What are the best ways to use data analytics for systems management in systems engineering?

Predictive Maintenance: Implement predictive analytics to forecast equipment failures or maintenance needs. By analyzing historical data, patterns of equipment degradation, and performance metrics, systems engineers can schedule maintenance proactively, minimizing downtime and reducing costs. Performance Monitoring and Optimization: Utilize data analytics to continuously monitor system performance metrics. Analyze real-time data to identify inefficiencies, bottlenecks, or anomalies in the system's operation. This information can then be used to optimize system performance, streamline processes, and improve overall efficiency. Fault Detection and Diagnostics...

Data & analytics is a powerful design and business tool. When properly applied across any kind of system design, it supports and potentially translates an end product into a high availability asset. Proper use of data and analytics will consider cost, schedule and performance to get the best bang for buck; for both recoverable and non-recoverable asset. It can become part of a strategic business plan for systems asset management and growth.

Les systèmes, surtout les systèmes complexes, intègrent de plus en plus de l’ “intelligence” en remplacement de la force brute. Au passage la donnée devient centrale. Il ne suffit pas de prévoir la gestion des données dans la phase de conception : il faut mettre au c?ur du système la capacité de gérer tout le process qui va des capteurs jusqu’à la capacité de décision, en passant par les étapes d’élaboration intermédiaires et successives en données, puis informations, puis connaissances.

It helps in improving a behavioral, operational and reliable system. It also provides a system which tends to be less prone to maintenance and provides a cost effective solution.

Within the fintech sector, data analysis is being employed to gain competitive advantages in the market. This industry in Mexico collects, processes, and transforms large volumes of data in seconds. Initially used to preserve business continuity, the focus has evolved to understand transactional behavior. This has led to the identification of new business opportunities during the requirements stage. As a result, innovative electronic banking services and integration with emerging technologies in smartphones and electronic devices are developed, marking a significant evolution in the sector.

Data analytics for systems testing is a must have. Observability as a market segment onto itself has grown quite large now and there are many open source and vendor implementations. There are no more excuses for not doing this. You want as many metrics and traces of the flow of data and function calls to determine stability and help to identify root cause issues on failure (reducing the time of outage/issue). This should be a platform feature for applications that the systems teams hardens for the systems the apps run on.

It is very important to plan the analytics before testing. I mean randomization in sampling will define the conclusion that can be established from testing. I have experienced testing teams with conclusions going further to what randomization allows.

The test is a breathing function for any system otherwise will die without continuous improvement! Efficiency is not the case argument is perspective

Data analytics can be a powerful tool for systems testing that enables analysts to uncover patterns and anomalies in the data generated during testing. By analyzing this data, testing teams can identify areas for improvement and optimize the system's performance. Moreover, data analytics can provide valuable insights into user behavior and usage patterns that can inform future testing efforts. and system development. Incorporating data analytics into systems testing can result in a more efficient and effective testing process, ultimately leading to higher-quality and more reliable systems, as well as greater user satisfaction.

Its roles are crucial for evaluating the performance, reliability, and functionality of a system. Data analysts contribute by designing and executing tests that involve large datasets to simulate real-world scenarios. They assess how the system handles diverse data inputs, identifying patterns, anomalies, and potential bottlenecks. By analyzing test results, they ensure data integrity and validate that the system meets performance expectations. Additionally, data analytics in system testing aids in identifying trends, enabling proactive problem-solving, and optimizing the system for efficiency. In summary, data analytics roles play a pivotal part in ensuring the robustness and effectiveness of a system during the testing phase.

The volume of collected data can really be overhelming. Just talking about RAM (Reliability, Availability & Mantainability), imagine parameters like failure rate, time between failures, time to repair, uptime and downtime being measured for each equipment of the system. Put on top of that the collection of data related to operation (e.g. pressure, voltage) and execution of condition based maintenance (e.g. vibration, insulation resistance). Lots of data indeed that are going to require many human and material resources to be gathered, stored, handled and analyzed.

Automated monitoring of telemetry from a system is critical to identifying issues in a timely manner, particularly since AI/ML features in the monitoring can identify changes in patterns of life that might be missed by a purely statistical approach such as red/yellow limit checks, or missed by a human operator. Whether you're monitoring satellites on-orbit, a manufacturing assembly line, or an order fulfillment and distribution system, automating the analysis and sensemaking of all the data that can be generated by monitoring the system is important to understanding how the system is performing compared to design intent.

