How do you use system performance data to inform system design decisions?

When you're talking about a system, you're referring to two or more components working together to provide an emergent behavior that would not have been possible without the interaction of the components. System performance data may be a "Measure of Performance" such as how fast a vehicle accelerates from 0 to 60 mph. There is likely an entire physical simulation architecture needed to prove that the system will meet the expected MoP. Stakeholders are typically very familiar with how they would like their system to perform. Keep in mind as you decompose your system, the stakeholders will change and the measures of performance will also. It then becomes very important to involve specialty engineers who are more familiar with these parts.

System performance data is where the theory of a system is measured against actual operations. The insights gleamed and learned as to how well a system performs relative to specs and KPPs can greatly reduces cost over the system life-cycle if incremental improvements are made over time, based upon consistent observation and reporting of system performance data. Did the reliability engineering assumptions hold true for various components and sub-components over time? If not, then why? If yes, did the components exceed reliability factors/assumptions? If so, then why? Performance data is as extremely valuable and can lead to remarkable improvements as part of a continuously evolving system life-cycle and incremental development.

System performance data isn't just a collection of metrics and feedback; it's a treasure trove of insights waiting to be unlocked. Imagine it as the DNA of your system, revealing not only its current health but also its evolutionary potential. By digging deeper into this data, you can uncover patterns and trends that go beyond surface-level observations, offering a more nuanced understanding of user behavior and system interaction.

Recopilar datos de rendimiento no es la única manera para asegurar que el sistema este funcionando adecuadamente, pero seguramente si es una de las formas mas efectivas, recopilar información te permite construir tableros que le den mayor sentido a la información para tomar decisiones oportunas, en nuestro caso particular usamos prometheus y grafana, sin embargo existen infinidad de herramientas para el mismo propósito. Obtener métricas te brinda un score de salud de la infraestructura y el comportamiento de la solución, sin embargo, es necesario combinar otros datos para entender el sentimiento que percibe el usuario al interactuar con nuestra solución, esto es crucial para mantener los indicadores de conversión.

IT & Telecom System Performance data consist of KPI (key performance indicator) in the form of Excel, access or xml format. These performance data is periodically analyzed by SQL, Excel or Access by system designer or performance analyst. A system design professional utilizes these KPI data to identify the congestion and capacity sharing between various nodes. After identifying the congested area or cluster it is necessary to increase the system capacity by designing the new IT & Telecom network. Even though if there is no existing performance data is available system designer needs to take expected targeted customer number, look into the GIS data as well as system capacity information to design the new system.

System Performance is many things - response speed, peak load, outages… We have to track all these metrics for capacity planning and upgrade planning. It is important to understand CPU utilization, network, utilization, memory, utilization, disk utilization to understand capacity, and peak performance. This tells us if we need to upgrade the system or wait and do monitoring and if needed repairs. Outages and flickers also, give us insight into maintenance, proactive maintenance, and mean time to failure indicators. All these in totality gives us an idea if we need to renew our refresh our maintenance contracts, or purchase new systems.

System Performance data are so much important that it provides the qualitative input for us to understand the system design. Based on the performance the system display, the system can be considered to be stalwart or NOT. Through analyzing the system performance data, more new logical theories can be understood about the system design and it obviously will help to improvise the design.

Think of system performance data as the compass guiding the ship of system design. It's more than a diagnostic tool; it's a strategic asset that can help anticipate future needs and trends. This data offers a unique opportunity to not just react to current system shortcomings but to proactively design for tomorrow's challenges, ensuring the system remains relevant and efficient in a rapidly evolving tech landscape.

- System behaviour under varying workloads, can be analysed. - This data helps us optimize the system for improved performance, greater efficiency, and increased reliability. - Helps us to understand if there are any bottlenecks in the design.

São importantes para que a melhoria contínua seja realizada. Baseado nos dados podemos por exemplo: 1 Escalar ou reduzir espaço em nuvem; 2 Avaliar tempo de resposta da aplicação; 3 Taxas de erro; 4 Volume acessos; 5 Interação do usuário. Entre outros… E então ir realizando as alterações sempre visando, do lado de produtos a melhor experiência de usuário e conversão e, do lado da engenharia melhor taxa de eficiência do sistema.

