Data strategy in connected business models

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Note: This blog post summarizes the article “Data strategy in connected business models”

Available at: https://www.henrystewartpublications.com/jdb/v8

Recommended Citation: Berentzen, Christoph & Schaefer, Benjamin, 2023. “Data strategy in connected business models,” Journal of Digital Banking, Henry Stewart Publications, vol. 8(3), pages 210-219.

INTRODUCTION

From 2019 to 2024, the global volume of digital data generated or replicated annually almost quadrupled – a trend expected to persist (Tenzer, 2024). The proliferation of devices and contact points has resulted in diverse data formats, essential for corporates to develop and enhance their business models. Tech companies demonstrate how data drives revenue, often overshadowing physical assets. Ignoring data can significantly disadvantage corporates, leaving them behind data-driven competitors. Effective data management offers numerous benefits, including improved product sales, better customer understanding, cost savings, enhanced security, customer experience, loyalty, and potential future uses. Advances in information technology, especially AI, facilitate the analysis of vast data amounts, promoting the importance of data collection and analysis. However, many corporates fail to utilize most of their data, facing issues like data breaches and data silos (DalleMule & Davenport, 2017). The key is to develop and implement data strategies which aim to create value from data for stakeholders by extracting, standardizing, storing, organizing, governing, analyzing, and deploying information assets (DalleMule & Davenport, 2017). Data strategies constitute a way of thought and leadership in corporates, with data as the basis of corporate activities. One of the possible building blocks to drive and utilize from established data management is implementing an adequate application programming interface (API) which aims the secure and flexible use of data, enhancing customer experiences and interactions across businesses. APIs can shape the future of data management and may strongly influence the way various businesses can interact with each other and provide value-creating services based on the information that can be already accessed.

CONCEPTUAL BACKGROUND

Data strategies are divided into defensive and offensive approaches (DalleMule & Davenport, 2017). Defensive strategies focus on mitigating risks by controlling data, ensuring reliable storage, regulatory compliance, and free data flow within the corporation (DalleMule & Davenport, 2017). Offensive strategies enhance competitive advantage and profitability through advanced data techniques like analytics and modeling (DalleMule & Davenport, 2017).

Figure 1 Overview of data strategies
Source: Adapted from DalleMule and Davenport (2017)

Both strategies must be simultaneously exploited for maximum benefit, with a foundation built on defensive strategies before advancing to offensive ones. As data volumes increase, corporations and financial institutions use APIs to access both internal and external data sources. APIs constitute virtual software interfaces that enable the rule-based exchange of information between two or more disparate and non-interconnected systems (Software Engineering Institute, 2003). Through an API, different Information provided in a variety of softwareare accessible, transferrable, and thus replicable to other information systems. Among many reasons for the evolution of APIs are applications built by separate teams of developers using different programming languages, eventually impeding communication between systems.

Figure 2 Application programming interfaces (APIs)
Source: Commerzbank AG (2023).
RELEVANCE OF DATA STRATEGIES FOR DIGITAL CORPORATES

In the modern business world, two types of corporates are noticeable: those that consider themselves to be data-driven and those that are in a prime position to generate value from data they can already access but are not yet fully data-driven. The first type, data-driven corporates, increasingly develop entire business models based on data monetization, moving beyond mere data processing and analysis. Prominent examples include big tech companies like Google, Apple, Microsoft, and Amazon, which rank among the world’s most valuable firms due to their IT-driven, innovative, and customer-centric business models and participation in emerging business ecosystems (Bean, 2021). Corporates that are not yet data-driven need to accept the new data-driven world and must build the required expertise to make internal data utilizable and generate new business opportunities. Before deciding to operate in a data-driven world, appropriate business opportunities arising from the use of data must always first be identified along the corporate dimensions. Interestingly, many product-based business models are evolving towards service models that generate further data to optimize and expand future product offerings.

Several corporates are already utilizing data on a large scale to their advantage. Tesla is a remarkable example of a corporation operating in an increasingly data-driven business. Tesla vehicles generate vast amounts of data, such as road conditions, which are processed and analyzed to improve autonomous driving. Initially used for internal purposes, this data will become invaluable to other vehicle manufacturers for training autonomous driving AI models once legal conditions allow (Marr, 2021). Traditional industries, such as aviation, are increasingly monetizing data, with airlines using data from customer loyalty programs to enhance their business models (Mwanalushi, 2019). Incumbents such as Audi AG have adopted a similarly flexible, data-driven initiative like Tesla by offering features on demand, allowing customers to activate, for example, more powerful headlights or a parking assistant, by subscribing to an equivalent package (Audi AG, 2023). Although numerous examples exist of corporates that have already discovered how to benefit from data, most corporates are currently in the middle of upheaval to become more digital and eventually data driven. Among them are various corporates, from small and medium enterprises (SMEs) to established corporates.

RELEVANCE OF DATA STRATEGIES FOR FINANCIAL INSTITUTIONS

Financial institutions are uniquely situated for developing and implementing data strategies given their extensive customer data, which has been collected for years — however, they have yet to be exploited to their great advantage. Recent advancements in information technology and the availability of external data sources enable them to push their data’s value, and the potential spheres of added value creation are tremendous. With an ever-increasing range of technological advances available, the amount of data financial institutions can access is rising exponentially. Considering their homogeneous product ranges, however, financial institutions must revise their strategy to differentiate themselves from customer-centric product offerings in the competitive market (Al‐Hawari & Ward, 2006). In this context, financial institutions that build core competencies in, for instance, analytics and prediction models, can maintain competitive advantages. For example, predictive analytics enables financial institutions to foster individualized and targeted products and services (Chintamaneni, 2016). Prospectively, financial institutions will have to anticipate the future needs of their private and corporate customers on a data-driven and event-driven basis while offering relevant services in real time. Further, data can serve to establish fraud detection mechanisms against internal or external threats (Chintamaneni, 2016).

