Nogamy Data & Analytics Glossary | Definitions for BI, AI, and Data Governance

Semantic Layer

A semantic layer is the business logic layer that acts as the 'control room' of analytics, defining a unified, governed language for KPIs and core metrics. By centralizing business definitions, it ensures everyone across BI and analytics teams operates with a single source of truth.

Data Masking

Data masking acts as the safety net for trustworthy insights, systematically protecting sensitive information such as PII by disguising data in non-production environments, analytics, and access controls. In BI and AI, data masking is essential for privacy compliance and risk reduction during data-driven processes.

Attribution Modeling

Attribution modeling acts as the GPS for strategy through numbers, allocating conversion credit across marketing channels and customer journey touchpoints to clarify which efforts truly move the needle in eCommerce, SaaS, and digital product sectors.

Data Anonymization

Data anonymization is the 'safety net for trustworthy insights'—it transforms personal or sensitive information into a form where individuals cannot be identified, supporting privacy-preserving analytics and regulatory compliance for sectors handling sensitive data.

PaaS (Platform as a Service)

PaaS (Platform as a Service) is the city plumbing for digital innovation, offering a managed, scalable cloud platform for deploying and running applications without the burden of infrastructure maintenance. In BI and AI, PaaS accelerates the delivery of analytics and smart services by abstracting away server and OS management.

Data Lineage

Data lineage acts as the nervous system of analytics, mapping the flow of data from source to consumption, meticulously recording every transformation and access point. For BI and AI teams in regulated sectors, robust data lineage is essential for compliance, transparency, and error-tracing.

Data Pipeline

A data pipeline is the city plumbing of analytics: a coordinated series of processes that reliably collect, process, and move data from sources to destinations, ensuring timely, clean, and accessible information for downstream BI and AI systems.

Data Integration

Data integration acts as the city plumbing for insights—combining streams from diverse pipelines into a single, usable flow for analytics and operations. In BI and AI, it enables organizations to bridge data silos, powering unified reporting and streamlined decision-making.

Data Mart

A data mart is like a specialized branch in the city plumbing for insights—an optimized data repository serving focused business domains or departments, enabling rapid, relevant, and secure analytics within BI and AI environments.

CDC (Change Data Capture)

CDC (Change Data Capture) operates as the city’s plumbing for insights—it enables organizations to move only the 'delta' or changed data, rather than performing full data loads between source and target systems, vastly optimizing BI and AI pipelines.

עיבוד באצ’ים

Batch processing is the city plumbing for insights—scheduled data handling (often nightly or hourly) that transforms raw data into analytics-ready formats, rather than reacting in real time. It forms the backbone of efficient BI and ETL, especially for routine, high-volume data workloads.

פרופיילינג נתונים

Data profiling acts as the quality control station for analytics—identifying patterns, outliers, and data gaps before modeling or loading into BI systems. This process is critical for robust pre-modeling and data integration, forming the backbone of reliable analytics pipelines.

Glossary

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