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Explore foundational terms across data literacy, analytics, governance, and measurement—organized by theme to help teams build shared understanding and better decisions.
The ability to read, work with, analyze, and communicate with data—not just technically, but critically and contextually.
Read moreA method of structured inquiry used to critically examine assumptions and surface deeper understanding in data discussions.
Read moreThe process of making data accessible to all stakeholders, regardless of technical expertise, allowing more people to use data for decision-making.
Read moreThe mindset and responsibility of engaging thoughtfully, ethically, and constructively with data—as a contributor, interpreter, and decision-maker
Read moreThe practice of combining data, narrative, and visuals to communicate insights in a compelling way that drives understanding and action.
Read moreThe graphical representation of information to help understand patterns, trends, and insights that might not be obvious in raw data.
Read moreThe branch of ethics that addresses moral issues related to data collection, sharing, and use, focusing on fairness, transparency, and responsibility.
Read moreA framework for assigning ownership, defining standards, and ensuring accountability for data access, quality, and usage.
Read moreA plan for how an organization manages and uses data to drive outcomes, create value, and enable decision-making.
Read moreThe practice of collecting, storing, and maintaining data in a way that makes it secure, accessible, and usable throughout its lifecycle.
Read moreThe measure of how well data serves its intended purpose, assessed through accuracy, completeness, consistency, timeliness, and validity.
Read moreThe practice of protecting sensitive data from unauthorized access while adhering to relevant laws, regulations, and standards.
Read moreIsolated collections of data that cannot be easily accessed or understood by other parts of an organization, limiting collaboration and insight.
Read moreThe systematic analysis of data to discover meaningful patterns, correlations, and insights that can guide business actions and strategy.
Read moreAnalysis that summarizes what has happened in the past, including data aggregation, visualization, and reporting of historical events.
Read moreAnalysis focused on understanding why something happened by examining data in depth to identify patterns and potential root causes.
Read moreThe use of data, algorithms, and historical trends to forecast likely future outcomes or behaviors.
Read moreAdvanced analysis that recommends specific actions to take based on predictive insights and optimization algorithms.
Read moreThe technologies, practices, and strategies used to collect, analyze, and present business information to support better decision-making.
Read moreTools and processes that enable non-technical users to access, explore, and analyze data without requiring assistance from IT or analysts.
Read moreThe practice of combining data insights with human judgment and context to guide choices and actions.
Read moreAn approach where decisions are primarily based on the analysis of data rather than intuition, with an emphasis on measurable outcomes.
Read moreA specific, measurable metric that reflects how effectively a team or initiative is achieving its objectives.
Read moreA forward-looking metric that helps predict future success or problems before outcomes are fully realized.
Read moreA backward-looking metric that shows what has already happened, confirming patterns and measuring final outcomes of past actions.
Read moreA measurement that appears impressive but doesn't meaningfully correlate with business goals or drive important decisions.
Read moreAn approach to problem-solving that views issues as parts of an overall system, focusing on relationships, interactions, and feedback loops rather than isolated components.
Read moreSystematic patterns of deviation from norm or rationality in judgment that can occur in data collection, analysis, and interpretation, leading to distorted results or conclusions.
Read moreThe practice of fully concentrating, understanding, responding, and remembering what is being said during data discussions, enabling deeper comprehension and more effective collaboration.
Read moreThe process of identifying and questioning underlying beliefs and presumptions in data analysis to uncover hidden biases and develop more accurate understanding.
Read moreAn environment where team members feel comfortable raising questions about data, sharing concerns, admitting errors, and challenging interpretations without fear of negative consequences.
Read moreThe disciplined process of actively conceptualizing, analyzing, and evaluating data to form sound judgments and make effective decisions based on evidence rather than intuition alone.
Read moreFalse or inaccurate information spread unintentionally through data visualizations, reports, or analyses that can lead to flawed understanding and poor decision-making.
Read moreThe way a data question or problem is structured, presented, and contextualized, which significantly influences how information is perceived and what insights are drawn from it.
