Data Governance and Trust in Modern CRM Platforms: Strategies for Scalable Customer Data Management
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I3P117Keywords:
Data Governance, Customer Relationship Management (CRM), Customer Data Management, Data Quality, Data Trust, Data Privacy, Data Security, Master Data Management (MDM), Regulatory Compliance, Cloud CRM Platforms, Data Stewardship, Customer Trust Frameworks, Scalable Data Architecture, Data Lifecycle Management, Governance Frameworks, Metadata Management, Customer Experience, Data Integrity, Risk Management, Digital TransformationAbstract
Customer Relationship Management (CRM) systems have become the mainstay of customer-centric organizations, enabling them to collect, manage, and analyze customer information from multiple channels and touchpoints. As more companies are turning to data for their decision-making, high-quality, consistent, safe, and reliable customer data have not only become key factors that influence business outcomes and customer satisfaction, but also play a role in complying with regulations. In this regard, data governance and trust are crucial in ensuring that customer data remain correct, accessible, safe, and ethically handled throughout their lifecycle. This article explores how data governance structures and trust-enhancing practices are becoming part and parcel of CRM tools nowadays, most importantly concentrating on those methods that allow scalable handling of customer data even in rapidly changing business circumstances. The paper aims to identify primary governance measures, quantify their overall impact on data quality and organizational trust, and determine how today's businesses can maintain a balance between data openness and protection/privacy. A qualitative methodology supported by a comprehensive case study of a CRM system implementation has been the method of this work exploring governance policies, data stewardship frameworks, metadata handling procedures, access control mechanisms, and compliance tactics which are the main factors for successful customer data management. The findings indicate that organizations that articulate their governance frameworks clearly, standardize their data management processes, and publicly set up accountability lines, experience a substantial increase in the accuracy of data, operational efficiency, customer trust, and regulatory preparedness. Also, the study highlights the role of technologically advanced tools like AI, automation, and live monitoring in enhancing governance accuracy and customer data resources trust. By delivering strategic insights and scalable governance models, this paper supports the growing body of research on CRM data management and provides practical recommendations for business executives, data professionals, and technology partners to build dependable, strong, and future-proof CRM systems leading to continuous business success and enhanced customer relations.
References
1. Basireddy, S. R. (2024). Architecting trustworthy and scalable CRM intelligence with LLM-driven integration and zero trust governance. Journal of Artificial Intelligence, Machine Learning & Data Science, 3(2), 2988-2993.
2. Muppaneni, R. K. (2021). Securing the Enterprise: How Dynamics 365 Meets Global Compliance Standards. International Journal of Emerging Research in Engineering and Technology, 2(1), 133-143. https://doi.org/10.63282/3050-922X.IJERET-V2I1P114
3. Parakala, A. (2024). Agentic Automation: What’s next for Jobs. American International Journal of Computer Science and Technology, 6(6), 25-35. https://doi.org/10.63282/3117-5481/AIJCST-V6I6P103
4. Hamza, O., Collins, A., Eweje, A., & Babatunde, G. O. (2023). A unified framework for business system analysis and data governance: Integrating Salesforce CRM and Oracle BI for cross-industry applications. International Journal of Multidisciplinary Research and Growth Evaluation, 4(1), 653-667.
5. Gaddam, R. R. (2024). Vertex AI Agent Builder for Regulated Environments. American International Journal of Computer Science and Technology, 6(2), 50-62. https://doi.org/10.63282/3117-5481/AIJCST-V6I2P106
6. Seetala, S. R. (2021). Master data management as a strategic foundation for enterprise consistency: Frameworks, architectures, and governance practices. International Journal of Computer Technology and Electronics Communication, 4(1), 3230-3240.
7. Muppaneni, K. (2022). Comparative Analysis of Client-Side Storage Mechanisms. International Journal of AI, BigData, Computational and Management Studies, 3(1), 171-182. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I1P119
8. Suryadevara, S. S. K., & Nakirikanti, S. (2024). Blockchain-Backed Content Authenticity Verification Framework. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 242-252. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P125
9. Egbuhuzor, N. S., Ajayi, A. J., Akhigbe, E. E., Agbede, O. O., Ewim, C. P. M., & Ajiga, D. I. (2021). Cloud-based CRM systems: Revolutionizing customer engagement in the financial sector with artificial intelligence. International Journal of Science and Research Archive, 3(1), 215-234.
10. Vppalapati, M. (2022). The Storage Stack Nobody Draws: Cabling, Panels, and the Illusion of Isolation. International Journal of Emerging Research in Engineering and Technology, 3(2), 211-220. https://doi.org/10.63282/3050-922X.IJERET-V3I2P121
11. Allenki, S. S. (2024). Automating Backups and Recovery: Reducing Manual Work by Over 50%. International Journal of Emerging Research in Engineering and Technology, 5(1), 166-176. https://doi.org/10.63282/3050-922X.IJERET-V5I1P119
12. Gregory, A. (2011). Data governance Protecting and unleashing the value of your customer data assets: Stage 1: Understanding data governance and your current data management capability. Journal of direct, data and digital marketing practice, 12(3), 230-248.
13. Kumar Doodala, A. N. (2024). Service Virtualization for API-First development: A shift-Left Testing Strategy. American International Journal of Computer Science and Technology, 6(4), 50-58. https://doi.org/10.63282/3117-5481/AIJCST-V6I4P105
14. Katangoori, Sivadeep, and Anudeep Katangoori. "Data-Centric AI in the Era of Large Volumes: Improving Model Outcomes through Data Quality Engineering." American Journal of Data Science and Artificial Intelligence Innovations 3 (2023): 430-457.
