooperation with ICT-tools and infrastructure as a socio-technical system.
To be more operable Data Governance can be divided into fields of action. These fields are not
conclusively defined in the literature. Examples are data quality, data scope, data protection, security and
compliance, data management, data catalogue, data lineage, data stewardship, data ownership, data ethics and
others (Gluchowski, 2020; Weber and Klingenberg, 2021).
The growing evidence of Data Governance as a research field and the lack of review papers concerning
the topic are the motivation for this study. The main objective of the review is to identify, assess and summarize
the existing evidence of Data Governance in context of the business function Marketing in a brief overview.
In the line of these goal this research intends to address the following research questions:
- RQ1: Where does the research field of Data Governance in Marketing currently stand?
- RQ2: Which action fields of Data Governance are specifically addressed?
- RQ3: What are content and trending topics of Data Governance in Marketing and how can the field
mature and progress?
The research questions are designed to ensure that the collection of data is based on content analysis.
2. Research Methodology
In this section, the research methodology used to achieve the scientific goal and the search algorithm
with full details are discussed. The main motivation of this section is to ensure the reproducibility of the results
for the SLR.
2.1 Design
This paper follows IMRaD structure as a common document format in scientific writing (Sollaci and
Pereira, 2004). Stage 1 being the Introduction clause (see paper section 1), stage 2 describing research Method
and Materials (see paper section 2), stage 3 describing the Results (key findings) using an established
procedure (see paper section 3) and stage 4 concludes the study with a Discussion (see section 4).
The study uses systematic review of academic literature (SLR). The term "systematic review" is multi-
faceted: in the context of this paper adapted as a comparative analysis of different papers by identifying,
assessing, evaluating and interpreting all results. The result is a kind of review paper that answers one or more
research questions on the chosen topic (Kitchenham, 2004). The reader is provided with an in-formative
summary of the findings of other studies that are closely related to the study at hand. Reviews have proven to
be an effective research method (Wahono, 2015; Creswell and Creswell, 2018; Boote and Beile, 2005; Cooper
and Hedges, 1994).
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
127
The SLR in this paper based on a systematic review procedure established by Fettke (2006), as we see
in Fig. 2.
1.
Formulation of
the problem
2.
Search
literature
3.
Literature
evaluation
4.
Analysis and
Interpretation
5.
Presentation
Figure 2. SLR Flowchart
Source: Fettke, 2006
After the need for the review is determined (see above, section 1), a review protocol is created with
aim and objectives, followed by research questions and search process.
2.2 Selection process
The analysis was limited to the most popular and reliable database, namely Scopus digital library
(www.scopus.com). The search date was the last part of October, 2022. Within Scopus the following search
string are used and set on the following search expressions: Article Title, Abstract, Keywords:
(“Marketing” OR “Digital Marketing”)
AND (“Data Governance” OR “Information Governance”)
The search term “Information Governance” was included, because it is often used synonymously to
the term “Data Governance”. As part of boolean logic, "AND" and “OR” were used to narrow and broaden
the scan accordingly.
After getting core hits in Scopus - in total 29 papers - the results are refined to the used language.
Papers are not written in English are excluded, since English is the international language in academic. Further
limitations, e.g. source type or document type or access type, were not made.
After identifying 29 documents that met the search strings and the refine criteria, the content was
reviewed using titles and abstracts to apply criteria of quality (1st screening as quality assessment). The
scientific and/or empirical quality of the selected studies should be strongly assessed to eliminate potential
bias and optimize the power of the results. The following reason (R) checklist was used:
1. Where the authors, abstract or keywords explicitly provided (R1)?
2. Are the aims or objectives of the study clear (R2)?
3. Is the research method of the study explained (R3)?
4. Is there a (direct or indirect) link to the Marketing field in the study (R4)?
5. Are the topic and/or action fields of Data Governance clearly addressed (R5)?
Eight items do not meet the specified quality characteristics (R1 = 1; R2 = 0; R3 = 1; R4/R5 = 6).
Finally, 21 documents were included in the analysis (2nd screening: full text).
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
128
Database searching:
Scopus Digital Library
(n = 29)
Records resulted eligible in the first
screening step
(n = 21)
First Screening (Title, Abstract) Records excluded (n = 8)
Second Screening (Full text)
Identiy
Screen
Include
Records resulted eligible in the second
screening step
(n = 21)
Records excluded (n = 0)
Refinings: language
(n = 29)
Refining results Records excluded (n = 0)
Applying exclusion criteria: duplicates,
absence of author or abstract or others
(n = 29)
Figure 3. Selection process flowchart
Source: Processing by the author
2.3 Results found
After applying the exclusion and inclusion criteria to 29 papers found, 21 primary articles were
identified and could be read and categorized. They show a variety of Data Governance content in the (digital)
Marketing field (Table 1).
Table 1. Included Paper and their characteristics
Ref.
Objective(s)
Method(s)
Fundings/Results
Akter et al.
(2022)
The authors examine the impact of
market turbulence by considering the
changes in technology, competitor and
customers and modelling their overall
effects using review. They offer
significant insights that can transform
marketing thoughts and practices
across B2B cloud sharing platforms.
The study is one of the first empirical
attempts to identify marketing analytics
capabilities of cloud sharing platforms
focusing on pattern identification, real-
time solutions and Data Governance.