THE VALUE OF A DATA STRATEGY IN BUSINESS NETWORKS

With the emergence of ecosystems and platforms, data strategies are becoming increasingly vital, as interactions between individuals and corporates generate vast volumes of data that corporates can leverage (Gawer, 2022). Further, the continuous evolution of interconnected architectures and technological advancements, such as new devices, enable corporates to foster data sharing and collaboration. Beneficially, assets and resources for the comprehensive offering of products and services thus no longer need to be provided solely: they can be supplemented remotely by other corporates participating within the ecosystem or platform (Gawer, 2022). To a significant extent, demand is shifting towards comprehensive rather than stand-alone products and services, requiring corporates to expand operations and link with other corporates in ecosystems to capture consumers’ attention. The increasing shift from a single product to product-as-a-service offerings and digital ecosystems drives the relevancy of data even further. With the rise of collaborations in ecosystem structures, financial institutions have a promising role to play. Financial institutions have the power and ability to enable ecosystems by using their data strategy in combination with standardized APIs focusing on their core competencies: providing essential payment services ‘behind the scenes’ to assure frictionless operation within the ecosystem. That said, financial institutions can also operate as providers of vital technical and financial solutions within ecosystems.

DATA CHALLENGES

Data growth poses challenges, such as handling unstructured data from new sources and integrating different data formats. The combination of ever-increasing data and varying formats is a mixed blessing for corporates: on the one hand, integrating this data requires a sophisticated data management approach, and on the other hand, mastering this situation inevitably lays the foundation for data-driven value creation (DalleMule & Davenport, 2017). Undoubtedly, the challenges identified also affect the financial institutions sector, characterized by customer relationships built on high trust fundamentals. The enormous quantity of available data largely consists of sensitive customer data yet to be harnessed to its full potential. Technical barriers need consideration, as some companies are in an unfavorable technical state because their IT systems have failed evolve over recent decades, resulting in data silos. (Bean, 2021).

CONCLUSION

Ecosystems, IoT and as-a-service products determine the growing markets. Growing competitiveness and crowded markets, however, fueled by diminishing transaction costs, hamper the ability of corporates to establish a presence in the market. Data strategies are becoming vital for corporates and financial institutions to succeed in today’s data-driven environment. Appropriate data management within the organization and beyond is essential to participate in novel business models and collaborative set-ups. Corporates and financial institutions need to initiate the allocation of resources to data management initiatives to respond to diverging market conditions. Financial institutions can position themselves as providers of vital technical solutions while leveraging their established data strategy to enhance customer experience with innovative products. Standardized data management and interfaces, such as APIs, will significantly help corporates and financial institutions to be positioned sustainably in the long term.

For further information on the decomposition of generic data strategies into more detailed options for companies, please refer to the “Data Value Creation Matrix” developed as part of the Competence Center Ecosystems via this link (Kakuschke, 2024).

References

Al‐Hawari, M., & Ward, T. (2006). The effect of automated service quality on Australian banks’ financial performance and the mediating role of customer satisfaction. Marrketing Intelligence & Planning, 24(2), 127-147. doi:10.1108/02634500610653991

Audi AG. (2023). Functions on Demand. Retrieved from Audi: https://www.audi.de/de/brand/de/service-zubehoer/audi-digital-service/functions-on-demand.html

Bean, R. (2021). Why Is It So Hard to Become a Data-Driven Company? Retrieved from Harvard Business Review: https://hbr.org/2021/02/why-is-it-so-hard-to-become-a-data-driven-company

Chintamaneni, P. (2016). How Banks Are Capitalizing on a New Wave of Big Data and Analytics. Retrieved from Harvard Business Review: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwimnZrqiqGGAxWUgv0HHXk0DlwQFnoECA4QAw&url=https%3A%2F%2Fhbr.org%2Fsponsored%2F2016%2F11%2Fhow-banks-are-capitalizing-on-a-new-wave-of-big-data-and-analytics%23%3A~%3At

DalleMule, L., & Davenport, T. (2017). What’s Your Data Strategy? Harvard Business Review. Retrieved from What’s Your Data Strategy?: https://hbr.org/2017/05/whats-your-data-strategy

Gawer, A. (2022). Digital platforms and ecosystems: Remarks on the dominant organizational forms of the digital age. Organization & Management, 24(1), 110-124. doi:10.1080/14479338.2021.1965888

Kakuschke, N. (2024). Data Value Creation Matrix — Options for Organizations to Create Value from Data. ECIS 2024 Proceedings. 9.

Marr, B. (2021). Data Strategy: How to Profit from a World of Big Data, Analytics and Artificial Intelligence. London: Kogan Page Publishers.

Mazzei, M., & Noble, D. (2017). Big data dreams: A framework for corporate strategy. Business Horizons, 60(3), 405-414. doi:10.1016/j.bushor.2017.01.010

Mwanalushi, K. (2019). Opportunity rising: How the airline industry is monetising data. Retrieved from Aviation business news: https://www.aviationbusinessnews.com/low-cost/airline-industry-data-monetisation/

Software Engineering Institute. (2003). Application Programming Interfaces. Carnegie Mellon University.

Tenzer, F. (2024). Statista. Retrieved from Volumen der jährlich generierten/replizierten digitalen Datenmenge weltweit von 2010 bis 2022 und Prognose bis 2027: https://de.statista.com/statistik/daten/studie/267974/umfrage/prognose-zum-weltweit-generierten-datenvolumen

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