Monetizing of “Data-Driven” business models – how data become the new oil of the digital healthcare

Abstract. A data-driven approach means that a company or organization makes decisions based on structured data by analysis and interpretation. The healthcare-sector faces an ongoing transformation due to the digitalization like in other industries and the disruption could be accelerated by new data-driven business models. Data security is the biggest challenge, especially when you handle with sensitive patient-data in healthcare. During this revolution new technology is emerging very fast, but other factors related to the way how data is used or analyzed and how the stakeholders are involved can enable or hinder the change. Data is the new oil and Big Data is a rising business in the future. This Article gives a short overview about the opportunities for the stakeholders in healthcare with examples of existing Uses-Cases. It outlines how data and AI can help physicians to make decisions for patient individual treatments and better outcomes.

Keywords: Big Data, AI, Business Intelligence, Data-driven Business, Platforms

JEL codes: C23, C61, R15, O33, Q55


1 Introduction and Relevance

Successful companies in the future will build business models around data. Therefore, these companies will base their core business on data to run a data-driven business model. The focused dependence on data can affect a new or existing business model in all dimensions. On the one hand, starting with the value proposition and, on the other hand, the value creation or the fundamental revenue model of a company. The actual added value is created from data, which is generated data acquisition, data evaluation, or data use become the core activities and consequently the key resources of a company.

“The use and evaluation of data and data streams are pacesetters of progress in all sectors – be it with regard to industry, mobility, energy, education or healthcare.”

(Quote by Angela Merkel, Former Chancellor of Germany [Radovic/2016])

The approach of building one’s core business on data is nothing new. At the same time, new technologies are opening up new opportunities for more and more industries or start-ups, which can be useful through legal storage and mass evaluation as well as profitable marketing of the data [Elton-O’Riordan /2016].

For example, in the field of industry, through the use of Internet of Things (“IoT”) and the prediction of maintenance cycles and minimization of failure risks for machines, for example in the automotive industry. Various sensors already measure many values and provide drivers with useful information in real time, from traffic jams to weather reports or engine data while driving [Radovic/2018].

Through the use of wearables in humans (e.g. smart devices such as watches or rings), smart products can be realized to integrate the human or patient more strongly into the decision-making process when recommending therapies. The topic of date-driven business thus goes far beyond the familiar fields of application in e-commerce, where ordering behavior, prices or purchasing decisions are supported by data.

These selected examples of data-driven business models show: It is not purely about more efficient value creation processes, but if data is the core element of a business model, it must be about more than the pure evaluation and use of data. Companies in all industries, not just manufacturing, must ask themselves how internal or external technologies and services can be used with existing or new data for data-driven business models: How disruptive such new value creation structures in ecosystems can be by means of new technologies and data, and what challenges accompany them, is different in every industry [MMH/2016].

In order not to miss the boat in the industry, the continuous monitoring and continuous monitoring and evaluation of technological developments and changes in its own ecosystem is absolutely essential. This is the only way to offer data-driven services better, more costeffectively, and possibly earlier than competitors. To do this, it is often necessary to fundamentally change value creation and value capture. In this change process of the business model, new tools and creative methods can help to establish data orientation [Accenture/2021].

It’s a real paradigm shift for companies that have previously taken a non-data-driven path, and this shift to a data-centric view is a key driver of digital transformation. In the future, every successful company must have a clear idea of what role data plays in its own business success. This data orientation will then be a central component of all areas of the company: Starting with the service offering, through the revenue model, the key resources, the value creation processes, the cost structures, the corporate culture, the customer and network orientation, and ultimately the entire corporate strategy. A company can only be sustainably successful in the future with a data-driven business model if data orientation takes place holistically and the effects on the entire business process are included [Fraunhofer/2022].

“It’s a broadening spectrum of data that we wish to use and analyse, and the range of sources is ever increasing. These range from regular feeds of live data as web services hosted by companies and agencies through to statistics, demographics, and risk datasets from an increasing number of third parties.” (Quote by Nigel Davis, Willis Group [Oxford/2014])

Data collection is defined by the way it is generated. In most industries, the data is collected from a variety of sources. For example, the retail (e-commerce) sector relies more heavily on its own transactional data than in direct comparison with the healthcare or insurance industries, which draw data from different data pots and often have a mix of open and proprietary data sources.

