Turn raw source data into curated, analysis ready datasets in Oracle Analytics Cloud by joining sources, adding calculated columns, and organizing outputs for reporting. The workflow also shows how to filter one branch for U.S. reporting and save a second global dataset, making data preparation scalable across teams. Check out additional resources at the links below.
This tutorial shows how to create a data flow in Oracle Analytics Cloud and Fusion Data Intelligence to turn raw order line data into analysis ready datasets. The workflow begins by selecting a source dataset, removing columns that are not needed, and protecting sensitive information that should not appear in analysis. It then enriches the data by joining a geography dataset, adding calculated columns to classify target attainment, and using binning to group results into meaningful performance bands. The video also shows how to branch the data flow so one output can be filtered for United States reporting while another remains global, giving teams two curated datasets from one reusable process. Before running the data flow, the configuration is reviewed to confirm column types and default aggregations, reinforcing reliable dataset output. The result is a scalable way to prepare consistent data for reporting without manual coding, supporting clearer analysis and more efficient analytics workflows across the business.
Check out detailed documentation about how data flows enable you to organize and integrate your data to produce curated datasets that your users can visualize: https://social.ora.cl/6009BBgnCv
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0:00 Introduction to Data Flows
0:44 Data Flow Setup
1:47 Data Enrichment and Transformation
2:46 Creating Analytical Outputs
3:46 Validating and Reviewing Output Datasets
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