Data Orchestration

Data orchestration is a general term primarily used by data management vendors. It refers to the process of coordinating and managing the flow of data within an organization or between different systems (often referred to as data silos) and platforms. It involves the arrangement, coordination, and optimization of data workflows to ensure that data is efficiently and effectively moved, processed, and utilized. The term data orchestration can refer to unstructured data and structured / semi-structured data.

Key Aspects of Data Orchestration


  • Data Integration: Bringing together data from various sources, such as databases, applications, and external APIs, to create a unified and cohesive view of the information. Increasingly data integration vendors focus on data automation and data orchestration. Modern examples include Boomi and SnapLogic.
  • Data Movement: Transferring data between different systems, platforms, or storage locations. This may involve tasks such as ETL (Extract, Transform, Load) processes or real-time data streaming. Increasingly data orchestration is a term used for unstructured data management solution providers. Komprise Intelligent Data Management is an example.
  • Workflow Automation: Designing and automating data workflows to streamline processes and reduce manual intervention. This can include scheduling, triggering, and monitoring data tasks. Learn more about Komprise Smart Data Workflows.
  • Data Protection: As it relates to unstructured data, a data orchestration strategy should automate the movement of critical data to online and offline storage and include a comprehensive strategy for valuing, classifying, and protecting these data assets from user errors, malware and viruses, machine failure, or facility outages/disruptions, in addition to reducing data storage costs. See Data Protection.
  • Data Transformation: Modifying the structure, format, or content of data to meet the requirements of the target system or application.
  • Data Quality: Ensuring the accuracy, completeness, and consistency of data by implementing validation checks, cleansing processes, and error handling.
  • Metadata Management: Managing metadata to provide context, lineage, and documentation for better understanding and governance of the data.
  • Scalability and Performance Optimization: Optimizing data processes for performance, scalability, and resource efficiency, especially in large-scale data environments.
  • Security and Compliance: Implementing measures to ensure data security, privacy, and compliance with relevant regulations and policies.
  • Monitoring and Logging: Implementing tools and processes to monitor the health and performance of data workflows, detect issues, and log relevant information for troubleshooting.
  • Collaboration and Governance: Facilitating collaboration among different teams and stakeholders involved in data management. Establishing governance policies to ensure data is handled responsibly and in accordance with organizational standards.

Data orchestration is broad term used by many different types of technology vendors. It is not a term that has been embraced by enterprise IT teams and there is not a Gartner Market Guide or Magic Quadrant focused on Data Orchestration because it is such a broad term. That said, it is increasingly important as part of a data management and data lifecycle management strategy, where organizations deal with diverse data sources, formats, and volumes. The right approach to data orchestration helps private and public sector organizations derive value from their data by making it more accessible, reliable, and actionable. Various tools and platforms, including data integration tools, workflow automation tools, unstructured data management platforms, are used to implement and manage data orchestration processes.


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