The following figure (see Fig. 1), based on a Study [Oxford/2014], shows an overview of the elements of “Big Data” and a data-driven business model nowadays and in future:


Fig. 1. Overview of Big Data elements (own representation based on [Oxford/2014]

2 Theoretical Background

2.1 Definition of the Data-Driven Organization (Overview)

A so-called data-driven organization is one that prioritizes data for decision making. Such organizations rely on data analytics and Big Data to drive their strategic and operational decisions, rather than just intuition or observation.

Data is considered the most important element in a data-driven organization, providing essential insights and aiding decision making. Decisions are made based on data analysis, interpretation and extrapolation, rather than on past experience.

The key factors of a data-driven enterprise are the data itself, the personnel who interpret the data, and the technology that captures, stores, and analyzes the data. The company uses data to track its own performance and measure its results, identify market trends early, and forecast future results.

Data-driven companies recognize the value of data and are investing significantly in tools and technologies that can help them capture and analyze data. In addition, they place great emphasis on hiring skilled personnel and developing young talent who can understand and interpert data.

There are various benefits that come with the shift to a data-driven organization. First, data can lead to more informed decision making, as decisions are based on actual facts rather than gut feelings. Second, it can lead to improved operational efficiency and performance improvement, as companies can use data analytics to identify trends, opportunities and areas for improvement, keeping service quality high. Finally, it can lead to a better understanding of customers and markets, as data analytics can provide insights into customer behavior, preferences, and trends.

But there are also some challenges to overcome on the road to a data-driven enterprise. These include privacy concerns, data security risks, and potential data quality or accuracy issues. In addition, it can be difficult to make the cultural shift required for a data-driven organization, as staff or customers throughout the company, as well as corporate environments, need to adopt a new mindset and often this causes skepticism among people.

To establish a data-driven business model, companies often employ strategies such as BigData analytics or the use of business intelligence (BI) tools and the application of data science methodologies, as well as developing strong data governance and embedding a culture of data literacy within the organization [McKinsey/2022].


2.2 Definition of the Big Data (Overview)

While many definitions exist for Big Data. Big Data refers to data that comes in great variety, in large volumes, and at high velocity. This is also known as the three V terms (Variety, Volume, Velocity). Big Dat refers to larger and more complex data sets, especially from new data sources. These data sets are so large that traditional data processing software cannot handle them. But with these massive volumes of data, you can tackle business problems you couldn’t solve before [Oracle/2022].


2.3 Meta-Analysis (Methodology)

According to the definition in the literature, meta-analysis [Glass/1976] or even a meta-meta-analysis [Cleophas-Zwinderman/2017] is a proven statistical method to quantitatively summarize and evaluate the results of different studies with a very similar question in a scientific research area.

Meta-analyses are based on empirical investigations or studies in which results and data are collected. These data results have usually already been statistically analyzed, for example to determine significance. Statistical tools are used to analyze the data from the individual studies and summarize them for comparison purposes so that overarching findings can be obtained. Meta-analyses usually aim to include as many comparable results as possible and to find out their variability.

They are used in various disciplines (e.g. medicine), especially in the context of systematic reviews, and are thus an important methodology for evidence-based medicine. The literature is then systematically evaluated for a specific question according to defined criteria.


2.4 Goals and Objectives

We are within a digital economy where data is more valuable than ever before. It’s the key to the functionality of everything from the government to local companies. Without it, progress would halt. Therefore, Data in the 21st Century is like Oil in the 18th Century: an immensely, untapped valuable asset. Like oil, for those who see Data’s fundamental value and learn to extract and use it there will be huge rewards.

This article presents current state of the art implementation guides in the context of a data-driven business models. The aim is to use concrete examples from practice to cast a “Blueprint” on the successful use of Big Data technology. For this purpose, various studies and online articles as well as white papers from renowned consulting companies (e.g. Oracle, Oxford, Cambridge, Fraunhofer, McKinsey, etc.) will be consulted.