Survey
(1) Marketing analytics capabilities (i.e.,
Data Governance, pattern recognition
and real-time solutions) in sharing
platforms bring buyers and sellers
together to engage customers, reduce
churn and deliver personalised
communication campaigns. (2) The
effectiveness of Marketing using
analytics largely depends on how agile a
company is in meeting customer needs.
Customer segmentation and targeting
programmes, tailored offers, unique
content and relevant marketing metrics
determine marketing effectiveness. (3)
Managers need to build robust marketing
agility to manage market turbulence.
Blomster and
Koivumäki
(2022)
The authors examined the
organizational resources,
competencies, and capabilities required
for the successful implementation of
Content
Analysis,
Case Study
(1) The ability of Marketing
organisations to understand and refine
data by also considering the impact of
the Marketing environment is the most
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
129
Ref.
Objective(s)
Method(s)
Fundings/Results
AI projects for digital marketing
activities in Marketing organizations.
important competence for the success of
development projects in the AI
environment. Marketing organisations
therefore need to develop key analytical
skills (including understanding of data).
(2) Marketing organisations need to
develop rigorous business processes and
management procedures to support data
management in order to provide
appropriate data for AI.
Pugliese et al.
(2021)
The authors conducted an overview on
geo worldwide trends (based on data of
China, the USA, Israel, Italy, the UK,
and the Middle East) of AI approaches,
particularly Machine Learning (ML)
algorithms, for intelligent data analysis
and applications in different areas
(medical, financial, cybersecurity,
nanotechnology, agriculture) from a
technical, ethical and regulatory point
of view.
Descriptive
Study using
secondary
sources like
journals,
articles
(1) The complexity of AI applications in
terms of their characteristics of "opacity"
(an external observer may not be able to
detect the potentially harmful features of
ML and AI) and "unpredictability" (AI
learn from "their experiences" and
consequently their "behaviour" is
potentially unpredictable) make it
particularly difficult to establish effective
(legal) rules. (2) Liability issues related
to AI applications are also complex,
because it is (often) difficult to determine
who should be responsible for the
damage caused by ML or AI tools due to
the above-mentioned characteristics.
Shah et al.
(2021)
The authors' goal was to define the
factors involved in participants'
thinking about their expectations for
data flow and managing secondary use
of health data in relation to contextual
cues using focus group design: (a)
understanding the factors that influence
public perceptions and acceptance of
health data sharing when contextual
integrity and values associated with
trade-offs are violated; (b)
characterizing participants' experiences
with sharing their health-related data in
different contexts; and (c) identifying
health data flow governance
preferences in different contexts.
Focus
groups
(1) The lack of information, transparency
and control with regard to data
collection, management and use are
barriers to trust in organisations to use
data in ways they deem appropriate. (2)
The use of data by third parties requires
greater transparency and accountability
than is currently the case.
Abrantes and
Ostergaard
(2022)
Focusing the Danish market, the
authors examined digital footprint
awareness to understand the sentiment
(perception) and behaviour (action) of
data owners and data traders in the
context of data surveillance of personal
lives.
(Descriptive-
explorative)
multi-
method
study
(1) There is a general inability to
minimise the risks of data misuse. (2)
There is a willingness to pay for security
services to protect privacy. (3) If
personal information is disclosed, there
is anger among those affected, but little
willingness to fight back.
Mahmoudian
(2021)
In this paper, the author explains what
ethical challenges exist in the aspects
of data collection, data security, and
data protection in connection with the
use of AI applications (e.g., Big Data
analyses, machine learning) and what
approaches are suitable for effectively
meeting these challenges.
Descriptive
study
(1) As a result of increased use of data,
ethical challenges in data collection, data
security and data protection need to be
considered. (2) Implementing a Data
Governance framework and
standardising the data lifecycle can help
analytics-based marketing departments
work more effectively and proactively
address the concerns associated with
their operations.
Zhang and
Wang (2021)
This study is focused upon the form of
sustainable value cocreation of smart
transportation systems (STS). They
Literature
review;
(1) The development of a sustainable
STS relies on data integration generated
in different places, i.e. the sustainable
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
130
Ref.
Objective(s)
Method(s)
Fundings/Results
identify a number of key factors (e.g.
Data Governance) that lead to
successful STS design and
implementation. They decodes how a
Big Data-driven STS ecosystem works
in a situation where different
stakeholders play a special role and
interact closely with each other.
Longitudinal
case study
development of data infrastructure and
management information. (2) This data
infrastructure requires Data Governance
that bundles standardised data and
databases in a complicated socio-
technical structure. (3) Companies
actively participate in the formulation of
standards, but the government initiates
nationwide standardisation.
Gamoura and
Malhotra
(2020)
Focusing the French hypermarket the
authors provides first a review of
Master Data Management (MDM)
research maturity in the interconnected
Supply Chain systems and then to
depict the landscape and gaps of the
current researches in the Big Data era.
Secondly, the paper offers a new
architecture to support a collaborative
and compliant system for the Supply
Chains partners from the industrial
view.
Literature
review, Case
study
(1) An MDM solution can overcome
heterogeneity in master data and increase
customer satisfaction in the long term.
