Managing Custom Datasets

Overview

Datasets are the data source for analyses and visuals. Expertise Insights comes with several standard datasets that refresh on an automatic cadence. You can also create your own datasets using external sources to add more data to Expertise Insights. This allows you to connect Kantata data with your external data for a complete, unified view.

To view datasets, hover over Insights in the left navigation, then select Dashboards Home. Select Datasets from the left menu.

The following information is displayed on the Datasets page:

• Dataset Type icon
• Name
• Owner
• Last modified—For the standard datasets, this timestamp indicates when the dataset was last refreshed. Standard datasets refresh every hour.

Select the More icon for a dataset to access the following options:

• Create analysis
• Edit *
• Add to folder
• Use in a new dataset
• Duplicate *
• Manage permissions *
• Row-level security *
• Column-level security *
• Delete

* These options are only available for custom datasets.

How to Create a Dataset from New Data Sources

You can add data to Expertise Insights from external sources such as files, Salesforce, Google Sheets, Amazon S3, MySQL, and more.

1. In the left navigation, hover over Insights, then select Dashboards Home.
2. Select Datasets from the left menu.
3. Select the New dataset button in the upper-right of the page. The Create a Dataset page opens, displaying a list of data sources.

4. Select a data source. The data sources listed below can be selected.

   Data sources

   • Upload a file: .csv, .tsv, .xlsx, or .json
   • Salesforce
   • Amazon S3 Analytics
   • Amazon S3
   • Amazon S3 Tables—Apache Iceberg tables
   • Amazon Athena
   • Amazon RDS
   • Amazon Redshift—Auto-discovered
   • Amazon Redshift—Manual connect
   • MySQL
   • PostgreSQL
   • ORACLE
   • SQL Server
   • Amazon Aurora
   • MariaDB
   • Presto
   • Spark
   • Teradata
   • Snowflake
   • Google BigQuery
   • Amazon OpenSearch Service
   • Amazon Timestream
   • Exasol
   • Databricks
   • Trino
   • Starburst
   • Impala
   • GitHub
   • Jira
   • ServiceNow
   • Adobe Analytics
   • Google Sheets
   Tip: See the Connect to your data external article for guidance on each data source.
5. Follow the prompts to upload or connect the data source.

6. To prepare the data before creating the dataset, select Edit/Preview data.

   Tip: Preparing data allows you to make customizations like changing field names and adding calculated fields. When customizations are done at this stage, the changes will be available to all analyses that use the dataset, so the customizations only need to be done once.
7. Update the dataset name in the field at the top. This name will be visible to other Authors.
8. In the Fields panel to the left, select the More icon for a field to access the following options:
   • Edit name & description
   • Add calculation—Create a calculated field using this field. The formula editor opens, with the selected field pre-populated in the editor. For information on the available functions and operators, see the Calculated field function and operator reference external article.
9. Select Add to the upper-left to access the following options:
   • Add calculated field—Create a new field that transforms data using mathematical operations, conditional logic, string manipulation, and more. For information on the available functions and operators, see the Calculated field function and operator reference external article.
   • Add filter
   • Add parameter
   • Add predictive field
10. Select Add data to the upper-right to add more data from other datasets, external sources, or files.
11. Select a save option:
    • Save & publish—Saves changes.
    • Publish & visualize—Save changes and open the analysis editor for a new analysis/begin editing a new analysis using this dataset in one step.