3 Methods

Based on the findings of the theoretical background, made in chapter 2, the following research questions will be answered as output of the assessment with the data-driven business models (DDBM):

1) What are the key innovations that make the DDBM a strategic technology trend?

2) Which branches have the greatest impact of going to the DDBM?

3) What technological or regulatory hurdles are currently slowing down the DDBM?

To process the research questions, a broad literature search was first carried out. This was the basis for the following qualitative content analysis [Mayring/2015].

The current state of research and the theoretical foundations were determined through an extensive literature review and mainly online search sources (databases).

The literature search was carried out by using of the following data-bases:

     a) Google Search

     b) Google Scholar

     c) Web of Science

     d) ELSEVIER

     e) Springer Link

     f) SCOPUS

     g) JSTOR

The following search keywords were used as part of a targeted litera- ture search:

“Data driven business models” (> 6.43 Million results), “Big Data” (> 10.20 Million results), “Data Mining”, (> 5.77 Million results), “KI” (more than 5.60 Million results).

Subsequently, the results were further limited by the search-filters “AND” and “DATE”. The literature search identified 18.800 potential sources, 363 of which were identified as relevant sources. All sources that corresponded to the generally valid scientific requirement for the level of detail and quality of the elaboration were classified as relevant.

Finally, 18 sources were used for editing. These sources were published between 2014 and 2022 and are mainly from English or German-speaking areas. For the qualitative content analysis, only sources that meet scientific standards were used. The qualitative content analysis was carried out with the software MAXQDA. The codes and subcodes used have been developed deductively and are summarized in Figure 2:

Fig. 2. MAXQDA Code/Subcode Overview (own presentation)

4 Results of the META-Analysis

Data is everywhere. And that amount will only grow to increasingly monumental proportions in the near future. Already, the amount of data generated by modern businesses is scarcely conceivable. You can find it in every industry and every organization, at every scale.

The use of data can create real added value in many areas of a company. There is the potential to help reduce costs and increase profits, but also reduce risks. This return on investment also motivates further thinking about data-driven strategies and processes, and new ways of running a business.

And these benefits can actually be measured, making it easier to communicate or visualize to customers, management and staff. Moving to a data-driven approach enables any organization to respond more effectively to change and challenges.

The insights from real data, allow to look further into the future than companies would normally do in traditional business. Instead of relying on daily snapshots of the business, valid, measurable goals can be forecast for several years into the future. And this concept brings many benefits: On average, data-driven companies achieve growth Using AI to actively prevent cyberattacks on the enterprise allows you to limit the scope and impact of potential cyberattacks and identify potential vulnerabilities before they are abused. Better insight into your company’s data doesn’t just benefit you financially. It also helps you identify other opportunities, such as increasing diversity or more effectively pursuing sustainable business practices. To show you how your business can use data to add value of more than 30 percent per year.

Data-driven work practices can be applied at any level within an organization, helping to improve results across the value chain. Key decision-making processes can be optimized so that faster and better decisions are made because they are each supported with relevant data, preferably in real time. The combination of these insights from comprehensive data also enables the development of new and innovative business models within an industry. Using AI to actively prevent cyberattacks on the enterprise allows you to limit the scope and impact of potential cyberattacks and identify potential vulnerabilities before they are abused. Better insight into your company’s data doesn’t just benefit you financially. It also helps you identify other opportunities, such as increasing diversity or more effectively pursuing sustainable business practices [Accenture/2022].


4.1 Healthcare (Telemedicine)

Everything is digital, but healthcare is often a huge paper mess [Arthur Little/2021].

How does that impact the healthcare industry when the success requires the continuous pursuit of three aims:

1) QUALITY – guaranteeing the effectiveness of care for the patients.

2) ACCESS – providing proactive care or facilitating entry into the healthcare

3) EFFICIENCY – improving healthcare processes and reducing costs

The World Health Organization (WHO) defines eHealth as “the use of information and communication technologies for health,” while digital health is described more as an umbrella term covering areas including eHealth, telehealth, and other digital services. During the next decade, the healthcare industry will perform a transformation with many important technologies, including AI.