(2) However, the introduction and
operation of an MDM solution causes
high maintenance costs and
organisational constraints compared to
the status quo of a heterogeneous master
data landscape. (3) The use of
commercially available software
solutions depends heavily on the type
and size of the company.
Jamieson et al.
(2019)
In this article, the authors focus on
inform consent to the processing of
data as an active action by users of
information systems (IS), whether
digital or not. To this end, they present
a model derived from action research,
the information communication (IC)
paradigm, that presents inform consent
in the context of digital platforms and
electronic commerce and their
representation in IS as a socio-
technical construct.
Descriptive
study
(1) Despite the introduction of the
General Data Protection Regulation
(GDPR) in 2018 as a means of protection
in relation to data processing, there is a
lack of transparency in data processing
and consequently secondary data use,
especially in the active involvement of
third parties in the form of consent/assent
to data processing. (2) Consent is not
only the simple transfer of information
objects (content), but also under which
perspective (roles, norms, origins and
intentions of the subjects) this has taken
place (context).
Earley (2019)
The author examines a number of
issues related to the more recently
emerging role of the Chief Data Officer
(CDO) in the enterprise using
interviews. These include (a) the
definition of the CDO role, (b) its
scope of responsibility as distinguished
from other functions (like the chief
information officer or chief digital
officer), (c) questions about how the
company's data maturity relates to the
use of the CDO, (d) how the chief
marketing officer (CMO) has used this
new role to date, (e) the challenges
associated with such collaboration, and
(f) how different companies view the
marketing data challenge. The paper
also describes the implications of the
GDPR as a catalyst for data quality
initiatives and models for collaboration
between the CMO and CDO.
Interview(s)
(1) The CDO is responsible for
managing the company's data, marketing
managers (CMOs) use data to generate
business. Therefore, they need to partner
with the CDO. (2) Marketing needs data
from across the business, so marketers
need a comprehensive understanding of
that data. (3) Lack of sufficient funding
and authority further fragments data,
hindering digital transformation. (4)
Instead of focusing on the data and its
management per se, it is important to
focus on the insights that need to be
gained (from it). (5) The GDPR should
not be seen as a hurdle. It improved data
quality and customer interaction. (6) The
size of the company, the type of data
generated and consumed, the processes
supported, the type of industry and the
technological infrastructure determines
the organisation of the interface between
CMOs and CDOs.
Tapsell et al.
(2018)
The main objectives of this work can
be summed up as below: (a)
Challenges of data ownership and
control, and how it can be transferred
to individual users to own/manage their
Content
analysis of
secondary
sources like
(1) Offering data transparency to users is
a possible option for gaining a
competitive advantage. (2) In addition,
the CODCA can be a key factor in
monetising data: it gives individuals the
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
131
Ref.
Objective(s)
Method(s)
Fundings/Results
data. (b) A framework that brings
together the three main stakeholders
(users, organisations, governments) to
build a Consumer Oriented Data
Control and Auditability framework
(CODCA). (c) Building blocks of
CODCA: Consumer Data Control and
Data Auditability.
books,
journals
opportunity to retain control over their
personal data, and when they share the
data, they benefit both in terms of service
and financially.
Vojvodic and
Hitz (2018)
In the study, the authors investigate
whether expenditures (compliance
costs) for data protection compliance
(GDPR) can also generate additional
value, in the specific case related to
customer data processing. The study
examines the impact of Customer Data
Compliance Capability on Customer
Data Utility Capability through the
mediating role of Customer-Centric
Cross-Functional Integration.
Descriptive
study
(1) Customer-Centric-Cross-Functional
Integration has a mediating and thus
positive effect on Customer Data Utility
Capability and Customer Data
Compliance Capability. (2) There is a
leverage effect of existing knowledge
from customer data that resides within
functions and the ability to assess cross-
functional impacts of decisions related to
customer data. (3) If cross-functional
coordination and integration of customer
data occurs, customer-facing business
units can benefit equally.
Kamioka et al.
(2016)
The authors analysed survey data from
Japan to examine whether
accountability in Data Governance-
including role definition, management
oversight of data roles, and the
effectiveness of those roles-helps
improve perceived Marketing
performance.
Survey
(1) Accountabilities in Data Governance
are positively related to the data
utilization level, which, in turn, also
contributes to perceived performance in
marketing by the increased number of
sales and customer spending. (2)
Accountability in data governance is
linked to perceived marketing
performance. (3) The organizational
mode is influenced by company size.
De Freitas et
al. (2013)
In this paper, the authors present
activities to plan the data quality
measures required for the analytical
environment. In addition to presenting
a list of issues identified in the
customer registration form, the impact
of these issues on financial institutions
in management reporting, customer
relations and marketing campaigns,
product offerings, and others is
presented.
Descriptive
paper
(1) Awareness of data quality as a
cyclical activity must be created within a
Business Intelligence organization. (2)
The source systems must be monitored
with regard to their data quality, any data
anomalies identified there and also
corrected there. (3) Legal aspects must
be taken into account when defining
rules and measures for data correction.
Brayshaw
(2013)
In this Trade Journal article, the
authors introduce the concept of
Location Intelligence (LI) as one of
three main pillars (along with mobile
and social media) to support the future
of marketing strategies. They embody
the combination of media, data and
channels with which consumers will
act in the future through location-based
and cloud-based services.
