Data Orchestration Services execute data pipelines that are taking siloed data from multiple data storage locations, coordinating the combining, verifying, organizing and storing of that data and making it available for data analysis tools. Data orchestration services evolved from manual orchestration efforts with an emphasis on automation, conceptualization and analytics to support optimization. While data orchestration services operate within larger workflows, the actual work they accomplish can vary from between different implementations. By and large, however, these tasks fall into five primary parts: * Collecting and Preparing Data -- More often than not, data must be structured and prepared before it enters or moves through a system. This includes performing checks for integrity and correctness, applying labels and designations, or enriching new third-party data with existing database information. * Transforming Data -- Not all data will work in the same system as is. Orchestration will inevitably apply transformations to pieces of data to ensure that it plays well within a given task. This contributes to creating an “omnichannel” view of the data relevant to a given application. * Automating Enrichment and Stitching -- Based on conditions with data, orchestration systems can start performing tasks like documenting and reporting on data, cleaning up duplicated data, and so on. * Decision-Making around Data -- A data orchestration schema will then start to make decisions that can weight, rank, organize or curate that data based on rule-based criteria. AI models may also drive intelligent decision-making around data orchestration. * Synchronizing -- Finally, writing data to a data store, data lake or data warehouse, depending on where it needs to be.