  As the most critical production factor in the era of the digital economy,
data will have a significant impact on social production and development.
Energy enterprises possess data that is interconnected with multiple
industries, characterized by diverse needs, sensitivity, and long-term nature.
The path to monetizing energy enterprises' data is challenging yet crucial.
This paper explores the game-theoretic aspects of the data monetization process
in energy enterprises by considering the relationships between enterprises and
trading platforms. We construct a class of game decision models and study their
equilibrium strategies. Our analysis shows that enterprises and platforms can
adjust respective benefits by regulating the wholesale price of data and the
intensity of data value mining to form a benign equilibrium state. Furthermore,
by integrating nonlinear dynamical theory, we discuss the dynamic
characteristics present in multi-period repeated game processes. We find that
decision-makers should keep the adjustment parameters and initial states within
reasonable ranges in multi-period dynamic decision-making to avoid market
failure. Finally, based on the theoretical and numerical analysis, we provide
decision insights and recommendations for enterprise decision-making to
facilitate data monetization through strategic interactions with trading
platforms.
