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Article: Tuesday, 9 January 2024

Data has evolved; it’s no longer a by-product of business operations but now a vital asset with immense potential value – but how much is a dataset worth? That depends on its quality, utility, demand, privacy, and security. So far there have been no standardised guidelines for pricing until a new framework developed by David Lensen (MSc Business Information Management 2023), and Professor of Digital Business Ting Li and Dr Sameer Mehta, from Rotterdam School of Management, Erasmus University (RSM). Their comprehensive framework evaluates monetisable data, and it was developed through rigorous academic research – including David’s award-winning master thesis. David produced a practical tool to help companies navigate the complexities of data monetisation, ensuring they can ethically and effectively capitalise on their data assets. David’s findings are presented in his master thesis What is my data worth? The development of a conceptual model for determining pricing of data, which has won one of four Professor Jo van Nunen Awards for 2023.

“Our journey into the complex world of data monetisation has led us to uncharted territories, uncovered fascinating insights, and culminated in a comprehensive framework for evaluating data's monetary value. The groundbreaking research we present is a starting point. It has the potential to reshape our understanding of data value and, in turn, our strategies for monetising data assets,” says David Lensen

“This has powerful implications for data providers, guiding strategic decisions on data management and monetisation. Our framework is not just an endpoint; it is a launchpad for collective exploration, refinement, and application.” He suggested the global community should leverage this tool, and share their experiences to propel the data economy forward.

David Lensen

MSc Business Information Management graduate 2023 and 
Jo van Nunen Award Winner 2023

Putting a price on it 

Data is now considered essential for a great deal of innovation and strategic decision-making in business, and as the researchers have shown, a dataset’s price is determined by its quality, utility, demand, privacy, and security. But it’s still a very complex process to price it because there are no standardized guidelines to govern data pricing, so there’s often opacity and ambiguity. And this obstructs the smooth functioning of the data economy, say the researchers. Furthermore, this lack of transparency can actually stifle innovation, especially in the scientific community where open access to data is paramount.

So, the researchers set out to design a framework. They conducted interviews, studied business cases, and conducted a broad survey of 150 data science professionals. A framework for pricing datasets is becoming a necessity for any business that uses data-driven decision-making. It could revolutionise data transactions, making them more efficient and equitable.

We were particularly interested in evaluating the framework's accuracy, comprehensiveness, clarity, and usefulness, and the results were illuminating

The five-part framework

The data pricing framework has five categories that consider quantitative and qualitative aspects of data valuation. It’s also a dynamic model that can be adapted to specific scenarios so it can be used in diverse contexts.

1. Data characteristics

Data characteristics – a measure of the intrinsic qualities of the data;

2. Market factors

Market factors – capture the economic influence on the data's worth;

3. Compliance with regulatory

Compliance with regulatory and governance-related factors – these are the legal and ethical dimensions of data handling that affect the data’s value;

4. Provider and user factors

Provider and user factors – aspects related to the source and usage of the data;

5. Cost factors

Cost factors – account for the financial outlay associated with data collection, processing, and storage.

A shift in focus to quality

The survey of 150 data science professionals was the final stage after the researchers had developed and refined the framework. “We were particularly interested in evaluating the framework's accuracy, comprehensiveness, clarity, and usefulness, and the results were illuminating,” said Prof. Ting Li.

Overwhelmingly, participants found the framework to be clear, comprehensive, and easy to use. They liked the inclusion of qualitative and quantitative parameters, and the visual representation that helped them to easily understand the information.

But what was more interesting was what the survey revealed about the relative importance of the five parameters. The researchers expected data quantity and data rarity to be seen as significant, but the data science professionals they talked to also valued factors such as data quality and data security. “This underscores the shifting focus in the data science community from sheer data quantity to data quality,” says Prof. Li.


Suggestions for improvement 

Insights from the survey were instrumental in fine-tuning the framework and provided valuable direction for its further development and use. Data science professionals suggested minor modifications to the framework: more clearly defining the factors, and including additional factors or subfactors under each parameter. “Most of these suggestions were already implicitly covered within our framework or the full report, confirming its comprehensiveness,” said David

The data science professionals also suggested focusing on a more practical approach in applying the framework in future – like comparing actual datasets for pricing.


Pivotal implications and benefits

The key findings have implications that could be pivotal for the monetization of data. There could be ‘a new era of understanding, evaluating, and capitalizing on data assets across multiple contexts’ suggests dr. Sameer Mehta

“Our framework, which is bolstered by the acceptance of the data science community, offers a systematic approach to assess the value of data and its potential market price. It means we now have a holistic comprehension of the value of data, and it’s possible to guide strategic decision-making linked to data management and monetization. It’s ready for real-world deployment.”


Prioritise quality 

There is already a demand for the most valuable datasets that have a large quantity of high-quality data with extensive applicability. To escalate the value of the data, the researchers urged data providers to prioritize:

  • Data quality and quantity management;
  • Uncovering novel data applications;
  • Managing data for privacy and security, especially in sectors dealing with sensitive data;
  • Optimise data usability, trustworthiness, and perceived value.

While the framework is effective and adaptable and ready for practical use, there are still some refinements to be made for dealing with unique dataset characteristics and contexts.

“Lastly, our research reveals that data is multifaceted and is affected by the interplay of internal and external factors. Now that we know this, we need a nuanced understanding of the value of data to be able to make strategies that are effective. Data providers need to account for a broad array of factors based on their data's specific characteristics and context.”

“Our research paves the way for a more structured, informed, and efficient approach to data monetization, benefiting organizations, individuals, and the broader data science community,” said David
 

“Apply it, test it, refine it” 

The researchers’ framework offers a robust and validated tool for assessing data's market price and is a springboard for further exploration and refinement, tailored to the unique characteristics and contexts of different datasets. They invite the global data science community, industry practitioners, and researchers to leverage the framework in their respective fields. “Apply it, test it, refine it,” they say. “Let its insights guide your strategies for managing and monetizing your data assets. At the same time, we call on you to share your experiences and insights gained from using the framework. This will allow us to collectively refine and enhance its effectiveness, ensuring it remains relevant and valuable in a fast-evolving digital landscape.

“Together, we can navigate the complex world of data monetization, unlocking the untapped value of our data assets and shaping a more efficient, transparent, and equitable data economy. This is not just an opportunity; it's an imperative. The future of data monetization is in our hands. Let's shape it together.”

Prof. dr. T. (Ting) Li
Professor of Digital Business
Rotterdam School of Management (RSM)
Erasmus University Rotterdam
Photo
Ting Li
Dr. (Sameer) Mehta
Assistant Professor
Rotterdam School of Management (RSM)
Erasmus University Rotterdam
Photo
Sameer Mehta

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