Metrics that may look impressive but do not provide meaningful insights into the success or performance of a product or business, such as total page views or social media likes. Important for distinguishing between metrics that drive real business value and those that do not.
The use of statistical techniques and algorithms to analyze historical data and make predictions about future outcomes. Important for optimizing marketing strategies and anticipating customer needs.
The process of using statistical analysis and modeling to explore and interpret business data to make informed decisions. Essential for improving business performance, identifying opportunities for growth, and driving strategic planning.
A research approach that starts with a theory or hypothesis and uses data to test it, often moving from general to specific. Essential for validating theories and making informed decisions based on data.
The use of data and insights to understand and manage relationships with customers and prospects. Crucial for enhancing customer engagement and building stronger relationships.
The process of designing, developing, and managing tools and techniques for measuring performance and collecting data. Essential for monitoring and improving system performance and user experience.
A research method that involves forming a theory based on data systematically gathered and analyzed. Useful for developing design theories and solutions that are directly grounded in user research and data.
Statistical data relating to a particular population and groups within it. Crucial for market research and understanding target audiences.
A comprehensive view of a customer that includes data from all interactions and touchpoints across the customer journey. Crucial for delivering personalized experiences and improving customer satisfaction.
Ontology is a comprehensive model that includes entities, their attributes, and the complex relationships between them, while taxonomy is a hierarchical classification system that organizes entities into parent-child relationships. Essential for understanding the depth and scope of data organization, helping to choose the appropriate structure for information management and retrieval.
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.
The use of data, algorithms, and machine learning to recommend actions that can achieve desired outcomes. Essential for optimizing decision-making and implementing effective strategies.
A cognitive bias that occurs when conclusions are drawn from a non-representative sample, focusing only on successful cases and ignoring failures. Crucial for making accurate assessments and designing systems that consider both successes and failures.
The error of making decisions based solely on quantitative observations and ignoring all other factors. Important for ensuring a holistic approach to decision-making.
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.
The use of data from digital devices to measure and understand individual behavior and health patterns. Crucial for developing personalized user experiences and health interventions.
Data points that represent an individual's, team's, or company's performance in the sales process. Essential for tracking progress, identifying issues, and optimizing sales strategies.
A research approach that starts with observations and develops broader generalizations or theories from them. Useful for discovering patterns and generating new theories from data.
A sorting algorithm that distributes elements into a number of buckets, sorts each bucket individually, and then combines the buckets to get the sorted list. Useful for understanding more advanced algorithmic techniques and their applications.
A tree-like model of decisions and their possible consequences, used in data mining and machine learning for both classification and regression tasks. Valuable for creating interpretable models in digital product design and user behavior analysis.
The practice of developing artificial intelligence systems that are fair, transparent, and respect user privacy and rights. Crucial for ensuring that AI technologies are developed responsibly and ethically.
A tendency for respondents to answer questions in a manner that is not truthful or accurate, often influenced by social desirability or survey design. Important for understanding and mitigating biases in survey and research data.
A dark pattern where the product asks for the user's social media or email credentials and then spams all the user's contacts. Recognizing the harm of this practice is important to protect user trust and avoid spamming their contacts.
An action in a user interface that, once performed, cannot be undone and typically involves deleting or removing content. Important for emphasizing the severity of the action and ensuring user confirmation to prevent accidental data loss.
An intermediary that gathers and provides information to users, typically in an online context. Important for helping users make informed decisions based on aggregated data.
A marketing strategy that uses user behavior data to deliver personalized advertisements and content. Important for improving user engagement and conversion rates by providing relevant and timely information to users.
The process of predicting future customer demand using historical data and other information. Crucial for optimizing inventory levels, production schedules, and supply chain management.
A statistical method that models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. Essential for predicting outcomes and understanding relationships between variables in digital product design and analysis.
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.
Enhanced search results that include additional visual or informational elements beyond the standard text, often derived from structured data on a webpage. Important for improving click-through rates and providing users with more useful information in search results.
