Bridging the Gap: How DevOps and Product Managers Can Drive Continuous Innovation
DOI:
https://doi.org/10.53555/ephijse.v8i2.270Keywords:
DevOps collaboration, product management in software development, continuous delivery, agile product development, DevOps cultureAbstract
Two mainstones in the ever changing area of software development that ensure products advance and give value to users are DevOps teams and product managers (PMs). Still, most of these teams have alignment problems that lead to delays, inefficiencies, and irritation on both sides.Whereas DevOps stresses dependability, automation, and fast deployment methods, Product Managers focus on customer needs, feature innovation, and business growth. Different priorities could cause a misalignment that disturbs development cycles and affects general product success. Companies looking for improved software dependability, faster time-to--market, and best feature delivery have to combine DevOps with Product Management. Good teamwork helps early alignment between technical performance and business goals, hence lowering ambiguity and problems. This paper analyzes the main obstacles in DevOps-PM cooperation, including misaligned priorities and inadequate communication, as described below and offers many concrete strategies for strengthening teamwork. Good cooperation ensures early alignment between technical performance and company goals, hence lowering ambiguity and problems. This paper addresses several reasonable recommendations for enhancing teamwork as well as the primary difficulties in DevOps-PM cooperation—including improper priorities and inadequate communication—as stated below. Real-time communication tools, cross-functional teams, and shared KPIs let companies establish a consistent approach combining operational excellence with product innovation. A case study reveals how a SaaS company reduced their time-to----market by 30% so proving the obvious benefits of better collaboration by including Product Managers into DevOps planning. The continuous change of the software industry depends on the cooperation between DevOps and Product Management, thereby defining if modern product development is successful.Companies who give agility, openness, and teamwork top priority will create better goods and get a competitive edge in a world going more and more digital.
References
Immaneni, J. "Cloud Migration for Fintech: How Kubernetes Enables Multi-Cloud Success." Innovative Computer Sciences Journal 6.1 (2020).
Immaneni, Jayaram. "Using Swarm Intelligence and Graph Databases Together for Advanced Fraud Detection." Journal of Big Data and Smart Systems 1.1 (2020).
Immaneni, Jayaram. "Using Swarm Intelligence and Graph Databases for Real-Time Fraud Detection." Journal of Computational Innovation 1.1 (2021).
Immaneni, Jayaram. "Scaling Machine Learning in Fintech with Kubernetes." International Journal of Digital Innovation 2.1 (2021).
Immaneni, Jayaram. "Securing Fintech with DevSecOps: Scaling DevOps with Compliance in Mind." Journal of Big Data and Smart Systems 2.1 (2021).
Shaik, Babulal. "Leveraging AI for Proactive Fault Detection in Amazon EKS Clusters." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 894-09.
Shaik, Babulal. "Cloud Cost Monitoring Strategies for Large-Scale Amazon EKS Clusters." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 910-28.
Shaik, Babulal. "Integrating Service Meshes in Amazon EKS for Multi-Environment Deployments." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 1315-32.
Shaik, Babulal. "Evaluating Kubernetes Pod Scaling Techniques for Event-Driven Applications." Distrib Learn Broad Appl Sci Res 5 (2019): 1333-1350.
Shaik, Babulal, and Karthik Allam. "Comparative Analysis of Self-Hosted Kubernetes Vs. Amazon EKS for Startups." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 1351-68.
Shaik, Babulal. "Dynamic Security Compliance Checks in Amazon EKS for Regulated Industries." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 1369-85.
Shaik, Babulal. "Dynamic Security Compliance Checks in Amazon EKS for Regulated Industries." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 1369-85.
Muneer Ahmed Salamkar. Scalable Data Architectures: Key Principles for Building Systems That Efficiently Manage Growing Data Volumes and Complexity. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Jan. 2021, pp. 251-70
Muneer Ahmed Salamkar, and Jayaram Immaneni. Automated Data Pipeline Creation: Leveraging ML Algorithms to Design and Optimize Data Pipelines. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, June 2021, pp. 230-5
Shaik, Babulal. "Automating Zero-Downtime Deployments in Kubernetes on Amazon EKS." Journal of AI-Assisted Scientific Discovery 1.2 (2021): 355-77.
Shaik, Babulal. "Developing Predictive Autoscaling Algorithms for Variable Traffic Patterns." Journal of Bioinformatics and Artificial Intelligence 1.2 (2021): 71-90.
Shaik, Babulal. "Designing Scalable Ingress Solutions for High-Throughput Applications on EKS." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 635-57.
Shaik, Babulal. "Network Isolation Techniques in Multi-Tenant EKS Clusters." Distributed Learning and Broad Applications in Scientific Research 6 (2020).
Shaik, Babulal, and Karthik Allam. "Integrating Amazon EKS With CI CD Pipelines for Efficient Application Delivery." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 876-93.