The best approach to perform data analysis for system monitoring involves defining clear objectives, collecting relevant data, utilizing appropriate analytical tools, and interpreting results to make informed decisions. Additionally, implementing real-time monitoring and employing machine learning techniques can enhance the effectiveness of data analysis in ensuring system performance and stability.

A really great way data and analytics can improve monitoring is by dramatically decreasing false alarms, ensuring resources are able to focus more on actual issues.

Most of my IT security consultancy journey has been deeply related to machine data, correlation, reporting, visualizations, automation, etc. Almost no machine data is irrelevant, but it's important to know how to link different datasets so they can answer your questions. When properly used, it can drastically reduce the time to repair or identify issues in the environment, or produce accurate real-time reporting on relevant areas like authentication events, and in some advanced cases, power SIEM solutions. The bottom line is, logs are there to help us understand issues, and connecting all these logs by a common information model (for correlation purposes) will get you on the right foot when it comes to monitoring your environment.

It is very crucial to undertake and have more control on the below features in systems engineering, like: 1. data integration and cleansing, 2. security and data retention, 3. automated data analytics, 4. user-friendly interface and experience, 5. real-time data capabilities, 6. robust reporting features, 7. data storage capability, 8. computerized and sort out data, 9. updates data, 10. separate useful data for sharing.

Once you're monitoring your system to understand what it is doing, or not doing, the next step is to use that information to determine courses of action. Many of those will be able to be automated, reducing the timelines to return the system to nominal operations after a fault, as well as enabling more optimal operation of the system while it is operating nominally. It may not always be a goal to reduce or eliminate the need for human operators, but that's another obvious potential benefit. To give an example from my "new Space" small satellites domain, monitoring telemetry can determine if onboard components are in an anomalous state, and the reset of many of those onboard components can be automated, often in the onboard flight software.

Key System improvements can happen mostly through data analytics. For example, when we put in new systems we want to understand usage patterns. Who is using it, where and when; if it is a mobile system as well as a desktop system we want to understand which platform is being used more, why? If it is system performance, we can profile the usage by location or by technology platform, and understand where the performance bottlenecks are. For example, if it is a mobile system, we can quickly troubleshoot the area by understanding connectivity issues at a location. We can isolate software issues by focusing in on database performance, or the application performance. By digging deeper through the data, we get more insights, better performance.

Data analytics and AI can change the landscape of system engineering to great extent. The design, testing, maintenance and improvement can utilise appropriate methods to take this to next level.

By incorporating data analytics into systems management, engineers can gain valuable insights, enhance decision-making, and optimize the performance and reliability of complex systems.

Para realizar uma análise de dados eficaz visando a melhoria de sistemas, é importante seguir um processo estruturado. Alguns dos passos essenciais, s?o: Defini??o de Objetivos de Melhoria, Identifica??o de Métricas Relevantes, Coleta e Organiza??o de Dados, Análise Descritiva, Análise Exploratória de Dados (EDA), Identifica??o de Causas Raiz, Implementa??o de Solu??es, Monitoramento Contínuo, Avalia??o de resultados e Itera??o e Aprendizado Contínuo. Ao seguir esses passos, você estabelece um processo sistemático para utilizar a análise de dados como uma ferramenta poderosa na busca constante pela melhoria de sistemas.

Analytics with on time quality and evaluated performance data with raw user driven applications can be applied to operations key performance. Using an integrated system improvement can demonstrate a scope for the organization system improvement and improvisation. Opportunities with data driven operations have given enterprises greater flexibility and productivity to manage their operations.

It depends on who collects the data and does the data include all the necessary information. A boss that looks at a spreadsheet without knowing where the numbers come from is making random decisions instead of informed decisions.

The potential for data overload sometimes causes system designers to minimize the number of data items that are collected, and sometimes causes system operators to turn off monitored alerts. This can lead to missing problems that can become worse over time. The solution to that data overload is automated data analytics. However, this does not replace using good system engineering to make sure that you're monitoring the right things, at an appropriate cadence to identify and characterize issues.

Consider your analytics suite as the functional part of your behavioral “digital twin” model. Building the model first, then the system based on it, equates to what folks are calling model based system engineering. Maintaining the model and using it to project “what ifs” about how the system matures based on use and environmental conditions is too, but more specifically called model based in service engineering.

Data analytics can play a critical role in systems management, helping organizations optimize their IT infrastructure and improve operational efficiency. Real-time monitoring, predictive analytics, capacity planning, root cause analysis, performance optimization, and compliance and security are some of the best ways to use data analytics for systems management. By leveraging data analytics in these ways, organizations can improve their systems management practices, reduce downtime, and improve overall operational efficiency.