Clean Customer Master Data for Customer Analytics: A Neglected Element of Data Monetization

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The authors should clarify the research gap of existing studies and how they address this research gap. In addition, the authors should explain the external validity of this study since different companies have different systems. 

Comments on the Quality of English Language

The authors should clarify the research gap of existing studies and how they address this research gap. In addition, the authors should explain the external validity of this study since different companies have different systems. Specifically, they should elaborate on how the results could be applicable to other companies.

Author Response

Thanks for this comment. We kept our initial arguments and tried to be more clear with our research gap. We also added some literature recommended by reviewer 2. In the revised manuscript, we argued as follows:

 

However, prior studies have overlooked two critical aspects. First, it remains unclear how companies’ legacy information and communication systems influence the outcomes of their data monetization initiatives. Legacy systems naturally shape the starting points of these initiatives by either inhibiting or facilitating company goals, roadmaps, strategic plans, and implementations. Legacy systems can impose burdens related to data quality, harmonization, and consistency, thereby delaying the achievement of expected outcomes in data initiatives [2, 7].

Second, existing research frequently examines data monetization initiatives at the company level while emphasizing the importance of implementing lighthouse projects and developing concrete use cases to create and sustain momentum for data initiatives. The literature often highlights the successes of individual use cases without delving deeply into selected examples or exploring the reasons behind the failures of these use cases. This oversight is notable, given that many companies report difficulties during the implementation and eventual discontinuation of promising use cases [8].

We contend that the existing evidence and theoretical considerations indicate a significant gap in the understanding of how companies can effectively benefit from data. To address these fundamental issues, we investigated the following research question through a single case study of customer analytics in collaboration with a medical technology company, Medical Inc.: How should companies prepare their customer master data to monetize their customer analytics efforts?

 

According to the external validation, we now argue in the following way.

 

Our insights emerged from joint research activities together with our single case company, Medical Inc. Naturally, the results yielded by our single case study are limited in terms of external validation. Thus, while our findings are not generalizable, we believe that they are transferable to many other companies [43]. Future research could apply our findings to other companies struggling with legacy IT environments.

Reviewer 2 Report

Comments and Suggestions for Authors

1.      This is a well-written paper, but it could be further improved.

2.      Any discourse pertaining to customer data, particularly that of a medical nature, should provide an explanation of the measures undertaken to ensure data privacy and the protection of personal information. This could encompass policies and safeguards implemented to uphold the confidentiality, integrity, and accessibility of such sensitive data, in alignment with relevant privacy regulations and industry best practices. The discussion could outline the technical, administrative, and physical mechanisms employed to mitigate the risks of unauthorized access, use, disclosure, or breach of the customer's personal information.

3.      The literature review includes a significant number of influential papers on the discussed concepts, reflecting academic thoroughness. However, considering additional sources that offer nuanced perspectives could further enhance the literature review. They include but are not limited to:

3.1.   Gülçay, Zehra. "Improving Master Data Governance Processes Within Supply Chain Management." Bachelor's thesis, University of Twente, 2024.

3.2.   Meena, Priyanka, and Praveen Sahu. "Customer relationship management research from 2000 to 2020: An academic literature review and classification." Vision 25, no. 2 (2021): 136-158.

3.3.   Pansara, Ronak Ravjibhai. "Maturity Model of Master Data Management at Enterprise Level." Sch J Eng Tech 2 (2024): 31-39.

Shah, Syed Iftikhar Hussain, Vassilios Peristeras, and Ioannis Magnisalis. "Government big data ecosystem: definitions, types of data, actors, and roles and the impact in public administrations." ACM Journal of Data and Information Quality 13, no. 2 (2021): 1-25

Author Response

Any discourse pertaining to customer data, particularly that of a medical nature, should provide an explanation of the measures undertaken to ensure data privacy and the protection of personal information. This could encompass policies and safeguards implemented to uphold the confidentiality, integrity, and accessibility of such sensitive data, in alignment with relevant privacy regulations and industry best practices. The discussion could outline the technical, administrative, and physical mechanisms employed to mitigate the risks of unauthorized access, use, disclosure, or breach of the customer's personal information.

 

Thanks for this comment. Our discussion on customer data is not about data for actual patients. So, data privacy is not such an issue for our case company. In the manuscript, we added following explanation:

 

Medical Inc. does not directly approach patients but rather sells its products to health care providers. When it comes to customer data, no private data on patients is involved, but only data on the professional health care providers. The discussion of data privacy is not applicable to Medical Inc.

 

 

The literature review includes a significant number of influential papers on the discussed concepts, reflecting academic thoroughness. However, considering additional sources that offer nuanced perspectives could further enhance the literature review. They include but are not limited to:

 

Gülçay, Zehra. "Improving Master Data Governance Processes Within Supply Chain Management." Bachelor's thesis, University of Twente, 2024.

 

Meena, Priyanka, and Praveen Sahu. "Customer relationship management research from 2000 to 2020: An academic literature review and classification." Vision 25, no. 2 (2021): 136-158.