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 data-driven methodology aimed at improving processes by identifying and removing defects, and reducing variability. Crucial for enhancing the quality and efficiency of digital product development processes.
The practice of protecting systems, networks, and programs from digital attacks, unauthorized access, and data breaches. Essential for safeguarding sensitive information, maintaining user trust, and ensuring the integrity and functionality of digital products and services.
The process of collecting, analyzing, and reporting aggregate data about which pages a website visitor visits and in what order. Essential for understanding user behavior and improving website navigation and content.
Research conducted in natural settings to collect data on how people interact with products or environments in real-world conditions. Crucial for gaining authentic insights into user behaviors and contexts.
Define, Measure, Analyze, Improve, and Control (DMAIC) is a data-driven improvement cycle used in Six Sigma. Crucial for systematically improving processes and ensuring quality in digital product development.
A role focused on driving user acquisition, engagement, and retention through data-driven strategies and experiments. Essential for scaling products and optimizing user growth.
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.
The process of estimating future sales based on historical data, trends, and market analysis. Crucial for setting realistic sales targets and planning resources effectively.
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.
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 decentralized digital ledger that records transactions across many computers in a way that ensures the security and transparency of data. Crucial for understanding and implementing secure, transparent digital transactions and applications.
The use of biological data (e.g., fingerprints, facial recognition) for user authentication and interaction with digital systems. Crucial for enhancing security and user experience through advanced authentication methods.
AI systems that can dynamically adjust their behavior based on new data or changes in the environment. Important for developing systems that can respond to real-time changes and improve over time.
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 pop-up dialog that appears when a user attempts to leave a page or application, which can be used to prevent loss of progress or data, or to confirm user intent. While it can be used ethically to prevent data loss or confirm actions, designers must avoid using it to deceive, delay, block, or interfere with the user's intent, thus ensuring it does not become a dark pattern.
3-Tiered Architecture is a software design pattern that separates an application into three layers: presentation, logic, and data. Crucial for improving scalability, maintainability, and flexibility in software development.
Quantitative measures used to track and assess the performance and success of a product, such as usage rates, customer satisfaction, and revenue. Essential for making data-driven decisions to improve product performance and achieve business goals.
Simple Knowledge Organization System (SKOS) is a standard for representing knowledge organization systems such as thesauri, classification schemes, and taxonomies. Essential for enabling interoperability and sharing of structured knowledge across different systems.
Code added to a webpage to help search engines understand the content and provide more informative results for users, enhancing SEO. Essential for improving SEO and ensuring that search engines can accurately interpret webpage content.
A design approach that uses data, algorithms, and predictive analytics to anticipate user needs and behaviors, creating more personalized and effective experiences. Crucial for enhancing user experience through anticipation and personalization.
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.
The practice of quickly testing and iterating on ideas to validate assumptions and learn from user feedback in a short time frame. Essential for agile development and making data-driven decisions efficiently.
The practice of preserving a user's data and settings between sessions in an application. Crucial for enhancing user experience by providing continuity and personalization.
A research method that involves repeated observations of the same variables over a period of time. Crucial for understanding changes and developments over time.
A type of artificial intelligence capable of generating new content, such as text, images, and music, by learning from existing data. Important for automating creative processes and generating novel outputs.
The process of continuously improving a product's performance, usability, and value through data-driven decisions and iterative enhancements. Crucial for ensuring that a product remains competitive and meets evolving user needs.
Numeronym for the word "Interoperability" (I + 14 letters + Y), the ability of different systems, devices, or applications to work together and exchange information effectively without compatibility issues. Crucial for ensuring compatibility and integration between systems.
A technique for creating interactive web applications by exchanging data with the server in the background without reloading the entire page. Essential for enhancing user experience by making web applications more dynamic and responsive.
A tool used to organize ideas and data into groups based on their natural relationships. Essential for designers and product managers to synthesize information and generate insights.