Elevate your system's efficiency by meticulously collecting performance data. Define precise metrics, employ reliable sources, methods, and tools. Craft a strategic data collection plan detailing when, where, and how data is gathered. Adhere to best practices and standards, emphasizing data quality, security, and ethics. #DataCollection #SystemPerformance #BestPractices

The most important step to collecting system data is knowing how the system works, and who uses the system the most often. Speaking with people who use the system (or designed if you are still in that phase) is instrumental in providing insight to important metrics. The system should enhance the customers experience and this can only be achieved through having metrics that reflect improvement for their goals.

Collecting data has become relatively 'easy'. However, collecting the right data in terms of business relevance and quality is a different challenge. The strategy should be designed at the top level to define the business objectives. Simultaneously, the solution for collecting this data should be designed at the ground level. This approach ensures the acquisition of high-quality data from the operational level to the top, and establishes a process that is tailored to the system and the people responsible for generating this data.

Un aspect important parfois sous estimé dans les moyens de collecte, y.c. pour les systèmes innovants sont de prévoir et d’utiliser des données de retour d’expérience : sur les usages de systèmes similaires, sur les données fournies par les systèmes externes en interface avec le système à concevoir et qui vont influencer ou être transformées par le système à concevoir. Ce retour d’expérience nécessite des processus adaptés qu’il convient d’ajuster pour fiabiliser ensuite les informations qui en sortiront en vue d’être exploitées. Un exemple très utile sont les données de fiabilité, les recensements et les analyses des pannes.

Physical system properties can be sensed and measured by sensor networks but perceived properties such as quality, safety, aethetics pose a challenge to physical metrics

System performance data in signaling is analyzed by collecting information from various sensors and networks. This data is then organized and summarized for key metrics like train punctuality and equipment reliability. Visualization tools help in spotting trends or issues, while statistical analysis identifies patterns and benchmarks against historical data. This process aids in understanding system behavior, predicting maintenance needs, and enhancing overall performance and safety.

Com base na minha experiência em gerenciamento de projetos complexos e na análise de stakeholders, é crucial enfatizar a importância de escolher cuidadosamente a tecnologia, as pessoas e os processos envolvidos. A correta seleção desses elementos é fundamental para garantir a eficácia na realização das atividades, bem como para assegurar que as contribuições da equipe de suporte sejam devidamente consideradas para o desempenho do sistema. Ao considerar a complexidade do projeto, é essencial que as decisões relacionadas à tecnologia, às equipes envolvidas e aos processos adotados sejam tomadas de forma criteriosa, a fim de garantir o sucesso e a eficiência do sistema.

In the realm of system performance analysis, a meticulous approach involves the systematic processing, organization, and interpretation of collected data. Employing sophisticated data analysis techniques, including statistical analysis, data visualization, data mining, and machine learning, is imperative. The utilization of advanced tools, encompassing specialized software, algorithms, and models, becomes paramount in this process. Adhering rigorously to best practices and industry standards ensures the integrity, accuracy, and consistency of the data. Executing these methodologies stands as a testament to the commitment to excellence in the ever-evolving landscape of data analysis. #DataAnalysis #TechnologyManagement #LeadershipExcellence

Des méthodes sont offertes à tous les niveaux pour analyser les données de performance d'un système. L'analyse préliminaire, l'analyse des modes de défaillances et de la criticité, la méthode des causes à effet avec l'utilisation du diagramme d'Ishikawa.

Analyzing system performance data should be an exercise in storytelling. Instead of just crunching numbers, try to weave a narrative that explains why the system behaves as it does. Employing techniques like predictive analytics can transform historical data into foresights, helping foresee potential system bottlenecks before they occur. This approach makes the analysis more intuitive and actionable for designers.

One aspect of system design is defining solution alternatives and performing a trade off study. Here, system performance data is directly used to evaluate an alternative and essentially rank ordering the alternatives. Algorithms for design exploration use the performance metrics to determine whether an architectural solution is a step in the right direction, hence pruning other alternatives. System performance data is also used in performing system diagnosis and prognostics, and we can adjust a performance parameter through data collection and inference.