Expert
article
(1) Standardize the way data is collected,
stored and maintained, bringing all
disparate systems into a central platform.
(2) Involve all employees to consolidate
the way of handling data.
Soares (2012)
In this Trade Journal article, the author
present a framework for Big Data
Governance as part of a broader
Information Governance programme
that formulates policies for Big Data
optimisation, privacy and monetisation.
Expert
article
(1) Organizations will be successful in
governing their big data if they adopt a
framework that covers the appropriate
types of big data, the information
governance disciplines, and the specific
use cases for their industry and function.
(2) Big Data Governance is meaningless
without an understanding of the
underlying data types.
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
132
Ref.
Objective(s)
Method(s)
Fundings/Results
Gregory and
Bentall (2012)
In this paper (as part of a series of
three) the authors explore (a) on how
organizations of any size can
significantly reduce their risks and
exposure when using third parties to
process their data and (b) on
identifying third-party touch points and
putting simple but effective risk
management controls in place.
Descriptive
paper
(1) Corporate information officers have
little sympathy when companies hand
over their responsibilities to unreliable
and unaudited third parties. (2)
Establishing sound internal processes
governing who can send data to external
organisations and what data can be sent,
standard contractual clauses and strict
SLAs for third parties all contribute to
the solution. (3) Relationships with third
parties shall be actively managed and
continuously reviewed, in particular
whether defined standards are met.
Gregory and
Hunter (2011)
In this paper (as part of a series of
three) the authors explore (a) on the
difference between data, information,
knowledge and wisdom; (b) on the
impact of inadequate data quality in
terms of direct costs, brand damage
and missed opportunity, as well as why
data quality is important to your
organization; (c) on fully understand
your organizations’ current capability
to deliver high data quality.
Descriptive
paper
(1) Find a high-level sponsor within the
company, ideally in top management,
who cares about data quality. (2)
Promote within the company that
information quality issues are being
looked for and ask employees to
participate in the improvement process.
(3) Evaluate the maturity of data quality.
(4) To achieve long-term success,
organizations need a vision, a
visualization of the vision, and a
roadmap to get there. (5) Quantify the
cost of poor data quality.
Jenson (2008)
In this Trade Journal article the author
presents his views on implementing
Data Governance best practices to
protect private information and
maintain the accuracy of financial
information.
Descriptive
study
(1) Data governance has become a
quality control discipline for assessing,
managing, using, improving, monitoring,
maintaining and protecting corporate
data. (2) Data governance assists in
overcoming various challenges in
complying with data protection and data
security regulations. (3) Proactive data
protection strategies prevent data
security breaches, while reactive
strategies detect security breaches that
have already occurred.
Sleep and
Harrison
(2022)
This study investigates the impact of
Information Governance on the quality
of information available, especially
how companies managing information
to provide high quality information and
how do collaboration do impact the
role of information use on information
quality and firm performance.
Survey
(1) A good structure, strategy and
process of Information Governance
positively impact information quality
which has a positive effect on business
results. (2) Differences in functional
power and in knowledge of Marketing
and IT at the executive level can
negatively affect collaboration between
these two functions.
Nahm (2012)
The author reports evaluative data
describing a potentially more scalable
process for the knowledge acquisition,
synthesis and definitional aspects of
data element standardization and
characterizes the semantic and
syntactic variability component of
information quality in data from
pivotal clinical trials in schizophrenia.
Empirical
Observation
(1) Semantic and syntactic variability in
clinical research data is a key
information quality issue in the
secondary use of these data. (2) Such
characterisation serves as a basis for data
standardisation efforts and provides
metrics to support data governance
efforts.
Source: The author elaboration
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
133
3. Key Findings
In this part we show the characteristics of the selected studies and give an overview of the interest in
the chosen topic over the years and the distribution of publications in different journals.
3.1 Overview
For a first overview, this study summarized the relevant papers (n=21) by source and document type,
territory, source title, subject area, year of publication, key components (layer) and fields of action of Data
Governance. Most of the papers published in (classical scientific and trade) journals, followed by conference
proceedings and reviews. Books are source type “underdogs” (Table 2).
Table 2. Source and Document Type
Source Type
Document Type
Reference(s)
A
CP
R
BC
N
Journal
7
2
1
Akter et al. (2022); Blomster and Koivumäki (2022);
Pugliese et al. (2021); Shah et al. (2021); Abrantes and
Ostergaard (2022); Mahmoudian (2021); Zhang and Wang
(2021); Earley (2019); Gregory and Bentall (2012);
Gregory and Hunter (2011)
Conference Proceedings
6
Jamieson et al. (2019); Tapsell et al. (2018); Vojvodic and
Hitz (2018); Kamioka et al. (2016); De Freitas et al.
(2013); Nahm (2012)
Trade Journal
3
Brayshaw (2013); Soares (2012); Jenson (2008)
Book; Book Series
2
Gamoura and Malhotra (2020); Sleep and Harrison (2022)
Abbreviation(s): A = Article; CP = Conference Paper; R = Review; BC = Book Chapter; N = Note; Limitation(s): no
multiple assignments
Source: The author elaboration
In terms of the territorial distribution of articles, the global camp is very dispersed (Table 3). The
leading regions are Europe (e.g., UK, Finland, Sweden, France, Czech Republic) and North America (US),
followed by Asia (e.g. India), Australia and South America (e.g. Brazil). All research papers provide a good
insight into the topic. The African region is not covered, which certainly has potential for future research. Of
critical importance is that collaboration between the regions as a whole is strengthened.
