Analytics and AI Glossary

Advanced analytics uses techniques like AI, machine learning, and predictive modeling to uncover insights, forecast trends, and improve decision-making.

Agentic AI refers to autonomous systems that act, learn, and optimize workflows without human prompts—transforming finance, healthcare, and operations.

Agentic analytics uses AI to enable autonomous, adaptive, and real-time decision-making for dynamic business insights and outcomes.

Analytics automation uses technology to streamline data collection, analysis, and reporting, enabling faster, accurate, and real-time insights.

Learn what Artificial Intelligence is, why it matters, and how it powers automation, insights, and self-directed decision-making in analytics.

Automated reporting uses software to generate reports, saving time, reducing errors, and delivering real-time, data-driven insights.

Big Data refers to large, fast, and diverse datasets. It powers AI and analytics by uncovering patterns, trends, and insights for better decisions.

Business Intelligence turns raw data into insights using tools like dashboards, AI, and reports to support faster, smarter decision-making.

Cloud data integration unifies data from multiple sources in the cloud, enabling real-time access, scalability, and seamless collaboration.

Data analytics is the process of examining raw data to identify trends, patterns, and insights that drive better decisions and business outcomes.

Data blending merges datasets from multiple sources for unified analysis, enabling deeper insights and better data-driven decisions.

Data cleaning removes errors, duplicates, and inconsistencies to ensure datasets are accurate, complete, and reliable for analysis.

Data completeness ensures all required information is present in a dataset, enabling accurate analysis, reliable insights, and better business decisions.

Data conformity ensures datasets follow defined rules and formats, enabling consistency, accuracy, and seamless integration across systems.

Data enrichment adds external or internal context to existing datasets to improve accuracy, segmentation, and decision-making.

Data governance ensures data quality, security, and compliance. It defines roles, policies, and controls for trustworthy, consistent analytics.

A data lake stores raw structured and unstructured data at scale for advanced analytics, ML, and real-time insights.

Data lineage maps the flow and transformation of data from source to destination, ensuring integrity, governance, and audit readiness.

Data mapping is the process of matching fields between data sources to ensure consistency, accuracy, and seamless integration in ETL processes.

Data modeling creates structured representations of data and relationships, ensuring consistency, integration, and efficient database design.

Data onboarding is the process of importing, validating, and integrating new data into a system, ensuring quality, consistency, and usability.

Data profiling analyzes and summarizes datasets to assess structure, quality, and patterns, ensuring accuracy and readiness for analytics.

Data quality measures accuracy, completeness, consistency, and timeliness of data to ensure reliable insights and effective decision-making.

Data Quality Management ensures accurate, consistent, and reliable data through profiling, cleansing, monitoring, and governance practices.

Data security protects sensitive digital information from breaches, corruption, and theft. Learn key strategies and tools.

Data standardization converts data into consistent formats, ensuring accuracy, compatibility, and easier integration across systems.

Data validation ensures accuracy, consistency, and integrity by checking data against predefined rules before analysis or reporting.

Data visualization uses charts, graphs, and visuals to present data clearly, helping identify patterns and support faster, informed decisions.

A data warehouse is a centralized repository for structured data, optimized for querying, reporting, and business intelligence insights.

A data workflow is the sequence of steps to collect, clean, analyze, and automate data for accuracy, efficiency, and better decisions.

Data wrangling is the process of cleaning and transforming raw data into a structured format for reliable analysis and insights.

Deep learning is a subset of machine learning that uses multi-layered neural networks to analyze data, recognize patterns, and power AI applications like vision and NLP.

Descriptive analytics reviews historical data to uncover trends, patterns, and insights, answering the question “What happened?”

Dimensional modeling structures data into fact and dimension tables for efficient querying, reporting, and business intelligence insights.

Embedded analytics integrates dashboards, reports, and insights directly into software applications, enabling real-time, in-context decision-making.

ETL (Extract, Transform, Load) is a data process to prep and consolidate data for analysis. Learn how it compares to ELT and modern data workflows.

Generative AI creates new content like text, images, or code by learning patterns from data, transforming industries with automation and creativity.

A Large Language Model (LLM) is an AI model trained on massive datasets to understand and generate human-like text, powering chatbots, content creation, and more.

What is Machine Learning, how it works, and how Deep Learning drives AI innovations like NLP, image recognition, and automation.

A modern data stack is a set of cloud-based tools for data ingestion, storage, ETL, and BI, enabling scalable, flexible, and real-time analytics.

Natural Language Processing (NLP) is an AI field that enables computers to understand, interpret, and generate human language for tasks like sentiment analysis and chatbots.

Predictive analytics uses machine learning and historical data to forecast trends, improve decisions, and reduce risk.

Prescriptive analytics recommends optimal actions using data, simulation, and AI to achieve desired outcomes and guide decision-making.

Regression analysis is a statistical method that examines relationships between variables to predict outcomes, identify trends, and guide decisions.

Semi-structured data has some organization using tags or labels but lacks rigid schema. Examples include JSON, XML, logs, and product reviews.

SOX compliance ensures financial transparency and internal controls to prevent fraud. Learn how it impacts governance and audits.

Structured data is organized in rows and columns, making it easily searchable and analyzable in databases, CRMs, and applications.

Supervised learning is a machine learning approach that trains models on labeled data to perform tasks like classification and regression.

Unstructured data lacks predefined format, including text, images, video, and social posts. It is harder to analyze but rich in insights.