Muneer Ahmed Salamkar. Batch Vs. Stream Processing: In-Depth Comparison of Technologies, With Insights on Selecting the Right Approach for Specific Use Cases. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
Muneer Ahmed Salamkar, and Karthik Allam. Data Integration Techniques: Exploring Tools and Methodologies for Harmonizing Data across Diverse Systems and Sources. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
Muneer Ahmed Salamkar, et al. The Big Data Ecosystem: An Overview of Critical Technologies Like Hadoop, Spark, and Their Roles in Data Processing Landscapes. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Sept. 2021, pp. 355-77
Sarbaree Mishra. A Distributed Training Approach to Scale Deep Learning to Massive Datasets. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
Sarbaree Mishra, et al. Training Models for the Enterprise - A Privacy Preserving Approach. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
Sarbaree Mishra. Distributed Data Warehouses - An Alternative Approach to Highly Performant Data Warehouses. Distributed Learning and Broad Applications in Scientific Research, vol. 5, May 2019
Sarbaree Mishra, et al. Improving the ETL Process through Declarative Transformation Languages. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June 2019
Sarbaree Mishra. A Novel Weight Normalization Technique to Improve Generative Adversarial Network Training. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
Sarbaree Mishra. “Moving Data Warehousing and Analytics to the Cloud to Improve Scalability, Performance and Cost-Efficiency”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
Sarbaree Mishra, et al. “Training AI Models on Sensitive Data - the Federated Learning Approach”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Apr. 2020
Sarbaree Mishra. “Automating the Data Integration and ETL Pipelines through Machine Learning to Handle Massive Datasets in the Enterprise”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
Sarbaree Mishra. “The Age of Explainable AI: Improving Trust and Transparency in AI Models”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Oct. 2021, pp. 212-35
Sarbaree Mishra. “Leveraging Cloud Object Storage Mechanisms for Analyzing Massive Datasets”. African Journal of Artificial Intelligence and Sustainable Development, vol. 1, no. 1, Jan. 2021, pp. 286-0
Sarbaree Mishra, et al. “A Domain Driven Data Architecture For Improving Data Quality In Distributed Datasets”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 2, Aug. 2021, pp. 510-31
Sairamesh Konidala. “What Is a Modern Data Pipeline and Why Is It Important?”. Distributed Learning and Broad Applications in Scientific Research, vol. 2, Dec. 2016, pp. 95-111
Sairamesh Konidala, et al. “The Impact of the Millennial Consumer Base on Online Payments ”. Distributed Learning and Broad Applications in Scientific Research, vol. 3, June 2017, pp. 154-71
Sairamesh Konidala. “What Are the Key Concepts, Design Principles of Data Pipelines and Best Practices of Data Orchestration”. Distributed Learning and Broad Applications in Scientific Research, vol. 3, Jan. 2017, pp. 136-53
Sairamesh Konidala, et al. “Optimizing Payments for Recurring Merchants ”. Distributed Learning and Broad Applications in Scientific Research, vol. 4, Aug. 2018, pp. 295-11
Sairamesh Konidala, et al. “A Data Pipeline for Predictive Maintenance in an IoT-Enabled Smart Product: Design and Implementation”. Distributed Learning and Broad Applications in Scientific Research, vol. 4, Mar. 2018, pp. 278-94
Muneer Ahmed Salamkar, and Karthik Allam. Architecting Data Pipelines: Best Practices for Designing Resilient, Scalable, and Efficient Data Pipelines. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
Muneer Ahmed Salamkar. ETL Vs ELT: A Comprehensive Exploration of Both Methodologies, Including Real-World Applications and Trade-Offs. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
Muneer Ahmed Salamkar. Next-Generation Data Warehousing: Innovations in Cloud-Native Data Warehouses and the Rise of Serverless Architectures. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
Muneer Ahmed Salamkar. Real-Time Data Processing: A Deep Dive into Frameworks Like Apache Kafka and Apache Pulsar. Distributed Learning and Broad Applications in Scientific Research, vol. 5, July 2019
Muneer Ahmed Salamkar, and Karthik Allam. “Data Lakes Vs. Data Warehouses: Comparative Analysis on When to Use Each, With Case Studies Illustrating Successful Implementations”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
Muneer Ahmed Salamkar. Data Modeling Best Practices: Techniques for Designing Adaptable Schemas That Enhance Performance and Usability. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Dec. 2019
Gade, Kishore Reddy. "Data-driven decision making in a complex world." Journal of Computational Innovation 1.1 (2021).
Gade, Kishore Reddy. "Migrations: Cloud Migration Strategies, Data Migration Challenges, and Legacy System Modernization." Journal of Computing and Information Technology 1.1 (2021).
Gade, K. R. "Data Analytics: Data Democratization and Self-Service Analytics Platforms Empowering Everyone with Data." MZ Comput J 2.1 (2021).
Gade, Kishore Reddy. "Data Governance and Risk Management: Mitigating Data-Related Threats." Advances in Computer Sciences 3.1 (2020).
Gade, K. R. "Data Mesh Architecture: A Scalable and Resilient Approach to Data Management." Innovative Computer Sciences Journal 6.1 (2020).
Gade, Kishore Reddy. "Data Analytics: Data Governance Frameworks and Their Importance in Data-Driven Organizations." Advances in Computer Sciences 1.1 (2018).
Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
Komandla, Vineela. "Effective Onboarding and Engagement of New Customers: Personalized Strategies for Success." Available at SSRN 4983100 (2019).
Komandla, Vineela. "Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction." Available at SSRN 4983012 (2018).
Komandla, Vineela. "Transforming Customer Onboarding: Efficient Digital Account Opening and KYC Compliance Strategies." Available at SSRN 4983076 (2018).
Komandla, Vineela. "Navigating Open Banking: Strategic Impacts on Fintech Innovation and Collaboration." International Journal of Science and Research (IJSR) 6.9 (2017): 10-21275
Katari, A. "ETL for Real-Time Financial Analytics: Architectures and Challenges." Innovative Computer Sciences Journal 5.1 (2019).
Katari, A. "Data Quality Management in Financial ETL Processes: Techniques and Best Practices." Innovative Computer Sciences Journal 5.1 (2019).
Katari, A. "Real-Time Data Replication in Fintech: Technologies and Best Practices." Innovative Computer Sciences Journal 5.1 (2019).
