The process of maintaining, updating, and improving a product or system after its initial deployment to ensure its continued functionality, performance, and relevance to users. Crucial for ensuring long-term user satisfaction, product reliability, and adaptation to changing user needs and technological advancements.
The process of identifying, assessing, and mitigating potential threats that could impact the success of a digital product, including usability issues, technical failures, and user data security. Essential for maintaining product reliability, user satisfaction, and data protection, while minimizing the impact of potential design and development challenges.
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.
An environment closer to production where final testing and validation occur. Crucial for ensuring that products are ready for production deployment.
The distribution of a new or updated software product to users. Important for delivering new features, improvements, and fixes to users, ensuring continuous enhancement of the product.
An environment used for testing software to identify issues and ensure quality before production deployment. Important for detecting and fixing bugs to ensure the software's reliability and performance.
A non-production environment used for development and testing before deployment to production. Important for ensuring that changes are thoroughly tested before going live.
An environment that replicates the production environment, used for final testing before deployment. Crucial for ensuring that digital products are thoroughly tested and perform as expected before going live.
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.
CSAT (Customer Satisfaction) measures how products or services provided by a company meet or exceed customer expectations. Essential for understanding customer needs and improving product offerings.
The ability of a system to maintain its state and data across sessions, ensuring continuity and consistency in user experience. Crucial for designing reliable and user-friendly systems that retain data and settings across interactions.
Application Support Engineer (ASE) is a professional responsible for maintaining and supporting software applications, ensuring their availability and performance. Crucial for ensuring the reliability and user satisfaction of digital products through effective support and maintenance.
The setting where software and systems are actually put into operation for their intended use. Essential for ensuring that products are fully functional and meet user requirements in a real-world setting.
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 preliminary testing phase conducted by internal staff to identify bugs before releasing the product to external testers or customers. Crucial for ensuring product quality and functionality before broader release.
ModelOps (Model Operations) is a set of practices for deploying, monitoring, and maintaining machine learning models in production environments. Crucial for ensuring the reliability, scalability, and performance of AI systems throughout their lifecycle, bridging the gap between model development and operational implementation.
A software development practice where code changes are automatically prepared for a release to production. Crucial for ensuring rapid and reliable deployment of updates.
A software development practice where code changes are automatically deployed to production without manual intervention. Important for maintaining a high level of productivity and quality in software development.
A quick and often temporary fix applied to a software product to address an urgent issue without going through the full development cycle. Essential for maintaining the stability and functionality of digital products in the face of critical issues.
The process of overseeing and coordinating the development, testing, and deployment of software releases to ensure they are delivered efficiently and effectively. Essential for managing software development cycles and ensuring successful product releases.
A deployment strategy that reduces downtime and risk by running two identical production environments, switching traffic between them. Crucial for ensuring seamless updates and minimizing disruptions in digital product deployment.
The high-level structure of a software application, defining its components and their interactions. Essential for designing robust, scalable, and maintainable digital products.
A performance testing method that evaluates the system's behavior and stability over an extended period under a high load. Essential for identifying memory leaks and ensuring the reliability and performance of digital products under prolonged use.
The quality of being uniform and coherent across different elements and touchpoints in design. Crucial for creating predictable and reliable user experiences.
A bias that occurs when the sample chosen for a study or survey is not representative of the population being studied, affecting the validity of the results. Important for ensuring the accuracy and reliability of research findings and avoiding skewed data.
A range of values, derived from sample statistics, that is likely to contain the value of an unknown population parameter. Essential for making inferences about population parameters and understanding the precision of estimates in product design analysis.
A type of bias that occurs when the observer's expectations or beliefs influence their interpretation of what they are observing, including experimental outcomes. Essential for ensuring the accuracy and reliability of research and data collection.
A design principle that ensures a system continues to function at a reduced level rather than completely failing when some part of it goes wrong. Crucial for enhancing system reliability and user experience in adverse conditions.
Data points that differ significantly from other observations and may indicate variability in a measurement, experimental errors, or novelty. Crucial for identifying anomalies and ensuring the accuracy and reliability of data in digital product design.
A principle that states tasks always take longer than expected, even when considering Hofstadter's Law itself. Important for setting realistic project timelines and managing expectations in digital product development.
