Data orchestration is managing and coordinating data from multiple sources to create a cohesive, consistent view of that data. Data orchestration includes tasks such as data integration, data quality management, and data governance.
Data orchestration aims to provide a single source of truth for an organization’s data. This can be challenging because data often comes from disparate systems that use different formats and standards. Data orchestration solutions must be able to handle this complexity in order to deliver accurate and up-to-date information.
The 3 Steps of Data Orchestration
The first step of data orchestration is to organize your data. This means structuring and labeling your data so that it can be easily accessed and understood. This can be done manually or with the help of software tools.
The second step of data orchestration is to transform your data. This means converting your data into the format that you need for your specific use case. This can be done using programming languages or specialized tools.
The third and final step of data orchestration is to activate your data. This means making your data available for use by other systems or users. This can be done by exporting your data to a file or database or by providing an API interface.
Why Is Data Orchestration Important
Data orchestration is the process of managing data flows between different systems. It is a key part of data management and helps to ensure that data is consistently managed across all systems.
Data orchestration can help to improve efficiency and accuracy by automating tasks that would otherwise be manual. It can also help to reduce costs by eliminating the need for duplicate data entry. Additionally, data orchestration can help to improve decision-making by providing a single view of data from multiple systems.
Overall, data orchestration is an important part of managing data effectively. Automating tasks and providing a single view of data can help to improve efficiency, accuracy, and decision making.