15. Lebaea, R., Roshe, Y., Ntontela, S., & Thango, B. A. (2024). The role of data governance in ensuring system success and long-term IT performance: A systematic review.
16. Srigadde, B. R. (2023). Creating Object Quick Actions with Lightning Web Components. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(2), 167-180. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I2P118
17. Takkalapally, D. (2024). ShiftLeft-AI: Machine Learning Framework for Proactive Performance Assurance in CI/CD Pipelines. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(4), 285-296. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P126
18. Ileana, M., Petrov, P., & Milev, V. (2025). Optimizing customer experience by exploiting real-time data generated by IoT and leveraging distributed web systems in CRM systems. IoT, 6(2), 24.
19. Shiramalla, R. (2023). Optimizing Cross-Platform Enterprise Integrations Using Workato: A Case Study of Salesforce and Oracle SaaS Applications. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 232-243. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P124
20. Strengholt, P. (2020). Data Management at scale. " O'Reilly Media, Inc.".
21. Allenki, S. S. (2024). Building Scalable Data Replication Pipelines for Real-Time Analytics. American International Journal of Computer Science and Technology, 6(1), 71-81. https://doi.org/10.63282/3117-5481/AIJCST-V6I1P108
22. Srigadde, B. R. (2023). The Hidden Gem: Lightning Headless Component. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 244-254. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P125
23. Mallempati, A., & Jaladi, D. S. . (2023). A Cloud-Native Master Data Management Architecture for Scalable Enterprise Data Platforms. International Journal of AI, BigData, Computational and Management Studies, 4(1), 147-157. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I1P115
24. Muppaneni, K. (2022). Optimizing React Hooks for Efficient State and Side-Effect Management. American International Journal of Computer Science and Technology, 4(6), 44-55. https://doi.org/10.63282/3117-5481/AIJCST-V4I6P105
25. Vppalapati, M., & Talasila, P. K. (2022). Correlated Independence: Why Redundant Storage Systems Share the Same Fate. International Journal of Emerging Trends in Computer Science and Information Technology, 3(1), 169-179. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I1P119
26. Collins, A., Hamza, O., Eweje, A., & Babatunde, G. O. (2024). Challenges and solutions in data governance and privacy: A conceptual model for telecom and business intelligence systems. Telecom and Business Intelligence Journal, 9(3), 110-125.
27. Muppaneni, R. K. (2021). How Enterprises are Achieving 360° Customer Views with Dynamics 365. International Journal of AI, BigData, Computational and Management Studies, 2(2), 129-138. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I2P114
28. Ijomah, T. I., Idemudia, C., Eyo-Udo, N. L., & Anjorin, K. F. (2024). The role of big data analytics in customer relationship management: Strategies for improving customer engagement and retention. World Journal of Advanced Science and Technology, 6(1), 13-24.
29. Takkalapally, D., & Takkellapally, M. R. (2024). AI-SynPerf: Synthetic Data Intelligence Framework for 5G Mobile Performance Simulation. International Journal of Emerging Trends in Computer Science and Information Technology, 5(1), 182-194. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I1P118
30. Suryadevara, S. S. K. (2024). Resilient Multi-CDN Delivery Model Using AI-Based Traffic Switching for Global AEM Deployments. International Journal of Emerging Trends in Computer Science and Information Technology, 5(3), 191-200. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I3P119
31. Shaffi, S. M. (2022). Enterprise Content Management and Data Governance Policies and Procedures Manual. International Journal of Science and Research (IJSR).
32. Parakala, A. (2024). Self Learning Bots & Cloud Native Platforms. International Journal of Emerging Trends in Computer Science and Information Technology, 5(4), 132-141. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I4P114
33. Shiramalla, R. (2024). Secure Multi-Cloud API Orchestration between Salesforce, Oracle CPQ, and Azure. American International Journal of Computer Science and Technology, 6(3), 102-113. https://doi.org/10.63282/3117-5481/AIJCST-V6I3P108
34. Ogeawuchi, J. C., Akpe, O. E., Abayomi, A. A., Agboola, O. A., Ogbuefi, E. J. I. E. L. O., & Owoade, S. A. M. U. E. L. (2022). Systematic review of advanced data governance strategies for securing cloud-based data warehouses and pipelines. Iconic Research and Engineering Journals, 6(1), 784-794.
35. Katangoori, Sivadeep, and Anudeep Katangoori. "Intelligent ETL Orchestration With Reinforcement Learning and Bayesian Optimization." American Journal of Data Science and Artificial Intelligence Innovations 3 (2023): 458-488.
36. Kumar Doodala, Appala Nooka. “Validating UX Consistency Across Omnichannel Platform”. American International Journal of Computer Science and Technology, vol. 6, no. 6, Nov. 2024, pp. 87-97
37. Sarabu, V. B. (2018). Building foundational data integrity in enterprise retail systems: A structured approach to early-stage data governance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 1(1), 2457-2465.
38. Gaddam, R. R. (2023). Progressive Delivery for Models with Quality KPIs. American International Journal of Computer Science and Technology, 5(4), 33-47. https://doi.org/10.63282/3117-5481/AIJCST-V5I4P104
39. Buttle, F., & Maklan, S. (2019). Customer relationship management: concepts and technologies. Routledge.
40. Veershetty, G. (2025, June 11). Designing clean-core extension architectures for RISE with SAP using SAP BTP: A reference model and evaluation framework. SSRN. https://doi.org/10.2139/ssrn.6749501