Read moreThe distinction between explaining what data shows (interpretation) and extracting meaningful, action-oriented understanding that creates value (insight).
Read moreThe cognitive ability to identify meaningful relationships, trends, and regularities within data, which helps in extracting insights and making predictions.
Read moreThe practice of clearly communicating and documenting data sources, methods, limitations, and assumptions to build trust and enable others to understand and evaluate the analysis process.
Read moreThe confidence that individuals and organizations have in the accuracy, reliability, and integrity of data, which determines how willing they are to use it for decision-making.
Read moreThe distinction between meaningful patterns (signal) and random variation or irrelevant data (noise) that can obscure true insights or lead to false conclusions.
Read moreThe critical distinction between two variables changing together (correlation) and one variable directly influencing another (causation), a common source of misinterpretation in data analysis.
Read moreA measure indicating the likelihood that an observed relationship or difference in data occurred by random chance rather than reflecting a real effect or pattern.
Read moreThe assessment of whether a statistically significant finding is large enough to be meaningful in a real-world context and worth acting upon from a business perspective.
Read moreA range of values that likely contains the true value of a population parameter, expressing the reliability of an estimate and helping to communicate uncertainty in data analysis.
Read moreThe range of values above and below a sample statistic within which the actual population parameter is likely to fall, representing uncertainty in measurements and estimates.
Read moreData points that differ significantly from other observations in a dataset, potentially indicating measurement errors, unusual events, or important insights that warrant further investigation.
Read moreThe statistical phenomenon where extreme measurements tend to be followed by measurements closer to the average, often mistaken for meaningful trends or effects of interventions.
Read moreA cognitive error that occurs when people ignore relevant statistical information (the base rate) in favor of specific but potentially misleading case information when making probability judgments.
Read moreA systematic error that occurs when a sample is collected in a way that prevents certain members of the population from being included, leading to non-representative results and flawed conclusions.
Read moreThe tendency to search for, interpret, and recall information that confirms one's pre-existing beliefs while giving less consideration to alternative possibilities.
Read moreThe tendency to rely too heavily on the first piece of information encountered (the "anchor") when making decisions, with subsequent judgments adjusted relative to this initial reference point.
Read moreThe tendency to view complex situations in oversimplified binary terms, overlooking nuances, middle ground, and alternative options that exist between two extremes.
Read moreThe cognitive bias where people's decisions are influenced by how information is presented (framed), rather than by the objective information itself.
Read moreThe mental shortcut where people base judgments on information that comes readily to mind, overestimating the likelihood or importance of things they can easily recall.
Read moreThe total amount of mental effort being used in working memory, which impacts decision quality and analytical capacity when processing complex data and information.
Read moreFrameworks or cognitive representations that help organize information, explain processes, and predict outcomes, serving as tools for understanding complex systems and making better decisions.
Read moreThe combination of human and artificial intelligence that leverages the complementary strengths of both to enhance decision-making, creativity, and problem-solving capabilities.
Read moreThe four levels of measurement that define the nature of data values and determine what kinds of statistical analyses can be meaningfully applied to a dataset.
Read moreThe distinction between detailed individual data points (raw) and data that has been combined, summarized, or processed (aggregated) to show trends or patterns.
Read moreThe difference between organized data with a predefined format (structured) and data that doesn't fit neatly into traditional databases or has an inconsistent format (unstructured).
Read moreMeasurements created by combining or calculating relationships between multiple primary metrics, often providing deeper insights than any single metric alone.
Read moreProcesses for moving data between systems, where ETL (Extract, Transform, Load) transforms data before storage and ELT (Extract, Load, Transform) stores data first then transforms it as needed.
Read moreInformation that describes other data, providing context about its content, quality, condition, and other characteristics to improve usability and discoverability.
Read moreTwo different approaches to data storage and management, where data lakes store vast amounts of raw data in native formats, while data warehouses contain structured, processed data optimized for specific business queries.
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