Analytics & Data Consulting Glossary

A controlled experiment comparing two versions of a webpage, email, or feature to determine which performs better against a specific metric like conversion rate or engagement.

Extremely large datasets characterized by volume, velocity, and variety that require specialized tools and infrastructure for storage, processing, and analysis.

Technologies, applications, and practices for collecting, integrating, analyzing, and presenting business data to support better decision-making and strategic planning.

The percentage of customers or users who stop using a product or service within a given time period. Critical for measuring retention and long-term viability.

A centralized repository that allows you to store all your structured and unstructured data at any scale, without having to first structure the dataset.

The process of discovering patterns, correlations, and anomalies within large datasets to predict outcomes using statistics, machine learning, and database systems.

A series of data processing steps that collect, move, and transform data from one or more sources into a destination like a data warehouse or data lake.

A system used for reporting and data analysis. A data warehouse is a central repository of integrated data from one or more disparate sources, structured for query and analysis.

A three-step process used to move data from a source to a destination. Data is extracted, transformed to match the target schema, and loaded into a destination system.

A measurable value that demonstrates how effectively a company is achieving key business objectives. Tracking KPIs allows teams to focus on outcomes rather than activities.

A subset of AI focused on building systems that learn from data to improve their performance over time without being explicitly programmed for every rule.

The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data patterns.

A statistical method used to examine the relationship between a dependent variable and one or more independent variables, often used for forecasting and impact analysis.

The use of NLP and text analytics to identify and extract subjective information from source materials, typically to determine if feedback is positive, negative, or neutral.

The use of analytical procedures that deal with time series data to extract meaningful statistics and other characteristics from the data, often for forecasting trends.

A type of machine learning where models learn patterns from unlabelled data without explicit feedback, commonly used for clustering, association, and anomaly detection.