data mart

Core meaning

A **data mart** is a subject-focused subset of an enterprise data warehouse or other centralized data store. It is structured to support the analytic, reporting, or monitoring needs of a specific business area, function, or use case.

In manufacturing and industrial operations, a data mart commonly contains curated, cleaned, and modeled data related to a defined topic, for example:

– Scrap and rework data across plants
– Batch or lot genealogy and quality results
– Maintenance events and equipment downtime
– Production orders and material movements

Data marts typically:

– Are derived from one or more operational systems (e.g., MES, LIMS, CMMS, ERP, historians)
– Use a consistent, documented data model geared to analysis (e.g., dimensional or star schemas)
– Contain a limited, purposeful scope rather than full operational detail

Use in industrial and regulated environments

In regulated manufacturing, data marts are often used to:

– Provide stable, validated structures for recurring reports and KPIs
– Support investigations and trend analysis without directly exposing full operational systems
– Consolidate data from OT and IT systems into a common analytical view

For example, a “scrap and yield” data mart may integrate MES event data, ERP order data, and quality results to allow engineers to analyze scrap patterns by product, line, shift, and supplier.

Boundaries and what a data mart is not

A data mart:

– **Is not** a raw operational system (e.g., MES, SCADA, historian). It usually contains cleaned, conformed, and sometimes aggregated data, not live control data.
– **Is not necessarily** the full enterprise data warehouse. It is usually smaller in scope and focused on a limited set of subject areas or stakeholders.
– **Is not** just a single report or dashboard. It is an underlying data structure that can support many reports and analyses.

Data marts may be:

– **Dependent** (built from a central data warehouse)
– **Independent** (built directly from operational systems)
– **Logical or virtual** (implemented via views over shared storage or lakehouse structures)

Common confusion and related terms

– **Data mart vs. data warehouse**: A data warehouse is enterprise-wide and integrated across many subject areas; a data mart is limited to a particular domain (e.g., quality, maintenance, finance) or audience.
– **Data mart vs. data lake**: A data lake is usually a large repository of raw or lightly structured data. A data mart is typically modeled, structured, and optimized for known analytic uses.
– **Data mart vs. operational data store (ODS)**: An ODS often holds near-real-time, integrated operational data for day-to-day processing. A data mart is mainly for analytics and historical reporting.

Site context: protecting confidential process information

When collaborating on topics such as scrap reduction with internal or external partners, organizations may use a data mart to:

– Expose **aggregated and anonymized** production or scrap data instead of detailed process parameters
– Limit access to **only those tables, fields, or time windows** relevant to the collaboration
– Implement **role-based views** that mask or omit proprietary recipes, control logic, or sensitive commercial data

In this context, a data mart acts as a controlled analytical layer, separating joint problem-solving data from full-process disclosure while still supporting meaningful analysis.