The Clinical workflows will be more agile by the use of AI and advanced analytics that automate decision-making processes. These technologies require a complete transformation from digital health to data-driven healthcare given the central role of data in automated decision making [Arthur-Little/2021].

Value-based healthcare as a solution for quality instead of quantity. These types of challenges are far more than a phenomenon of the German healthcare system, which is why Michael Porter presented a framework for restructuring healthcare systems in 2006, which he called Value-based Healthcare (VBHC). The overarching goal of this framework is to place the focus on value creation for patients in care.

Porter defines this value (value) of a treatment strategy for a patient as the ratio of the treatment result (outcome) for the patient and the costs of all resources that are necessary to achieve this outcome. In addition to the objective measures that have already been collected, such as mortality and new operation rates, the VBHC approach also counts the subjective ratings of the patients in the outcomes

The latter measurements are also called patient-reported outcome measures (PROMs) in a structured and standardized form.

When measuring the costs, all resources should really be taken into account, including costs for personnel, equipment, facilities, IT or administration [Brainwaves/2020].

The healthcare industry will face big changes in the next few years to transform the patient care. In this early revolution, trends such as integration of wearable healthcare devices, cloudification, and the incorporation of AI into healthcare is one big thing. These new technologies will generate a huge quantity of data that must be processed and secured to create value. [Alberto-Springer/2015]

Various stakeholders are involved in the transformation of the healthcare sector. Government can promote the implementation of integrated systems and knowledge sharing and individuals are sometimes critical to share data if they are unable to benefit from it directly [Arthur-Little/2021].

Digital health insights with the latest trends from startups and other innovative key-players can help other leaders in the healthcare sector to make better decisions of technology usage including artificial intelligence-based tools and systems such as clinical decision support, telehealth, and remote monitoring [HC-Transformers/2022].


5 Discussion – Data-driven business is not an option

Nowadays for many companies, their data infrastructure and IT services are still just a cost center and should become a profit center by using the data to improve the business itself. Companies must begin treating data as a holistic corporate asset while also managing the data within business units and beyond to scale business.

This sharing of data about products and customers is an enabler to gain opportunities to up sell, cross sell, improve customer service and retention rates. The use of internal data in combination with external data is a big performance indicator for many companies among branches to create new products and services across the existing business and for future success.

As your data business grows, there are many options and opinions about which path to take. It is advisable to work on the principle of “good data beats opinions”. There is hardly anything that cannot be tested, measured and or improved.

If you can measure it, you can improve it. Company personnel should be encouraged to test. Because every test is valuable. At least you get new insights into whether it works or not (and possibly why), and in the best case these insights help to improve the company’s results directly or indirectly.

In doing so, the company should ensure that the data is available in real time. It is not enough to simply know the total revenue, profit or costs. It is much more important to know which KPIs positively or negatively influence these and other business goals, because only then can companies improve their results in the long term. In the end, the value is in the micro data, not the macro data [Accenture/2022].


6 Conclusion

As the benefits of using Big Data continue to grow, companies are being forced to incorporate innovative data-driven solutions into their business models. The goal is to take advantage of opportunities or minimize risks in terms of competitiveness, market share and ultimately revenue [Cambridge/2015].

A data-driven business model enables companies to develop their own data strategy, tailored to their business and environment. Data has become invaluable to the success of most businesses. For most companies that want to grow sustainably or survive in the long term, it is therefore no longer a “can-do” question, but data-driven business management is a “must-have” and it is more a question of how and when this strategy is implemented [Cambridge/2014].



1. ACCENTURE, Accenture Dienstleistungen GmbH: „Why you need to capitalize on the rise of the data-driven enterprise“, Germany, Berlin (2021). Online available at:, last accessed 2021/06/11.

2. ACCENTURE, Accenture Dienstleistungen GmbH: „Business Futures 2021: Signal of Change“, Germany, Berlin (2022). Online available at:, last accessed 2021/06/11.

3. ALBERTO et al, Alberto Salido López, Carmelo Del Valle, María José Escalona et al, Patient Lifecycle Management: In Springer eBook (2015): „An Approach for Clinical Processes”. Online available at:, last accessed 2021/05/08.

