| Term | Definition |
|---|
| AI Agent | An artificial intelligence tool that autonomously operates across different systems to deliver specific results and assist businesses in various operations. |
| AI-ready Data | Data that has been prepared to support artificial intelligence applications. |
| API | A software interface that enables different applications and systems to communicate and exchange data with each other.
"API" stands for "Application Platform Interface", "Application Program Interface" or "Application Programming Interface" |
| API Management Platform | A tool for designing, publishing and managing APIs. |
| Application Integration | A process that enables communication and data sharing between different software applications through a common framework. |
| Automated Data Management | A system that enables data to synchronize automatically between different applications and sources.
It reduces the need for manual data entry, unifies data formats, and minimizes errors in data handling. |
| Automated Deployment | A method of managing software deployment through scripts or deployment interfaces that reduce project duration by eliminating manual configuration changes. |
| Batch Data Integration | A method of processing data where information is collected and stored until a specified amount is gathered, then processed all at once as a batch.
It allows scheduled integration at regular intervals, optimizes resource allocation, and improves performance for high-volume data transformation, being particularly useful when real-time analysis isn't required. |
| Business Capability | A core functionalities of an organization.
Business capabilities can be enhanced and differentiated by combining data and applications in unique ways. Organizations can improve them through data integration and analysis to create better user experiences and operational efficiency. |
| Business Intelligence | A technology-driven process that transforms integrated data into meaningful visualizations, reports, and dashboards to support strategic decision-making. |
| Change Data Capture (CDC) | A process that captures and replicates data changes from source systems to target systems in real-time or near real-time.
This approach enables continuous data synchronization across different systems. It helps maintain data consistency and enables timely data integration without impacting the performance of source systems. |
| Clean Data | A state of data quality achieved through an ongoing process of detecting, correcting, and maintaining accurate, consistent, and up-to-date information. |
| Cloud Migration | The process of transferring legacy databases to cloud computing platforms.
It is often implemented gradually using data integration strategies like middleware integration to maintain business continuity. |
| Common Data Model | A standardized data model that provides a unified way to represent and understand data across an organization.
It enables all integration processes to speak the same language, facilitating future integrations and allowing for easier creation of services and events involving business objects. |
| Company-wide Standards | Established standards for data entry and maintenance that ensure data is kept clean, updated, and organized across an organization.
They include documented processes for application connectivity and designated responsibilities for quality and management. |
| Cross-team Collaboration | A strategic approach to data integration where multiple teams, including IT, marketing, and sales, work together from early stages to align priorities and ensure integration success. |
| Customer 360 View | A comprehensive, unified collection of customer data integrated from multiple business systems and sources that provides a complete picture of customer interactions and behavior. |
| Customer Relationship Management (CRM) | A system that helps businesses improve and manage their customer relationships through various tools and applications. |
| Data Catalog | A tool that helps businesses find and inventory data assets. |
| Data Cleansing Tool | A tool that cleans up dirty data by replacing, modifying, or deleting it. |
| Data Connector | A tool for moving data between databases while handling necessary transformations in the process. |
| Data Coupling | A measure of how tightly data is bound to specific applications or systems, affecting its flexibility and reusability across different contexts. |
| Data Democratization | The ability for virtually every user to access data for making everyday business decisions. |
| Data Federation | A technique that creates a virtual unified database layer over multiple data sources, enabling integrated access to distributed data without physically moving or copying it. |
| Data Governance | A systematic approach to managing data assets through consistent policies, controls, and procedures that ensure data quality, security, and compliance across an organization.
It relies on metadata and tagging to track data lineage and enforce access controls. |
| Data Governance Tool | A software solution that helps organizations implement and maintain practices ensuring the availability, security, usability, and integrity of their data. |
| Data Ingestion | A component of data integration that focuses on collecting, importing, and moving data from various sources into a target destination with minimal initial transformation.
This process serves as the foundation for subsequent data processing and analytics workflows. |
| Data Ingestion Tool | A software components that facilitates the collection, import, and transfer of data from diverse sources into target systems for immediate processing or storage. |
| Data Integration | The process of combining and unifying data from multiple sources into a consolidated view to enable analysis, derive business value, and provide consistent access across an organization. |
| Data Lake | A centralized storage repository that holds large volumes of structured, semi-structured, and unstructured data in its native format from various sources. |
| Data Lineage | The end-to-end tracking of data flow that helps ensure data governance and maintain compliance according to corporate policies.