Table 3. Territory
Territory
Qty
Reference(s)
Europe
12
Akter et al. (2022); Blomster and Koivumäki (2022); Pugliese et al. (2021); Shah et al.
(2021); Abrantes and Ostergaard (2022); Zhang and Wang (2021); Gamoura and Malhotra
(2020); Jamieson et al. (2019); Tapsell et al. (2018); Vojvodic and Hitz (2018); Kamioka et
al. (2016); Gregory and Hunter (2011)
North America
5
Mahmoudian (2021); Earley (2019); Brayshaw (2013); Sleep and Harrison (2022); Nahm
(2012)
Asia
3
Akter et al. (2022); Gamoura and Malhotra (2020); Kamioka et al. (2016)
Not defined
3
Soares (2012); Gregory and Bentall (2012); Jenson (2008)
Australia
2
Akter et al. (2022); Shah et al. (2021)
South America
1
De Freitas et al. (2013)
Abbreviation(s): Qty = Quantity; Limitation(s): multiple assignments
Source: Processing by the author
Regarding the sources (Table 4), the articles can be found in various journals, which mostly address
topics of Marketing, e-Business and Business Intelligence and Analytics. The conferences are predominantly
conferences with focus on "Information Systems".
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
134
Table 4. Source Title
Source
Type
CS*
Qty
Reference(s)
Applied Marketing Analytics
J
0,4
2
Mahmoudian (2021); Earley
(2019)
Data Science and Management
J
n/a
1
Pugliese et al. (2021)
DB2 Magazine
TJ
0,101
1
Jenson (2008)
Developments in Marketing Science: Proceedings of the
Academy of Marketing Science
B
n/a
1
Sleep and Harrison (2022)
GEO: connexion
TJ
0,0
1
Brayshaw (2013)
IBM Data Management Magazine
TJ
0,0
1
Soares (2012)
Impacts and Challenges of Cloud Business Intelligence
B
n/a
1
Gamoura and Malhotra
(2020)
Industrial Marketing Management
J
10,4
1
Akter et al. (2022)
Information Systems and e-Business Management
J
5,3
1
Blomster and Koivumäki
(2022)
Information Systems Frontiers
J
10,3
1
Zhang and Wang (2021)
International Journal of Medical Informatics
J
8,0
1
Shah et al. (2021)
Journal of Direct, Data and Digital Marketing Practice
J
1,0
2
Gregory and Bentall (2012);
Gregory and Hunter (2011)
Journal of Marketing Analytics
J
3,4
1
Abrantes and Ostergaard
(2022)
Proceedings - Pacific Asia Conference on Information
Systems, PACIS 2016
CP
1
Kamioka et al. (2016)
Proceedings - 16th IEEE International Conference on
Computational Science and Engineering, CSE 2013
CP
1
De Freitas et al. (2013)
Proceedings - 17th IEEE International Conference on Trust,
Security and Privacy in Computing and Communications
and 12th IEEE International Conference on Big Data
Science and Engineering, Trustcom/BigDataSE 2018
CP
1
Tapsell et al. (2018)
Proceedings of the 31st International Business Information
Management Association Conference, IBIMA 2018:
Innovation Management and Education Excellence through
Vision 2020
CP
1
Vojvodic and Hitz (2018)
Proceedings of the International Conference on Electronic
Business (ICEB)
CP
1
Jamieson et al. (2019)
Proceedings of ICIQ 2012: 17th International Conference on
Information Quality
CP
1
Nahm (2012)
Abbreviation(s): J = Journal | TJ = Trade Journal | CP = Conference Proceedings | CS = CiteScore at Scopus for 2021 |
Qty = Quantity; Limitation(s): no multiple assignments
Source: The author elaboration
The Scopus CiteScore 2021 counts the citations received in 2018-2021 to articles, reviews, conference
papers, book chapters and data papers published in 2018-2021, and divides this by the number of publications
published in 2018-2021 (Scopus, 2022). The distribution of articles within the scientific disciplines (Table 5)
is balanced between "Science, Technology, Engineering, and Mathematics (STEM)" and "Business
Administration" (which includes Economics, Finance and Accounting). This reflects the integrated view in
terms of Business-IT-Alignment in the Marketing field.
Table 5. Subject Area
Subject Area
Qty
Reference(s)
Business, Management, and Accounting
11
Akter et al. (2022); Pugliese et al. (2021); Abrantes and Ostergaard
(2022); Mahmoudian (2021); Jamieson et al. (2019); Earley
(2019); Vojvodic and Hitz (2018); Soares (2012); Gregory and
Bentall (2012); Gregory and Hunter (2011); Sleep and Harrison
(2022)
Computer Science
10
Blomster and Koivumäki (2022); Pugliese et al. (2021); Zhang and
Wang (2021); Gamoura and Malhotra (2020); Jamieson et al.
