====Future perspectives in enterprise modeling practices====


===INTRODUCTION===
Enterprise Modeling (EM), often referred to as Enterprise Architecture (EA), is the process of creating a visual representation of an organization's components, processes, data, and relationships in order to gain a better understanding, analyze, and optimize them. This process involves identifying and defining various elements, and creating models or diagrams to illustrate how these elements interact and relate to each other. Enterprise Modeling practices encompass the methods, techniques, and processes employed to generate organizational models, e.g. Business Process Modeling Notation (BPMN), Unified Modeling Language (UML), and Entity-Relationship Diagrams (ERD), which represent the organizational structures, processes, and data flows. This essay discusses the future perspectives in enterprise modeling practices, shedding light on key trends and potential opportunities.


===INDUSTRY 4.0 IMPACT===
The fourth industrial revolution, commonly known as Industry 4.0, is marked by the integration of digital technologies, data analytics, and automation into various industries. For instance, it disrupts all enterprise functions related to product distribution, commercialization, and the way enterprise interacts with clients, suppliers, and competitors. Enterprise modeling must adapt to the demands of this transformation. Some research detected that the element called I4.0 component (such components include various advanced technologies and concepts, such as IoT, AI, and robotics) impacts almost all of the Enterprise Architecture elements at all architecture perspectives [1]. 


===AGILITY IMPACT===
One of the most significant future perspectives in enterprise modeling is the emphasis on agility. Traditional models often struggled to keep pace with the ever-changing business environment, so agility is emerging as the norm for advanced enterprise models [2]. Agile modeling approaches, such as Agile, DevOps, and Continuous Integration/Continuous Deployment (CI/CD), are becoming increasingly essential. These methodologies allow organizations to quickly adapt to changing market conditions, identify opportunities, and address challenges efficiently. For instance, there is a new methodology for proposing an artifact to improve the organization’s EA modeling process called AgEAMM [3].


===DATA-DRIVEN DECISION-MAKING (DDDM) IMPACT===
The future of enterprise modeling is tied to data-driven decision-making. Data has become "the new oil", and enterprise modeling practices need to focus on using this resource effectively. Predictive modeling, machine learning, and artificial intelligence will be essential tools in transforming raw data into actionable insights. If organizations want to monetize their data, enterprise modeling should be changed to be able to provide data-driven business models (DDBM) [4][5].


===CLOUD-NATIVE IMPACT===
Cloud-Native Enterprise Modeling represents a pivotal advancement that aligns enterprise modeling with the dynamic and scalable capabilities offered by cloud computing. This concept marks a paradigm shift in how organizations design, deploy, and manage their modeling tools and frameworks. For example, Instagram was able to generate exponential business growth and value in a very short amount of years, but was hosted by Amazon Web Services without owning any data center or any noteworthy IT assets. It means that in the era of Cloud-Native Enterprise Modeling, the traditional constraints of infrastructure and IT ownership no longer define the limits of what an organization can achieve [6]. Embracing this new way of doing things will definitely change how companies work with models, helping them achieve greater success in the future.


===ARTIFICIAL INTELLIGENCE & MACHINE LEARNING IMPACT===
The integration of artificial intelligence (AI) and machine learning into enterprise modeling practices is changing how organizations capture, interpret, and act upon data. AI systems have the potential to autonomously generate and update enterprise models. The use of machine learning techniques in enterprise modeling is an emerging trend with great potential [7]. Additionally, enterprise modeling can contribute to the creation of machine learning projects [8], further advancing both enterprise modeling and AI practices.


===CONCLUSION===
In conclusion, the future of enterprise modeling practices is in front of significant transformation and evolution. With the Industry 4.0 revolution, agility, data-driven decision-making, cloud native solutions, and the integration of artificial intelligence and machine learning, organizations are on the cusp of a new era in understanding and optimizing their operations. These developments raise the adaptability, efficiency, and strategic decision-making of businesses, ultimately leading to greater success and competitiveness in the ever-changing environment of the modern enterprise. As technology continues to advance, embracing these future perspectives in enterprise modeling is not just a choice but a necessity for organizations to thrive.


===REFERENCES===
[1] Industry 4.0 Impact Propagation on Enterprise Architecture Models. Elena Kornyshova, Judith Barrios. https://www.sciencedirect.com/science/article/pii/S1877050920322377 Accessed: 22.10.2023

[2] Enterprise Modeling for Business Agility. Jennifer Horkoff, Manfred A. Jeusfeld, Jolita Ralyté & Dimitris Karagiannis. https://link.springer.com/article/10.1007/s12599-017-0515-z Accessed: 22.10.2023

[3] An Agile Approach for Modeling Enterprise Architectures. Petronio Medeiros, Alixandre Santana, Myllena Lima, Hermano Moura & Miguel Mira da Silva. https://www.scitepress.org/Papers/2021/104505/104505.pdf Accessed: 22.10.2023

[4] How does Enterprise Architecture support the Design and Realization of Data-Driven Business Models? An Empirical Study. Faisal Rashed & Paul Drews. https://www.researchgate.net/publication/355704887_How_Does_Enterprise_Architecture_Support_the_Design_and_Realization_of_Data-Driven_Business_Models_An_Empirical_Study Accessed: 22.10.2023

[5] Enterprise Architecture’s Role in Building a Data-Driven Organization. Ashutosh Gupta. https://www.gartner.com/smarterwithgartner/enterprise-architectures-role-in-building-a-data-driven-organization Accessed: 22.10.2023

[6] ClouNS - A Cloud-native Application Reference Model for Enterprise Architects Nane. Kratzke & Rene Peinl. https://www.researchgate.net/publication/304024156_ClouNS_-_A_Cloud-native_Application_Reference_Model_for_Enterprise_Architects Accessed: 22.10.2023

[7] Machine Learning-Based Enterprise Modeling Assistance: Approach and Potentials. Nikolay Shilov, Walaa Othman,  Michael Fellmann & Kurt Sandkuhl. https://www.researchgate.net/publication/356114747_Machine_Learning-Based_Enterprise_Modeling_Assistance_Approach_and_Potentials Accessed: 22.10.2023

[8] Enterprise Modeling for Machine Learning: Case-Based Analysis and Initial Framework Proposal. Dominik Bork, Panagiotis Papapetrou & Jelena Zdravkovic. https://link.springer.com/chapter/10.1007/978-3-031-33080-3_33 Accessed: 22.10.2023