It provides visibility into how data moves and transforms throughout the organization. |
| Data Mapping | The process of defining how data elements from different systems correspond to each other to ensure proper data alignment during integration. |
| Data Mart | A focused repository of data specific to particular business functions or departments. |
| Data Mesh | A federated solution where every business unit manages its data independently but presents it to others in a centrally defined format. |
| Data Migration | The process of transferring data between different computers, storage systems, or application formats. |
| Data Quality | The measure of accuracy, consistency, reliability, and relevance of data |
| Data Replication | A technique that creates and maintains duplicate copies of data across different systems. |
| Data Security | The protection of data during transit and at rest. |
| Data Silo | An isolated collection of data within an organization.
Data silos create barriers to information sharing and unified access across departments. |
| Data Synchronization | The process of maintaining consistent data across multiple systems or locations through continuous or periodic updates. |
| Data Transformation | The process of converting and structuring data into a format suitable for target applications and systems. |
| Data Virtualization | A data integration approach that creates a virtual access layer enabling unified access to multiple data sources without physically moving or duplicating the data from its original locations.
The virtual layer abstracts away the technical complexity of accessing diverse data sources, allowing users to query and manipulate data as if it were in a single database. |
| Data Warehouse | A centralized repository that consolidates and stores integrated data from multiple sources for business analytics, reporting, and analysis purposes.
The data is structured and organized to facilitate efficient querying and access for business intelligence needs. |
| Datastream | A continuous data integration service that enables real-time processing and synchronization of data as it flows through a system.
It automatically captures and processes data changes as they occur, allowing for immediate analysis and response. |
| DevOps | The combination of software development and IT operations teams that focus on building, testing, and deploying applications. |
| ETL Tool | A software applications that facilitates the Extract, Transform, Load process. |
| Extract, Load, Transform (ELT) | A data integration process where data is first extracted from source systems, loaded into a target system, and then transformed within that target environment.
This approach differs from traditional ETL by performing transformations after loading, leveraging the processing power of modern data warehouses and cloud platforms. |
| Extract, Transform, Load (ETL) | A data integration technique where data is physically extracted from multiple source systems, transformed into a different format, and loaded into a centralized data store. |
| Federated Data Integration | A strategic approach to data management where data remains in its original source systems while enabling real-time querying across distributed sources. |
| Integrated Data | A unified collection of information from multiple sources that provides a complete and accurate view of business operations, enabling informed decision-making across an organization. |
| Integration Solution | A software system that enables the combination and coordination of multiple subsystems or components into a unified whole, allowing different data systems to work together and share information effectively. |
| IoT Data Processing | The integration, analysis and management of data collected from Internet of Things devices and sensors to derive actionable insights. |
| Legacy System | An older but vital business technology platform that contains critical historical data and core functionalities.
These systems typically run on outdated architectures that may not easily support contemporary integration methods. |
| Logical Data Integration | The process of organizing and connecting data from multiple systems through schema mapping and transformation rules to enable seamless data sharing and maintain consistency. |
| Master Data Management (MDM) | A system that helps to establish and maintain consistent, accurate master data definitions and classifications across an organization to create a single source of truth. |
| Metadata | Data about data.
It helps users understand the data's context, source, and meaning, and enhances discoverability and usability. |
| Physical Data Integration | A data integration approach that involves the actual movement and consolidation of data from multiple source systems into a centralized target system. |
| Real-time Data Integration | The immediate capture, processing, and synchronization of data from source systems to target systems as it becomes available.
This approach enables organizations to make immediate data-driven decisions based on the most current information. |
| Single Source Of Truth | A consolidated data repository that resolves issues of duplicates, inconsistencies, and outdated information across disconnected systems. |
| Streaming Data Integration | Real-time processing and integration of continuously flowing data from dynamic sources like IoT devices, sensors, and social media feeds. |
| Zero-Copy Integration | A method that enables direct connection to and analysis of data from multiple sources without creating physical copies or moving the data from its original location. |
