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– Visualizing your data landscape: By slicing and dicing the data landscape in different ways, what connections, relationships, and outliers can be found? Automated tools work by examining and cataloging metadata. Automated metadata discovery is the secret sauce by which the relationships between data sources can be found.
Processes produce, process and consume data –information captured in the metadata layer. Organizations and specifically the C-suite are demanding to see risk profiles at different slices and dices of a particular process. Now, let’s add the process layer to the equation. Because that is where the GRC and data layers meet.
Any type of metadata or universal data model is likely to slow down development and increase costs, which will affect the time to market and profit. In both cases, semantic metadata is the glue that turns knowledge graphs into hubs of data, metadata, and content. The diagram below illustrates this in a simplified form.
For sales leaders, what’s hugely empowering is the ability to slice and dice data on the fly, understand what team and individual reps should be achieving, and easily measure the team from a data driven standpoint. After creating the daily snapshot, then calculate the metadata such as: how many times is that opportunity pushed?
Dimensions provide answers to exploratory business questions by allowing end-users to slice and dice data in a variety of ways using familiar SQL commands. SCD2 metadata – rec_eff_dt and rec_exp_dt indicate the state of the record. It is also called the surrogate key and has a unique value that is monotonically increasing.
Interactivity can include dropdowns and filters for users to slice and dice data. Metadata Self-service analysis is made easy with user-friendly naming conventions for tables and columns. They can be presented in the context of a single chart or in a collection of visualizations in a dashboard. addresses).
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