Analyze Data with Explain

Explain uses machine learning to find useful insights about your data.

What is Explain?

Explain analyzes the selected column within the context of its dataset and generates text descriptions about the insights it finds. For example, for any column you'll find out basic facts, key drivers, segments that explain the column, and anomalies.

Explain uses Oracle's machine learning to generate accurate, fast, and powerful information about your data, and creates corresponding visualizations that you can add to your workbook's canvas.

Explain is for data analysts who might not know what data trends they're looking for, and don't want to spend time experimenting by either dragging and dropping columns onto the canvas, or using data flows to train and apply predictive models.

Explain is also a useful starting point for data analysts to confirm a trend that they're looking for in their data, and then use that information to create and tune predictive models to apply to other datasets.

What Are Insights?

Insights are categories that describe the selected column within the context of its dataset.

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The insights that Explain delivers are based on the column type or aggregation that you chose and will vary according to the aggregation rule set for the chosen metric. Explain generates only the insights that makes sense for the column type that you chose.

Insight Type Description
Basic Facts Displays the basic distribution of the column's values. Column data is broken down against each of the dataset's measures.
This insight is available for all column types.
  • For a selected metric, this insight shows the distribution of the aggregated metric value for each member of each attribute column.
  • For a selected attribute, this insight shows the value of each metric in the dataset across the member values of the attribute.
Key Drivers Shows the columns in the dataset that have the highest degree of correlation with the selected column outcome. Charts display the distribution of the selected value across each correlated attributes value.

This tab displays only when explaining attribute columns, or when explaining a metric column that has an average aggregation rule.

Segments Displays the key segments (or groups) from the column values. Explain runs a classification algorithm on the data to determine data value intersections and identifies ranges of values across all dimensions that generate the highest probability for a given outcome of the attribute.

For example, a group of individuals of a certain age range, from a certain set of locations, with a certain range of years of education form a segment that has a very high probability of purchasing a given product.

This tab displays only when explaining attribute columns.

Anomalies Identifies a series of values where one of the (aggregated) values deviates substantially from what the regression algorithms expect.

Use Explain to Discover Data Insights

When you select a column and choose the Explain feature, Oracle Analytics uses machine learning to analyze the column in the context of the dataset. For example, Explain searches the selected data for key drivers and anomalies.

Explain displays its findings to you as text descriptions and visualizations, which you can add to your workbook's canvas.
If you perform explain on a column and the results contain too many correlated and highly ranked columns (for example, ZIP code with city and state), then excluding some columns from the dataset so that Explain can identify more meaningful drivers. See Hide or Delete a Column.
  1. In the Home page, click Create and then Workbook to create a new workbook.
  2. Click Visualize to open the Visualize page.
  3. In the Data Panel, right-click a column and select Explain <Data Element>.

    You must have write access to the dataset for the Explain <Data Element> option to display.
    For Explain to successfully analyze an attribute, the attribute must have three to 99 distinct values.
    The Explain dialog displays basic facts, anomalies, and other information about the selected column.
  4. Review the suggested insights in other categories by clicking on the tabs. For example, Basic Facts about <attribute> or Anomalies of <attribute>.
  5. For each insight that you want to include in your workbook's canvas, hover over it and click Select for Canvas.
    You'll see a green tick (Green Tick) next to selected items.

    You can select multiple insights from any of the tabs.
  6. Click Add Selected to add insights marked with a green tick on any of the tabs.
    You can manage the Explain insights like any other visualizations you’ve manually created on the canvas.
To fine-tune the insights, click Settings to change which columns are analyzed and configure options for that category, for example, select the minimum size for segments.