Intertwined gears representing data sharing between competing businesses.

Data Sharing Dilemma: Can Competitors Collaborate for Mutual Gain?

Theo Raines in Business & Economy September 2025 • 4 min read.

"Unlocking the potential of strategic data sharing in competitive markets—a guide for businesses seeking collaborative advantages."

In today's data-driven world, businesses increasingly recognize the value of machine learning (ML) for gaining a competitive edge. However, access to data, a critical ingredient for effective ML, is often fragmented across different firms. This has led to a growing interest in data sharing, where companies collaborate to pool their resources and improve their predictive capabilities.

The concept of data sharing raises a fundamental question: Why would competitors willingly share their valuable information? This is where strategic decision-making comes into play. Firms must carefully weigh the potential benefits of collaboration against the risks of revealing sensitive data to rivals. The decision becomes even more complex when considering the different stages of ML: the training phase (where models are built) and the inference phase (where models are used to make predictions).

Recent research delves into these very questions, examining the conditions under which data sharing agreements can be mutually beneficial for competing firms. By analyzing various scenarios and game-theoretic models, the study sheds light on the optimal strategies for data sharing, paving the way for more informed and collaborative business practices.

The Challenge of Sharing: Training vs. Inference

Intertwined gears representing data sharing between competing businesses.

One of the key insights is the distinction between sharing data during the training phase versus the inference phase. Sharing training data allows firms to build better prediction models by leveraging a larger and more diverse dataset. However, it also reveals information about the firm's internal data and model-building techniques.

Sharing inference-time predictions, on the other hand, allows firms to improve their predictions on new instances by incorporating insights from their competitors. This type of sharing is less revealing about the firm's internal data but still carries the risk of competitors free-riding on their efforts.

Training Phase: Focuses on improving model accuracy by sharing labeled historical data.
Inference Phase: Enhances predictions on new instances by sharing real-time predictions.
Full Sharing: Combines both training and inference data sharing for maximum potential benefit.

The optimal data sharing strategy depends on a variety of factors, including the competitive landscape, the nature of the data, and the firms' individual capabilities. To better understand these dynamics, researchers have developed a Bayesian framework that captures the key elements of data sharing.

Navigating the Future of Data Collaboration

As machine learning becomes increasingly distributed, data sharing will likely become a more common practice. By carefully considering the strategic implications of different data sharing models, businesses can unlock new opportunities for innovation and growth. The key lies in finding the right balance between collaboration and competition, ensuring that data sharing agreements are both mutually beneficial and sustainable in the long run.

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This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2403.17515,

Title: Prediction-Sharing During Training And Inference

Subject: econ.th cs.ai cs.gt cs.lg cs.ma

Authors: Yotam Gafni, Ronen Gradwohl, Moshe Tennenholtz

Published: 26-03-2024

Everything You Need To Know

Why would competitors consider sharing data, and what are the potential benefits?

Competitors might consider sharing data to gain mutual benefits, enhance predictions, and develop new competitive strategies. The core benefit lies in leveraging data sharing for machine learning (ML). By collaborating and pooling resources, firms can improve their predictive capabilities. Sharing data, especially during the training phase, allows firms to build more accurate ML models by using a larger and more diverse dataset. Further, sharing data can foster innovation and growth, making data sharing agreements mutually beneficial and sustainable.

What's the difference between sharing data during the training phase versus the inference phase, and what are the trade-offs?

The distinction between the training phase and the inference phase is crucial in data sharing. Sharing data during the training phase involves sharing labeled historical data to build better prediction models. This reveals insights into a firm's internal data and model-building techniques. Sharing data during the inference phase involves sharing real-time predictions to improve predictions on new instances. This is less revealing but carries the risk of competitors free-riding on your efforts. Each approach presents a unique set of trade-offs, influencing the strategic decision-making process for firms considering data sharing agreements.

What is 'Full Sharing' in the context of data collaboration, and what does it entail?

In the context of data collaboration, 'Full Sharing' combines both the training and inference data sharing strategies. It represents the most comprehensive approach to data sharing, where firms share labeled historical data for training and real-time predictions for inference. This strategy offers the maximum potential benefits, but it also comes with the highest level of data exposure and requires a careful consideration of the competitive landscape and potential risks. Successful implementation of full sharing depends on finding the right balance between collaboration and competition.

How do the competitive landscape, the nature of the data, and a firm's individual capabilities impact the optimal data-sharing strategy?

The optimal data sharing strategy is influenced by several factors. The competitive landscape, which includes the number of competitors and their market positions, influences the potential benefits and risks of data sharing. The nature of the data, such as its sensitivity and the types of insights it provides, affects the willingness of firms to share it. A firm's individual capabilities, including its ML expertise and data analysis skills, determine its ability to leverage shared data effectively. Understanding these elements helps firms tailor their data-sharing agreements to maximize benefits while mitigating risks.

What is a 'Bayesian framework' in relation to data sharing, and how does it help businesses?

A Bayesian framework is a tool that researchers use to capture the key elements of data sharing. It allows businesses to better understand the dynamics involved in data sharing agreements. It can model and analyze the conditions under which data sharing can be mutually beneficial for competing firms. The framework considers the competitive landscape, the nature of the data, and the firms' individual capabilities. By using this framework, businesses can make more informed decisions about data sharing, paving the way for more collaborative and strategic business practices.