 

Pansara, Ronak Ravjibhai. "Maturity Model of Master Data Management at Enterprise Level." Sch J Eng Tech 2 (2024): 31-39.

 

Shah, Syed Iftikhar Hussain, Vassilios Peristeras, and Ioannis Magnisalis. "Government big data ecosystem: definitions, types of data, actors, and roles and the impact in public administrations." ACM Journal of Data and Information Quality 13, no. 2 (2021): 1-25.

 

Thanks for this comment. We added these articles to the manuscript.

Reviewer 3 Report

Comments and Suggestions for Authors

The economic value of data resources is a widely concerned topic in the era of digital economy. This field is worth studying, and I appreciate the author's attempt.

Some suggestions can be considered before publication.

A major concern. The object of this study is the monetization of underlying data, which seems to be a solved problem. Also, there is a huge difference with the concept of digitization, digital transformation.

Section 2 introduces a number of concepts that are not actually theoretical background. Moreover, what is the relationship between these concepts? Although these concepts are all relevant, there are significant differences in these concepts. What is the effect of this on this study?

Section 3 is proposed to be divided into several parts, including research data, analysis methods, analysis process, etc.

The process for dealing with these challenges in Section 4.4 can be illustrated or tabulated.

In the conclusion part, the theoretical conclusions of this study can be discussed. However, this is also my concern that the study may lack theoretical insights.

Author Response

The economic value of data resources is a widely concerned topic in the era of digital economy. This field is worth studying, and I appreciate the author's attempt.

 

Some suggestions can be considered before publication.

 

A major concern. The object of this study is the monetization of underlying data, which seems to be a solved problem. Also, there is a huge difference with the concept of digitization, digital transformation.

 

Thanks for your comment. We agree there is a huge difference with the concept of digitization and digital transformation. That is why we defined them in the manuscript.

 

Digitization involves converting analog signals into digital ones, thereby separating data from its medium.

 

Digitalization involves integrating digital technologies into organizational processes to create new value opportunities.

 

Digital transformation immerses the entire enterprise in digital methods, extending beyond processes and data to impact operations, business models, and competencies.

 

Please check the manuscript for further details

 

 

 

Section 2 introduces a number of concepts that are not actually theoretical background. Moreover, what is the relationship between these concepts? Although these concepts are all relevant, there are significant differences in these concepts. What is the effect of this on this study?

 

Thanks for your comment. We want to explain the background more comprehensively. That is why we mentioned various concept even if not all of them directly contribute to the results. But they have an indirect effect on our contributions.

 

Section 3 is proposed to be divided into several parts, including research data, analysis methods, analysis process, etc.

 

Thanks. We structured this section according to your suggestion.

 

The process for dealing with these challenges in Section 4.4 can be illustrated or tabulated.

 

Thanks. We added a Table a suggested.

 

In the conclusion part, the theoretical conclusions of this study can be discussed. However, this is also my concern that the study may lack theoretical insights.

 

Thanks. We strengthened the theoretical conclusions. In the manuscript, we argue as following:

 

Overall, our findings supplement the existing literature on digitization, digitalization, and digital transformation; data monetization, structuring, and analytics; customer analytics; and algorithms for data integration and cleaning in three different ways [3, 16, 18]. First, we identify key activities that are necessary for the successful deployment of data initiatives in customer analytics. Rather than aiming for an exhaustive list, we focus on specific key activities we deem most crucial for advancing data utilization and monetization through customer analytics. Second, we integrate these key activities into an overarching framework, illustrating how companies can advance their data and advanced analytics initiatives throughout their digitization and digital transformation efforts. Third, we address a gap in the 3Ds—digitization, digitalization, and digital transformation—highlighting that digitization alone is insufficient for achieving the next step in digital advancement [1, 9].

In more detail, our case study of Medical Inc. reveals that data cleaning, preparation, and harmonization are foundational for deriving value from customer data [22, 23]. In-sights gleaned from Medical Inc.’s endeavors offer a valuable framework for a generalized customer master data cleansing process, comprising problem identification, categorization, prioritization, and subsequent cleansing activities. This structured framework underscores the iterative nature of data management, emphasizing continuous evaluation and refinement to maintain data integrity and drive informed decision-making [35].

We propose a nine-step framework within the phase of pre-digitalization (datatization), emphasizing that digitization alone is insufficient for achieving digital transformations. Companies must integrate key activities into a comprehensive approach, addressing both the theoretical and practical aspects of data utilization. We call this datatization. Datatization refers to the process of converting various forms of information into data that can be quantified, analyzed, and utilized in analytics systems. This involves capturing, storing, and organizing data from diverse sources, thereby transforming it into structured formats suitable for computational analysis and decision-making processes. Datatization enables the extraction of actionable insights, supports data monetization strategies, and facilitates the integration of data into broader digital ecosystems.

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

It is a well-done work.