One thing I found important is trends are your best friend. It is important to understand them in the overall design. Trends should be reflecting the output effects of changing different design parameters, individually first, and then in combination in order to understand their individual effects first and then their combined interaction. Focus on the trends especially when you cannot accurately attribute an accurate output value to your design choices

Metrics help tell a story, but they never complete the picture. The performance metrics may tell you that a problem exists, or that a certain design may fit more inline with your goals but it is important to not rely solely metrics. This falls into design timelines as well as projects often deliver inferior products when the pressure of a time constraint is added.

L'analyse des données qualitatives et quantitatives au moyen de contrôle du fonctionnement d'un système en permanence, de manière aussi exhaustive à l'aide d'outils composés d'un plan de veille, des rapports d'inspection, d'audit ou du retour d'expérience, permettent la prise de la bonne décision.

Integrating system performance data into design decisions should go beyond traditional metrics. It's about understanding the emotional and psychological impact of the system on users. For instance, using data to enhance user empathy in design can lead to more user-centric solutions, ultimately resulting in a system that resonates better with its audience

To enhance the utilization of system performance data, integrate findings into the design and maintenance processes, ensuring they guide decision-making. Translate insights into actionable recommendations and implement design changes based on identified failure patterns. Optimize maintenance strategies, establish a continuous monitoring system, and provide training to stakeholders for effective application. Document best practices, promote collaboration across departments, and use benchmarking to set realistic improvement goals. Develop risk mitigation strategies, regularly review and update approaches, fostering a culture of continuous improvement and informed decision-making to improve system reliability.

To optimize system performance, data utilization, continuous evaluation & enhancement of data acquisition, analysis, and utilization processes are essential. This involves a thorough review & updating of data requirements, methods, and tools to ensure their compatibility and effectiveness within the system design. Moreover, it is crucial to actively seek feedback from stakeholders, including system users, customers, & sponsors. Incorporating real-time user interactions to iteratively adjust algorithms contributes to a responsive and adaptive system architecture. The symbiotic relationship between technical evolution & stakeholder input forms the foundation for sustained excellence in the ever-changing landscape of technological frameworks.

To elevate the usage of system performance data, consider adopting a cyclical approach to learning and improvement. Implementing a feedback loop where data not only informs the design but also evolves based on ongoing user interactions can create a dynamic system that continuously adapts and improves. Engaging in regular, structured 'data retrospectives' can also help keep your processes agile and responsive.

Take User feedbacks and reports into Analysis when viewing the system performance data as this help the Team find out the exact issues to fix and what to take out in order to maintain the System's efficiency

Improving system performance data usage is in two parts: one on collecting appropriate data from systems in active usage, second from how the collected data informs system designers. For the former, continuous data collection and inference is critical. For instance we can collect data from several vehicles on how their battery state of charge changes when in shallow sleep. We can then infer what environmental conditions and the system elements contribute to the drain. For the latter, we take the inferred information and make sure our system requirements and tests appropriately address any quality conditions for a battery drain. Customer clinics are important to make sure we collect about attributes meaningful to customer.

For a software intensive system, hardware simulation and architecture simulation is important, to accurately predict solutions alternatives performance against the metrics.

Consider how support staff contributed to the performance of the system. Often technical or business staff are the metrics of choice, but admin, configuration management, travel, and accounting are the life blood of the system. If the blood isnt performing the rest of the system suffers.

- Take user feedback into account when creating KPIs. - Use User XP as a drive. - Avoid jumping to conclusions without solid evidences.

In the realm of system performance data, one often-overlooked aspect is the power of qualitative data. While quantitative data tells you what is happening, qualitative data can offer insights into why it's happening. Integrating user stories, case studies, and anecdotal evidence into your data pool can add a layer of depth to your understanding, providing a more comprehensive foundation for informed design decisions.

Cuidado con el efecto sandía: 🍉Verde por fuera (todas las métricas en verde), rojo por dentro (clientes insatisfechos ) Esto generalmente sucede cuando solo nos enfocamos en objetivos basadas en datos del sistema: - Disponibilidad - Velocidad de procesamiento - Otros ¿Cumplir estas métricas realmente significa que estamos generando valor para nuestra organización? A los objetivos basados en datos de rendimiento del sistema, se recomienda complementar con métricas de negocios, calidad y satisfacción del usuario, ejemplos: Financieras: -ROI -Beneficios generados: ingresos adicionales, ahorros generados, eficiencias generadas Calidad del aplicativo: -Satisfacción de las partes interesadas