4. ARTHUR-LITTLE, Arthur D. Little Services: „Analyzing the forces driving the transformation of healthcare”(2018), Online available at:, last accessed 2021/05/08.

5. BRAINWAVE, Brainwave Hub: „Value-Based Healthcare — Qualität statt Quantität” Germany, Berlin (2020): Online available at:, last accessed 2021/05/31.

6. CAMBRIDGE, Cambridge Service Alliance: „Data and Analytics – Data-Driven Business Models: A Blueprint for Innovation“, UK, London (2015). Online available at:, last accessed 2021/06/11.

7. CAMBRIDGE, Cambridge Service Alliance: „Big Data for Big Business? A Taxonomy of Data-driven Business Models used by Start-up Firms“, UK, London (2014). Online available at:, last accessed 2021/06/11.

8. CLEOPHAS, Ton J., ZWINDERMAN, Aeilko H.: “Meta-Meta-analysis. In: Modern Meta-Analysis: Review and Update of Methodologies”, Springer International Publishing, Cham, ISBN 978-3-319-55895-0, P. 135–143 (2017), DOI:10.1007/978-3-319-55895-0_11.

9. ELTON, Jeff/O’RIORDAN, Anne: “Healthcare Disrupted – Next Generation Business Models and Strategies”. Wiley, New Jersey (2016).

10. FRAUNHOFER, Fraunhofer Center for Applied Research SCS of the Fraunhofer IIS (Homepage): „Erfolgreiche zukünftige Geschäftsmodelle basieren auf Daten“, Germany, Nuremberg (2022). Online available at:, last accessed 2021/06/21.

11. HEALTHCARE-TRANSFORMERS, F. Hoffmann-La Roche Ltd: „Transforming the future of healthcare through digitization” (2022). Online available at:, last accessed 2022/05/08.

12. GLASS, Gene V.: Primary, “Secondary and Meta-Analysis of Research”, In: Educational Researcher 5, P. 3–8 (1976), DOI:10.3102/0013189X005010003 JSTOR:1174772.

13. MAYRING, Philipp: „Qualitative Inhaltsanalyse – Grundlage und Techniken“, 12., P. 50ff überarbeitete Auflage. Germany, Weinheim and Swiss, Basel: Beltz Verlag (2015).

14. McKINSEY, McKinsey & Company: „The data-driven enterprise of 2025“, USA, New York (2022). Online available at:, last accessed 2021/06/18.

15. MMH, Modern Materials Handling: „Make the Transition to Data-Driven Lifecycle Management (2021), USA, New York. Online available at:, last accessed 2022/05/12.

16. OXFORD, Oxford Internet Institute (Research-Study): „Data-Driven Business Models: Challenges and Opportunities of Big Data“, UK, Oxford (2014). Online available at: Modelschallenges.pdf, last accessed 2021/06/11.

17. ORACLE, Oracle Deutschland B.V. & Co. KG: „Was versteht manunter Big Data?“, Germany, Munich (2022). Online available at:,vor%20allem%20von%20neuen%20Datenquellen, last accessed 2022/06/18.

18. RADOVIC, Milos: „Data Driven Business: Von Insights über Relevanz hin zu Wachstum“ Germany, (2018). Online available at:, last accessed 2021/05/11.



Mr. Stefan Odenbach-Wanner, PhD-Student 1

Comenius University, Faculty of Management, Bratislava, Slovakia 

Doctoral Supervisor: Prof. Dr. Natalia Kryvinska, Comenius University, Faculty of Management, Bratislava, Slovakia



Ing. Jaroslav Vojtechovský, Phd. <>

Department of Information Management and Business Systems

Faculty of Management, Comenius University Bratislava

Odbojárov 10, 82005 Bratislava 25, Slovakia


prof. RNDr. Michal Greguš, PhD. <>

Univerzita Komenského, Fakulta managementu

Department of Information Management and Business Systems

Faculty of Management, Comenius University Bratislava

Odbojárov 10, 82005 Bratislava 25, Slovakia


Digital Science Magazine, Číslo 1, Ročník IX. ISSN: 1339-3782