The term "data provisioning" refers to the structured delivery of data from various sources to other systems, applications, or users. The goal is to make data accessible, usable, and ready for further processing—whether for analysis, operational purposes, or integration into third-party systems. This may involve raw data, cleansed datasets, or aggregated information, provided manually, automatically, or in real-time.
Data Export: Delivering data in the desired format (e.g., CSV, XML, JSON) for external use or further processing.
Data Integration: Consolidating data from various sources (e.g., ERP systems, CRM, sensors) into a central data platform.
Interface Management (API/EDI): Making data available via standardized interfaces for machine-based use by other systems.
Data Access Control: Defining who can access which data, when, and to what extent.
Real-Time Data Delivery: Transmitting data in real time, e.g., for machine control or live analytics.
Data Modeling and Transformation: Structuring and contextualizing raw data into understandable and usable information.
Report Generation: Automated creation and distribution of reports with defined data content for specific target audiences.
Cloud-Based Data Provisioning: Providing access to data via cloud storage or data-sharing platforms for internal or external partners.
A production system delivers sensor data in real time via an API to a MES (Manufacturing Execution System).
A controlling team exports consolidated financial data from the ERP system for further processing in Excel or Power BI.
A data warehouse integrates data daily from several source systems and provides it in a structured format for reporting.
An energy provider shares consumption data with customers via a self-service portal.
A company provides third-party systems with aggregated usage statistics via a REST API.