Model-Based Systems Engineering (MBSE) is a methodology that uses visual modeling to support system requirements, design, analysis, and validation activities throughout the development lifecycle. Essential for managing complex systems, improving communication among stakeholders, and enhancing the overall quality and efficiency of systems engineering processes.
An environment closer to production where final testing and validation occur. Crucial for ensuring that products are ready for production deployment.
Garbage In-Garbage Out (GIGO) is a principle stating that the quality of output is determined by the quality of the input, especially in computing and data processing. Crucial for ensuring accurate and reliable data inputs in design and decision-making processes.
A testing methodology that verifies the complete workflow of an application from start to finish, ensuring all components work together as expected. Important for ensuring the reliability and performance of digital products, leading to better user satisfaction and fewer post-launch issues.
The process of running a system for an extended period to detect early failures and ensure reliability. Important for ensuring the stability and performance of digital products before full-scale deployment.
A statistical method used to assess the generalizability of a model to unseen data, involving partitioning a dataset into subsets for training and validation. Essential for evaluating model performance and preventing overfitting in digital product analytics.
Human in the Loop (HITL) integrates human judgment into the decision-making process of AI systems. Crucial for ensuring AI reliability and alignment with human values.
Proof of Concept (PoC) is a demonstration, usually in the form of a prototype or pilot project, to verify that a concept or theory has practical potential. Crucial for validating ideas, demonstrating feasibility, and securing support for further development in product design and innovation processes.
A preliminary version of a project or system used to test and validate its feasibility before full-scale implementation. Crucial for identifying potential issues and making necessary adjustments to improve the final product.
A testing method where the internal structure of the system is not known to the tester, focusing solely on input and output. Essential for validating the functionality of digital products from an end-user perspective.
A practice of performing testing activities in the production environment to monitor and validate the behavior and performance of software in real-world conditions. Crucial for ensuring the stability, reliability, and user satisfaction of digital products in a live environment.
A model by Don Norman outlining the cognitive steps users take when interacting with a system: goal formation, planning, specifying, performing, perceiving, interpreting, and comparing. Important for designing user-friendly and effective products by understanding and supporting user behavior at each stage.
A usability testing method where users interact with a system they believe to be autonomous, but which is actually operated by a human. Essential for testing concepts and interactions before full development.
User Acceptance Testing (UAT) is the final phase of the software testing process where actual users test the software to ensure it meets their requirements. Crucial for validating that the software functions correctly in real-world scenarios before its release.
A method used in AI and machine learning to ensure prompts and inputs are designed to produce the desired outcomes. Essential for improving the accuracy and relevance of AI responses.
Design strategies aimed at preventing user errors before they occur. Crucial for enhancing usability and ensuring a smooth user experience.
Know Your Customer (KYC) is a process used by businesses to verify the identity of their clients and assess potential risks of illegal intentions for the business relationship. Essential for preventing fraud, money laundering, and terrorist financing, particularly in financial services, while also ensuring compliance with regulatory requirements and building trust with customers.
A cognitive bias where individuals overestimate the accuracy of their judgments, especially when they have a lot of information. Important for understanding and mitigating overconfidence in user decision-making.
In AI, the generation of incorrect or nonsensical information by a model, particularly in natural language processing. Important for understanding and mitigating errors in AI systems.
Software Requirements Specification (SRS) is a detailed document that outlines the functional and non-functional requirements of a software system. Crucial for ensuring clear communication and understanding between stakeholders and the development team.
Also known as Expert Review, a method where experts assess a product or system against established criteria to identify usability issues and areas for improvement. Essential for leveraging expert insights to enhance product quality and usability.
The belief in one's ability to succeed in specific situations or accomplish a task, influencing motivation and behavior. Crucial for designing systems that enhance user confidence and encourage goal achievement.
Artificially generated data that mimics real data, used for training machine learning models. Crucial for training models when real data is scarce or sensitive.
A technique used to evaluate a product or system by testing it with real users to identify any usability issues and gather qualitative and quantitative data on their interactions. Crucial for identifying and resolving usability issues to improve user satisfaction and performance.
Plan-Do-Check-Act (PDCA) is an iterative four-step management method used for continuous improvement of processes and products. Essential for quality control and operational efficiency.
A symmetrical, bell-shaped distribution of data where most observations cluster around the mean. Fundamental in statistics and crucial for many analytical techniques used in digital product design and data-driven decision making.