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
135
Subject Area
Qty
Reference(s)
(2019); Tapsell et al. (2018); Kamioka et al. (2016); De Freitas et
al. (2013); Jenson (2008); Nahm (2012)
Decision Science
7
Pugliese et al. (2021); Abrantes and Ostergaard (2022);
Mahmoudian (2021); Earley (2019); Tapsell et al. (2018);
Vojvodic and Hitz (2018); Jenson (2008)
Engineering
3
Tapsell et al. (2018); Soares (2012); Nahm (2012)
Material Sciences
1
Soares (2012)
Earth and Planetary Sciences
1
Brayshaw (2013)
Economics, Econometrics and Finance
1
Abrantes and Ostergaard (2022)
Mathematics
1
Zhang and Wang (2021)
Medicine
2
Shah et al. (2021)
Abbreviation(s): Qty = Quantity; Limitation(s): multiple assignments
Source: Processing by the author
Looking at the years in which the above-mentioned articles were published, there is an increasing
trend, although in the years themselves the numbers sometimes vary considerably (Table 6, Figure 4).
Table 6. Year of Publishing
Year
Qty
Reference(s)
2008
1
Jenson (2008)
2011
1
Gregory and Hunter (2011)
2012
3
Soares (2012); Gregory and Bentall (2012);
Nahm (2012)
2013
2
De Freitas et al. (2013); Brayshaw (2013)
2016
1
Kamioka et al. (2016);
2018
2
Tapsell et al. (2018); Vojvodic and Hitz
(2018)
2019
2
Jamieson et al. (2019); Earley (2019)
2020
1
Gamoura and Malhotra (2020)
2021
4
Pugliese et al. (2021); Shah et al. (2021);
Mahmoudian (2021); Zhang and Wang
(2021)
2022
4
Akter et al. (2022); Blomster and Koivumäki
(2022); Abrantes and Ostergaard (2022);
Sleep and Harrison (2022)
Abbreviation(s): Qty = Quantity; Limitation(s): multiple assignments
Source: Processing by the author
Figure 4: Year of Publishing
This also correlates with Jagels et al. (2021), saying that publications concerning DG actually started
in 2005 and has increased ever since. Nevertheless, the topic enjoys a constant attention in the academic world.
The upward trend should continue in the future as a result of an increasingly data-driven world. According the
defined term of Data Governance described above, the studies focus on all Data Governance key components
(layer), primarily on processes and resources, but crossover aspects, like ethical considerations (known as data
ethics), are applied (Table 7).
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
136
Table 7. Focused Data Governance Layer
Data Governance Layer
Qty
Reference(s)
Strategy
5
Blomster and Koivumäki (2022); Gamoura and Malhotra (2020); Gregory and
Hunter (2011); Sleep and Harrison (2022); Nahm (2012)
Process(es)
20
Akter et al. (2022); Blomster and Koivumäki (2022); Pugliese et al. (2021);
Shah et al. (2021); Abrantes and Ostergaard (2022); Mahmoudian (2021);
Zhang and Wang (2021); Gamoura and Malhotra (2020); Jamieson et al.
(2019); Earley (2019); Tapsell et al. (2018); Kamioka et al. (2016); De Freitas
et al. (2013); Brayshaw (2013); Soares (2012); Gregory and Bentall (2012);
Gregory and Hunter (2011); Jenson (2008); Sleep and Harrison (2022); Nahm
(2012)
Resource(s)
11
Akter et al. (2022); Blomster and Koivumäki (2022); Pugliese et al. (2021);
Shah et al. (2021); Abrantes and Ostergaard (2022); Zhang and Wang (2021);
Gamoura and Malhotra (2020); Earley (2019); Brayshaw (2013); Soares
(2012); Gregory and Hunter (2011)
Cross-over Aspects
4
Shah et al. (2021); Mahmoudian (2021); Jamieson et al. (2019); Earley (2019)
Abbreviation(s): Qty = Quantity; Limitation(s): multiple assignments
Source: Processing by the author
In terms of fields of action of Data Governance (Table 8), the studies focus primarily on Data
Protection, Security and Compliance (from the author's point of view as a consequence of the introduction of
the GDPR in May 2018 and their implementation), Data Management (because of getting customer insights
for decision-making) and Data Quality (because Marketing needs accurate and timely information to manage
Marketing service effectiveness and to prioritize and ensure the best use of resources). Because of missing
other action fields, like Data Scope, Data Catalogue or Data Lineage, research potential is given.
Table 8. Focused Action field of Data Governance
Fields of Action in Data
Governance
Qty
Ref.
Data Quality
7
Earley (2019); De Freitas et al. (2013); Brayshaw (2013); Gregory and Hunter
(2011); Jenson (2008); Sleep and Harrison (2022); Nahm (2012)
Data Management
16
Akter et al. (2022); Blomster and Koivumäki (2022); Pugliese et al. (2021);
Mahmoudian (2021); Zhang and Wang (2021); Gamoura and Malhotra (2020);
Earley (2019); Tapsell et al. (2018); Vojvodic and Hitz (2018); Kamioka et al.
