Data Mesh in Action: Case Studies from Leading Enterprises
Keywords:
Data Mesh, Data Architecture, DecentralizationAbstract
As data grows in scale and complexity, enterprises have been forced to reevaluate their data architectures. One solution that has emerged as a game-changer is the Data Mesh, which encourages a shift from centralized data management to a more decentralized, domain-oriented approach. Instead of having a single, monolithic data team handle all data across the organization, the Data Mesh advocates treating data as a product, with individual teams owning & managing their respective data domains. This approach is particularly relevant for large organizations needing help with data governance, scalability, and quality issues. In this article, we explore how leading enterprises have embraced the Data Mesh, diving into case studies highlighting the challenges and successes of implementing this architecture. Through these real-world examples, we uncover how organizations have tackled critical issues such as data silos, inconsistent data quality, & the complexity of managing vast data sets. We also look at how decentralizing data ownership has empowered teams to be more agile, improve data accessibility, and reduce bottlenecks that typically arise with a traditional centralized model. These case studies demonstrate that, while adopting a Data Mesh requires significant changes in both culture and technology, the rewards include Greater scalability, More effective data governance & A more flexible and efficient way to handle data across the enterprise. Ultimately, the Data Mesh is proving to be a viable solution for companies seeking to break free from the limitations of traditional data architectures and adapt to the demands of modern, data-driven businesses.
Downloads
References
Feigenbaum, L., Herman, I., Hongsermeier, T., Neumann, E., & Stephens, S. (2007). The semantic web in action. Scientific American, 297(6), 90-97.
Vlachos, I. (2015). Applying lean thinking in the food supply chains: a case study. Production Planning & Control, 26(16), 1351-1367.
Gansky, L. (2010). The mesh: Why the future of business is sharing. Portfolio Penguin.
Bakıcı, T., Almirall, E., & Wareham, J. (2013). A smart city initiative: the case of Barcelona. Journal of the knowledge economy, 4, 135-148.
Garrido-Hidalgo, C., Hortelano, D., Roda-Sanchez, L., Olivares, T., Ruiz, M. C., & Lopez, V. (2018). IoT heterogeneous mesh network deployment for human-in-the-loop challenges towards a social and sustainable Industry 4.0. Ieee Access, 6, 28417-28437.
van Veen-Dirks, P., & Wijn, M. (2002). Strategic control: meshing critical success factors with the balanced scorecard. Long range planning, 35(4), 407-427.
Arayici, Y. (2007). An approach for real world data modelling with the 3D terrestrial laser scanner for built environment. Automation in construction, 16(6), 816-829.
Yang, S. Q., Chen, M., Jing, H. W., Chen, K. F., & Meng, B. (2017). A case study on large deformation failure mechanism of deep soft rock roadway in Xin'An coal mine, China. Engineering Geology, 217, 89-101.
Sahni, Y., Cao, J., Zhang, S., & Yang, L. (2017). Edge mesh: A new paradigm to enable distributed intelligence in internet of things. IEEE access, 5, 16441-16458.
Trebat, T. J. (1983). Brazil's state-owned enterprises: a case study of the state as entrepreneur (No. 45). Cambridge University Press.
Duening, T. N., & Click, R. L. (2005). Essentials of business process outsourcing. John Wiley & Sons.
Marques, J. P., Caraça, J. M., & Diz, H. (2006). How can university–industry–government interactions change the innovation scenario in Portugal?—the case of the University of Coimbra. Technovation, 26(4), 534-542.
.Bahr, M. (2006, August). Proposed routing for IEEE 802.11 s WLAN mesh networks. In Proceedings of the 2nd annual international workshop on Wireless internet (pp. 5-es).
Zhang, Y., Zhang, G., Chen, H., Porter, A. L., Zhu, D., & Lu, J. (2016). Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research. Technological forecasting and social change, 105, 179-191.
Jaeger, J. A., Schwarz-von Raumer, H. G., Esswein, H., Müller, M., & Schmidt-Lüttmann, M. (2007). Time series of landscape fragmentation caused by transportation infrastructure and urban development: a case study from Baden-Württemberg, Germany. Ecology and Society, 12(1).
Gade, K. R. (2020). Data Mesh Architecture: A Scalable and Resilient Approach to Data Management. Innovative Computer Sciences Journal, 6(1).
Gade, K. R. (2020). Data Analytics: Data Privacy, Data Ethics, Data Monetization. MZ Computing Journal, 1(1).
Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
Thumburu, S. K. R. (2020). Integrating SAP with EDI: Strategies and Insights. MZ Computing Journal, 1(1).
Thumburu, S. K. R. (2020). Interfacing Legacy Systems with Modern EDI Solutions: Strategies and Techniques. MZ Computing Journal, 1(1).
