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Convert FDI Workbook Tables into Datasets
Providing additional context to the summary given during the Oracle Data Visualization Challenge Feedback session: users would greatly benefit from the capability to convert workbook tables directly into datasets (without the need to export data out of FDI). Conventional variance analysis efforts (which involve combining multiple data points/sources) are growing in scale exponentially thanks to FDI's seamless approaches to cleanse, combine, and transform data via dataset editor and data flows. As a result, the initial summary dashboards created offer more paths for individual analyses and data explorations than ever before. Data actions and parameters provide helpful approaches to isolate points of interest, yet the underlying number of records that make up a target value in aggregate can be vast. As such, users might simply export a series of tables (each containing the relevant attributes and dimensions for a particular insight or variance) as csv/xlsx outputs to externally complete individual, more granular analyses via excel/essbase techniques. If users had the direct ability to click to convert their workbook tables into FDI datasets (equipped with access sharing/permission setting rights), advanced features such as Explain and Auto-Insights could become more accessible/interpretable for business analysts who are new to the tool. A functionality to lock the converted tables as 'point-in-time' references for a given date (e.g., quarter end) would enable users to create and archive variance analysis/commentary packages that enable leaders to quickly view all relevant decision-making/reporting details (from highest to lowest level). An option to pin (within the workbook notes) or link these packages (with a clickable url) to historical data points contained in the original summary dashboard would be an added plus. Finally, the ability to set the converted tables as dynamic datasets (that update as records change over time) would be a key feature for ongoing monitoring and predictive model training, as users become more confident in the specific dimensions and attributes most pertinent to their strategic organizational objectives