Data Governance Issues in Digital Marketing: A Marketer’s Perspective

Keywordsdata governance digital marketing systematic review

JEL Classification M31

Full Article

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 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).

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).

The SLR in this paper based on a systematic review procedure established by Fettke (2006), as we see in Fig. 2.

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).

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

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

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

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".

Table 4. Source Title

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

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

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).

Table 7. Focused Data Governance Layer

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

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 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 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.

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.

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Funding: This research received no external funding.

Conflicts of Interest: The author declare no conflicts of interest.

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