(2016); Brayshaw (2013); Soares (2012); Gregory and Bentall (2012); Gregory
and Hunter (2011); Sleep and Harrison (2022); Nahm (2012)
Data protection, Security
and Compliance
10
Pugliese et al. (2021); Shah et al. (2021); Abrantes and Ostergaard (2022);
Mahmoudian (2021); Jamieson et al. (2019); Earley (2019); Tapsell et al.
(2018); Vojvodic and Hitz (2018); Gregory and Bentall (2012); Jenson (2008)
Abbreviation(s): Qty = Quantity; Limitation(s): multiple assignments
Source: Processing by the author
3.2 Content and Trending Topics
This chapter analyses the results of the review according to the theoretical introductions presented at
the beginning of the systematic review. Recall that Data Governance is an enterprise-wide concept
encompasses the strategy, processes and resources (people, ICT) needed to manage, protect and enhance an
organisation's data capital (e.g. Marketing) in order to guarantee universally understandable, accurate,
complete, trustworthy, secure and discoverable data. For this we systemized the results on the three levels -
the key components of Data Governance, also on cross-over aspects and trending topics.
Strategy: Establishing Data Governance in Marketing requires direction to bring it into a “lived
Framework” of an Organisation. This direction is provided by the Data Governance Strategy (Vision) with the
goal of actively shaping and empowering the Marketing organisation to make the best use of its data capital
as well as to manage the increasingly complex compliance requirements in a low-risk manner (Blomster and
Koivumäki, 2022; Zhang and Wang, 2021; Gregory and Hunter, 2011). This includes active responsibility at
top management level, e.g. by a Board Member/Executive Director (Gregory and Hunter, 2011), as well as
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
137
taking into account the peculiarities of the industry and operational organisation. Furthermore, this must be
aligned with the corporate strategy.
Process(es): Furthermore, the results of the studies underline that Data Governance processes must be
established, documented and lived in order to (a) minimise data silos, inconsistent data and incorrect
classifications through establishing rules to reduce semantic and syntactic variability in data (Nahm, 2012) and
data management (e.g. Akter et al., 2022; Pugliese et al., 2021; Gamoura and Malhotra, 2020), (b) permanently
increase data quality by checking and measuring it - at least on a legal and regular basis (e.g. De Freitas et al.,
2013; Brayshaw, 2013; Soares, 2012; Jenson, 2008; Sleep and Harrison, 2022) and (c) restrict access to critical
and sensitive data in order to meet data protection, data security and compliance requirements (e.g. Tapsell et
al., 2018; Vojvodic and Hitz, 2018; Gregory and Bentall, 2012; Abrantes and Ostergaard, 2022; Jamieson et
al., 2019). In particular, compliance with regulatory requirements, such as the GDPR, are crucial for the
acceptance of Data Governance (e.g. Shah et al., 2021; Mahmoudian, 2021; Earley, 2019). Such regulations
are important because they can be helpful in clarifying grey areas. For example, the GDPR attempted to clarify
what constitutes a high-risk use case and what is expected in these use cases (confirmatory test assessments,
audits and the like). This kind of clarification increases process understanding and reduces risks. Company
size also influences the process organizational form of data governance in a balance of automation and non-
automation (Kamioka et al., 2016).
Resource(s): The competence of (Marketing) staff to understand the possibilities of data and the use
of technology, as well as the understanding of software and computer skills are important skills for digital
marketing organisations (Blomster and Koivumäki, 2022; Gregory and Hunter, 2011). In many companies,
the role of "Chief Data Officer (CDO)" has been established to reflect the increasing importance of data
(Earley, 2019). Its tasks and responsibilities must not collide with those of the "Chief Marketing Officer
(CMO)", but a partnership of both is required: the CDO focuses on providing fully integrated information
sources of sufficient quality, the CMO focuses on brand, communication and business strategy, as well as
analytics, data, customer segmentation and social media. According data quality this activity is not an IT
activity alone (Gregory and Hunter, 2011). It should by start in the IT, but it must be follow in business
involving all business areas that create, utilize or report on business information. And all activities should be
supported by the right ICT (Gregory and Hunter, 2011).
Cross-Over Aspects: Throughout all phases of Data Governance, ethical considerations must be
integrated into the aspects of data collection, data security and data protection (Mahmoudian, 2021; Earley,
2019; Shah et al., 2021). In this respect, Data ethics is not primarily a privacy and security compliance exercise.
It is also not about bias or fairness, but about the whole managing process: if, for example, rules for handling
and protecting critical or sensitive data are not implemented or not implemented correctly, this can have an
impact on people (Mahmoudian, 2021). This also applies to consent to the processing of data (e.g. in the
context of a marketing campaign, as an active act by users of information systems (IS), whether digital or not
(Shah et al., 2021; Jamieson et al., 2019). Users of IS must be able to determine with what content (that is, the
information generated and exchanged within the IS) and under what perspective and what purposes the consent
was given.
Trending topics: One of the emerging trends in Marketing is the introduction of AI methods (Bünte,
2021). Data and its management are the most important resource for the successful implementation of an AI
development project in Digital Marketing (Blomster and Koivumäki, 2022). However, not only the data itself,
but also the performance of the learning algorithms influence the success and acceptance of AI in the marketing
field (Pugliese et al., 2021; Mahmoudian, 2021).
3.3 Future Research and Limitations
The selected studies give a good first impression of Data Governance research in the Marketing field.
