Qm semi final

• Descriptive Analytics


  Focuses on summarizing historical data to understand what happened in the past. Involves techniques such as data aggregation, data visualization, and reporting to provide insights into past trends, patterns, and performance.
• Diagnostic Analytics

  Aims to understand why certain events occurred by identifying the root causes of past outcomes or problems. Involves deeper analysis of data to uncover relationships, correlations, and causal factors that explain past events.
• Predictive Analytics


  Uses historical data and statistical algorithms to forecast future outcomes or trends. Involves building predictive models based on historical patterns and using them to make predictions about future events or behaviors.
• Prescriptive Analytics


  Goes beyond predicting future outcomes by recommending actions to achieve desired outcomes. Involves analyzing data to determine the best course of action or decision based on predictive models, business rules, and optimization techniques.
• Types of Optimization Techniques

  • Process Optimization
  • Automation
  • Resource Consolidation and Virtualization
  • Cloud Optimization
  • Performance Tuning
  • Capacity Planning
  • Security Optimization
  • Cost Optimization
• Process Optimization


  Streamlining and optimizing IT processes to improve efficiency and reduce costs. Techniques include Lean Six Sigma, Business Process Reengineering (BPR), and Continuous Improvement (CI) methodologies.
• Automation


  The use of software tools and scripts to perform repetitive tasks and workflows automatically, without human intervention. Reduces manual effort, speeds up processes, and minimizes errors.
• Resource Consolidation and Virtualization

  Consolidating IT resources and leveraging virtualization technologies to optimize resource utilization and reduce hardware and operational costs.
• Cloud Optimization


  Techniques to optimize cloud computing services for scalability, agility, and cost savings. Includes rightsizing cloud resources, optimizing workload placement, leveraging reserved instances or discount plans, and implementing cost management tools.
• Performance Tuning


  Optimizing the performance of IT systems, applications, and databases to deliver optimal responsiveness and throughput. Techniques include code optimization, database indexing, caching, load balancing, and tuning of system parameters.
• Capacity Planning


  Forecasting future demand for IT resources and ensuring sufficient capacity is available to meet business requirements. Involves analyzing historical usage patterns and growth trends to proactively provision and scale IT resources.
• Security Optimization


  Enhancing the security posture of IT systems and infrastructure while minimizing the impact on performance and usability. Techniques include risk assessment, vulnerability management, security configuration management, and implementing defense-in-depth strategies.
• Cost Optimization


  Analyzing IT spending and identifying opportunities to reduce costs without sacrificing performance or quality of service. Includes renegotiating vendor contracts, optimizing software licensing, consolidating IT vendors, and implementing cost-saving measures such as energy efficiency initiatives.
• Methodologies in Process Optimization

  • Lean Six Sigma
  • Business Process Reengineering (BPR)
  • Continuous Improvement (CI)
• DMAIC


  1. Define: Identify project goals, scope, and stakeholders. Clearly define the problem or opportunity that needs to be addressed and establish project objectives.

  2. Measure: Collect and measure relevant data to establish a baseline of current performance. Identify key metrics, collect data, and analyze process performance to understand the current state.

  3. Analyze: Analyze the collected data to identify root causes of problems or opportunities for improvement. Use statistical and analytical tools to analyze data and identify factors contributing to variations.
• Improve phase


  1. Develop and implement solutions to address root causes

  2. Test and validate potential solutions

  3. Implement process changes

  4. Measure the impact of improvements
• Control phase


  1. Establish control mechanisms

  2. Monitor process performance

  3. Implement measures to prevent regression and ensure continued success
• Availability


  Measures the uptime of IT systems and services, expressed as a percentage
• Mean Time to Repair (MTTR)

  Measures the average time to restore a system or service after a failure or disruption
• Mean Time Between Failures (MTBF)

  Measures the average time elapsed between system failures
• Service Level Agreements (SLAs) Compliance

  Measures compliance with agreed-upon levels of service between IT and its customers
• Incident Resolution Rate


  Measures the percentage of incidents resolved within a specified time frame
• Change Success Rate


  Measures the percentage of changes successfully implemented without causing disruptions or incidents
• IT Cost per User


  Evaluates the cost-effectiveness of IT services by dividing total IT costs by the number of users or devices supported
• User Satisfaction


  Measures the satisfaction levels of employees or customers with IT services
• Network Performance Metrics


  Measures network latency, throughput, and packet loss
• Security Metrics


  Assess the effectiveness of cybersecurity measures, such as the number of security incidents, percentage of vulnerabilities patched, and compliance with regulatory standards
• Steps in processing data

  1. Define objectives

  2. Collect Data

  3. Clean Data

  4. Analyze Data

  5. Visualize Data-Modelling Data

  6. Interpret Data

  7. Evaluation

  8. Deployment
• Types of Data Analytics

  • Descriptive
  • Diagnostic
  • Predictive
  • Prescriptive
• Types of data


  • Ungrouped
  • Grouped
• Skills of a quality assurance (QA) professionals

  • Attention to Detail
  • Analytical Thinking
  • Communication
  • Problem-Solving
  • Technical Aptitude
  • Testing Techniques
  • Knowledge of QA Tools
  • Understanding of Standards and Regulations
  • Teamwork
  • Time Management
  • Adaptability
  • Continuous Learning
• Risk Management


  • Identifying potential risks to quality and developing strategies to mitigate or manage them effectively
• Attention to User Experience

  • Understanding the end-user perspective and ensuring that products not only meet technical specifications but also provide a positive user experience
• Documentation Skills


  • Thoroughly documenting test plans, test cases, test results, and any issues encountered during testing for future reference and analysis
• Metrics


  Quantifiable measures used to assess and evaluate various aspects of performance, processes, or activities within an organization
• Metrics provide objective data that can be used to track progress, measure efficiency, identify areas for improvement, and make informed decisions
• Data analytics plays a crucial role in business decision-making by enabling organizations to make informed and data-driven decisions
• Data-driven decision-making involves using data to gather insights, identify patterns, and draw conclusions that guide the decision-making process
• By analyzing large volumes of data, businesses can gain valuable insights into customer behavior, market trends, and operational efficiency, helping them make strategic choices for growth and success
• Data-driven decision-making is often objective, unbiased, and less influenced by personal preferences or biases