Data Governance Issues in Digital Marketing

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 AI projects for digital marketing activities in Marketing organizations.

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 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 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.

Literature review; Longitudinal case study

(1) The development of a sustainable STS relies on data integration generated in different places, i.e. the sustainable 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 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.

Content analysis of secondary sources like books, journals

(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 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.

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 signi?cantly 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 e?ective 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.