Nevertheless, recommendations for further research are given here and limitations are pointed out.
Future research recommendations: The starting point of a Data Governance initiative is to measure
the maturity of the Marketing organization , the ability or maturity level with respect to the asset data. Due this
fact a specifically Data Governance Maturity Model for Marketing should be developed. This model helps the
Marketing organization pass in its quest to achieve a fully developed data management program.
Further investigation on other fields of action of Data Governance should be done. This concerns (a)
data catalogues providing a central view on meta data of Marketing data, (b) data lineage providing the
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
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information about the use, processing, quality and performance of Marketing data throughout its lifecycle,
from original creation to deletion, (c) data ownership having legal rights and complete control over all
Marketing data elements and (d) data scope establishing principles and procedures for the evaluation and
prioritisation of high-value and high-risk Marketing data.
The Marketing organization and its changing role - in more and more digitalized companies as the
customer journey becomes more complex - are another research object. At its core, it is about hiring and
developing analytical skills while maintaining a culture of creativity, collaboration in hybrid work
environments, and increasing competition for talent.
Another research aspect of particular importance is the question of sponsorship at management level
or the organizational integration of the topic of Data Governance into corporate organization. The focus is on
differentiating the Marketing function, especially the Chief Marketing Officer, from earlier established roles,
e.g., the Chief Information Officer or Chief Technology Officer, and roles that have emerged more recently,
e.g., the Chief Data Officer and Chief Digital Officer.
And last but not least, it is also a question of differentiating Data Governance from other Governance
areas (e.g., knowledge governance and information governance), taking industry and company peculiarities
into account.
Limitations of the study: With regard to the review conducted, some limitations should be noted.
Firstly, the scope of the studies was not as large as the author had expected due to the current hype around the
topic of Data Governance. Further studies using other types of research methods may provide additional
information. Secondly, only one database (Scopus) was retrieved. Extending the search to other common
libraries may also provide additional information. In addition, only articles written in English were considered.
Furthermore, only the information contained in the selected studies was assessed and merged; therefore, some
publication error cannot be completely excluded.
4. Conclusion
This paper presents the topic of Data Governance in the Marketing environment. From the analysis of
the study results, the author concludes that Data Governance has a great potential to develop a systematic and
integrative view of (relevant) data in Marketing. For this approach theoretical aspects and practical
implications are marked out.
4.1 Theoretical Aspects
The aim of this work was to promote a better understanding of the extent to which Data Governance
in the Marketing environment can support the targeted maximization of the value of Marketing data. This
study makes an important contribution to the Marketing literature by presenting current studies from the fields
of action of Data Governance and summarizing their results. It thus presents an overview of the current state
of assimilation of Data Governance in the Marketing field.
The research addresses all three levels of Data Governance for the marketing division - strategy,
processes, resources. This is a clear reflection of the fact that Data Governance is seen as an integrative
approach. A well-designed strategy supports uniform, standard processes as well as responsibilities and shows
which data - from the point of view of data security and data protection - require careful control and how
support (human, IT) can be provided in a way that makes sense in terms of resources. Customer-oriented
business units can benefit from a homogeneous data infrastructure if it bundles standardized data and databases
in a socio-technical structure. This applies in particular to standardization of the way master data is collected,
stored and maintained, the quality of which increases customer satisfaction.
Data governance helps in overcoming various challenges in complying with data protection and data
security regulations. Providing transparency in data processing can be a competitive advantage for customer-
facing businesses. In more and more digitalized and thus data-driven companies, the topic needs to be
anchored organizationally at the highest management level. It is advantageous to have a high-level sponsor
within the company.
Schmuck, M., 2022. Data Governance Issues in Digital Marketing: A Marketer’s Perspective. Expert Journal of Marketing, 10(2), pp.124-142.
139
4.2 Practical Implications
In addition to the theoretical insights, this study also offers a number of implications for marketing
practitioners. First and foremost, practitioners must recognize that Data Governance does not exist "out-of-
the-box." It has to be developed individually for the respective company. Since the know-how for handling
data is usually already available in the company, it is not necessary to reinvent everything.
This study has confirmed that Data Governance must have a clear strategy from which concrete goals
can be derived, which in turn are concretized in a concrete governance plan. Such a plan includes not only the
procedure for how decisions are made when processing data, but also the documentation of data protection
and data security principles, ownership and responsibilities. It should be noted that the organization of
decisions as well as the principles of data protection and data security are not the same for all data and it is
therefore advisable to group data semantically, i.e., with the same requirements. This facilitates the definition
of rules.
From this point, Data Governance processes need to be defined, described and established. When
designing and implementing Data Governance, the specifics of the operational organization and the respective
industry must be taken into account. This has an influence on the concrete organizational structure and process
organization and the use of people and machines in a sensible combination as a socio-technical system.
Furthermore, Data Governance must be understood as a continuous (improvement) process. The
results achieved must be continuously compared with the defined goals in order to correct the measures and,
if necessary, the goals. For this purpose, metrics that are as measurable as possible are already defined in the
data governance strategy. Suitable tools for collecting values for the defined metrics are interviews or
anonymous surveys.
Funding: This research received no external funding.
Conflicts of Interest: The author declare no conflicts of interest.
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