A continuación, le indicamos cómo puede mejorar su rendimiento en la computación en la nube con los comentarios de los clientes.

Be aware that major hurdle is usually clients understanding of cloud computing. Here’s a great example: I worked once on a large insurance client. They have been implementing cloud for sales and underwriting processes. But they completely resisted use cloud for API connectivity. Result? They insisted to build a lot custom code and use 20 years old on premise integration technologies that only increased latency. Instead using native cloud services like API gateway. Of course when they compare performance of cloud with their on premise solution, “surprisingly” cloud performance was worse! Of course they were sending data from on premise to Frankfurt and back that introduced big latency. Only when they saw data they got it!

To increase cloud computing performance depending on client input, create a thorough feedback mechanism that collects insights on a regular basis. Actively listen to your clients' needs, pain points, and recommendations. Use feedback to iterate and improve services, ensuring that cloud solutions are effective, dependable, and secure. Maintain open communication lines to handle concerns immediately and attentively. Implement regular learning and development initiatives based on client feedback to stay current with industry trends and best practices. Finally, focus client satisfaction and align cloud services with their evolving demands to achieve mutual success.

To make cloud computing better, listen to what clients say. Ask them what they like and don't like, and use that to improve your services. Keep them updated on changes based on their feedback. It's about making the cloud work better for them.

Collect feedback from clients and customers via surveys, interviews, or forms tailored to their cloud computing experience. Analyze this data to identify improvement areas and develop a targeted strategy for enhancing cloud performance.

To gather comprehensive data for improving cloud computing performance, use a multi-faceted approach. Conduct business analysis to identify key stakeholders, such as clients and internal teams. Engage them through interviews, focus groups, and workshops to gain qualitative insights. Complement this with quantitative feedback collected through well-designed, form-based surveys. Utilise online platforms, email questionnaires, or embedded feedback forms to reach a broader audience. Combine the contextual information from interviews with the measurable data from surveys to establish a comprehensive understanding of your clients' needs, enabling data-driven improvements in your cloud computing performance.

- "Listen to learn." - Feedback analysis involves collecting and evaluating feedback from stakeholders about your cloud computing project. This feedback can be used to identify areas for improvement and ensure that the project meets the needs of all stakeholders. - Here are some best practices: (1) Create a safe and open environment where stakeholders feel comfortable providing feedback. (2) Use a structured approach to feedback analysis to identify trends and patterns. (3) Communicate your findings and planned actions to stakeholders in a timely manner. - For example, a cloud team has migrated a CRM application to the cloud. They gather feedback from sales representatives about the new cloud-based CRM system.

Systematically analyzing and addressing feedback can enhance cloud computing services, improving performance and user satisfaction. Here's my template to analyze feedback: - Collect feedback highlighting issues like cloud service downtime. - Organize feedback into categories like performance and reliability. - Quantify the frequency of downtime issues to identify the most common problems. - Analyze specific comments describing when and how downtime occurs. - Use text analysis tools to detect sentiment and key themes. - Compare feedback with uptime metrics to understand the scope of the issue. - Prioritize adding a robust monitoring system to improve reliability.

Site reliability engineering principles will be handy where we configure monitoring and define what is acceptable and iteratively, we could improve it by shrinking our SLA's and response times .

Analyze collected feedback to identify patterns and pinpoint strengths and weaknesses in your cloud services. This structured approach uncovers improvement opportunities and guides strategic prioritization for enhanced performance.

When prioritising feedback for improving cloud computing performance, focus on business priorities rather than just technology. While technical enhancements are important, they should align with the organisation's goals. By analysing feedback based on the company's strategic vision, cloud service providers can ensure that improvements directly contribute to business success. This may involve prioritising feedback related to customer satisfaction, operational efficiency, cost optimisation, or competitive differentiation. Keeping business objectives at the forefront of feedback prioritisation helps deliver tangible value to clients and supports the organisation's long-term growth and success.

Implement changes based on identified improvement areas, addressing both quick wins and deeper systemic issues. Maintain transparency with clients and customers regarding the enhancements, fostering trust and demonstrating the effectiveness of their feedback.

To measure the impact of your performance improvements in Cloud Computing, leverage both KPIs and direct client feedback. In my experience with managed services, using tools like AWS CloudWatch or Azure Monitor to track service uptime, response times, and error rates is essential. Pair this with regular client surveys and feedback sessions to gauge user satisfaction. Comparing these metrics before and after implementing changes gives a clear picture of your progress. This blend of data-driven insights and client feedback ensures you can make informed adjustments and continually enhance your cloud services.

Here's my 2 cents on how I would measure impact: - Define Key Metrics, e.g., Track cloud service uptime and average response time. - Establish baseline metrics before implementing changes, e.g., Record an average uptime of 95% and response time of 300ms. - Track Changes: Post-implementation, monitor uptime daily, and measure response times during peak hours. - Customer Feedback: Among other things, send surveys/emails asking clients about their experience

A KPI to measure Client's Satisfaction Level with direct interviews is very important for a perfect client experience. It boosts relationships and their experience keeps on improving. It's important to quantify their experience because that's a good way to improve continuously. For example, for how long did the client face downtime if any. How much is the deployment time for application?

Establish a system for continuous feedback to monitor client and customer satisfaction in cloud computing. Regularly update feedback mechanisms to adapt to service changes and user needs, ensuring proactive management of performance standards.

In my experience, setting up a continuous feedback loop is key for keeping cloud performance high. One time, we made a feedback portal so clients could report issues and suggest improvements anytime. We also had regular check-ins with key clients to chat about their experiences. This helped us fix problems fast and build better client relationships. By updating our feedback system often, we made sure we stayed on track with what users needed and kept making our services better.

Clear and accessible documentation is always a great start. Supplement this with interactive webinars and step-by-step tutorial videos that demonstrate new features and improvements. Regularly update these resources to reflect the latest changes. Engaging users through Q&A sessions and feedback forms during webinars can also help address their concerns directly and foster a continuous improvement cycle. This proactive approach ensures users can make the most of your cloud services with confidence.

Collection of all the errors or mistakes you have done during implementation of work and make notes of it to keep them in mind and check and realise what should you have done to correct that flow. In the end, finally apply all the points from your past learning which will definitely help you to learn more.