The process of combining different systems or components in a way that ensures they work together smoothly and efficiently without disruptions. Essential for providing a cohesive user experience and ensuring the reliability of complex systems.
A deployment strategy where a new version is released to a small subset of users to detect any issues before a full rollout. Crucial for minimizing risk and ensuring the stability of digital products during updates and deployments.
The part of an application that encodes the real-world business rules that determine how data is created, stored, and modified. Crucial for ensuring that digital products align with business processes and deliver value to users.
A server dedicated to automating the process of building and compiling code, running tests, and generating software artifacts. Crucial for ensuring continuous integration and maintaining the integrity of the codebase in digital product development.
A testing method that examines the internal structure, design, and coding of a software application to verify its functionality. Essential for ensuring the correctness and efficiency of the code in digital product development.
A statistical theory that states that the distribution of sample means approximates a normal distribution as the sample size becomes larger, regardless of the population's distribution. Important for making inferences about population parameters and ensuring the validity of statistical tests in digital product design.
A statistical technique that uses random sampling and statistical modeling to estimate mathematical functions and simulate systems. Useful for risk assessment, decision-making, and performance optimization in digital product design.
A model of organizational change management that involves preparing for change (unfreeze), implementing change (change), and solidifying the new state (refreeze). Important for successfully implementing and sustaining changes in product design processes and organizational practices.
The process of designing and refining prompts to elicit accurate and relevant responses from AI models. Crucial for optimizing the performance of AI applications.
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 Japanese term for "mistake-proofing," referring to any mechanism or process that helps prevent errors by design. Crucial for designing systems and processes that minimize the risk of human error.
A statistical measure that quantifies the amount of variation or dispersion of a set of data values. Essential for understanding data spread and variability, which helps in making informed decisions in product design and analysis.
The spread and pattern of data values in a dataset, often visualized through graphs or statistical measures. Critical for understanding the characteristics of data and informing appropriate analysis techniques in digital product development.
Application Release Automation (ARA) is the process of automating the release of applications, ensuring consistency and reducing errors. Crucial for accelerating the delivery of software updates and maintaining high-quality digital products.
A team responsible for developing and maintaining the foundational systems and services that support other teams and products. Crucial for ensuring scalability and efficiency across the organization.
A method of splitting a dataset into two subsets: one for training a model and another for testing its performance. Fundamental for developing and evaluating machine learning models in digital product design.
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.
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 Japanese word meaning inconsistency or variability in processes. Helps in recognizing and addressing workflow imbalances to improve efficiency.
A statistical phenomenon where a large number of hypotheses are tested, increasing the chance of a rare event being observed. Crucial for understanding and avoiding false positives in data analysis.
Internet of Things (IoT) refers to a network of interconnected physical devices embedded with electronics, software, sensors, and network connectivity, enabling them to collect and exchange data. Essential for creating smart, responsive environments and improving efficiency across various industries by enabling real-time monitoring, analysis, and automation.
A practice of performing testing activities earlier in the software development lifecycle to identify and address issues sooner. Essential for improving software quality, reducing defects, and accelerating development cycles in digital product design.
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.
An experimental design where subjects are paired based on certain characteristics, and then one is assigned to the treatment and the other to the control group. Important for reducing variability and improving the accuracy of experimental results.
The tendency for individuals to give positive responses or feedback out of politeness, regardless of their true feelings. Crucial for obtaining honest and accurate user feedback.
A software development practice where code changes are frequently integrated into a shared repository, with each change being verified by automated tests. Essential for catching errors early and improving the quality of software.
A preliminary testing method to check whether the most crucial functions of a software application work, without going into finer details. Important for identifying major issues early in the development process and ensuring the stability of digital products.
A statistical rule stating that nearly all values in a normal distribution (99.7%) lie within three standard deviations (sigma) of the mean. Important for identifying outliers and understanding variability in data, aiding in quality control and performance assessment in digital product design.
A framework inspired by Maslow's Hierarchy of Needs, applied to user experience design, prioritizing basic functionality and reliability before enhancing usability and delight. Essential for creating well-rounded and satisfying user experiences.
A cognitive bias where people underestimate the complexity and challenges involved in scaling systems, processes, or businesses. Important for understanding the difficulties of scaling and designing systems that address these challenges.