Data Tables

A data table is more than just a spreadsheet, Catalytic data tables take static spreadsheet data and open it up to automation.

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Every Pushbot stores data in a unique master data table, where data from all past instances are stored. Data tables are also made by uploading spreadsheets or using actions to create them in your automation.

Example of use

One Pushbot may have multiple data tables, and one data table may be used by multiple Pushbots. Since a data table can store anything a spreadsheet would–a log of activity, a list of items, a calculation of totals––it’s used across all automation.

For example, you could set up a data table to track every vendor your company works with: each row of the table would be a different vendor and each column would be an attribute of the vendor, like name, address, pricing, or satisfaction.

With could leverage and reference this table across different Pushbots:

  • In a Vendor Approval Pushbot, use the Tables: Add a row action to add that vendor to the Vendors table once approved.
  • In a Vendor Selection Pushbot, use the Tables: Apply filters action to filter the table only to vendors within a specific category.
  • In a Vendor Renewal Pushbot, use the Tables: Start Pushbot for Each Row action to initiate a renewal instance for every vendor.

View and manage data tables

Every data table you create or have shared with you is tracked and listed on the data tables screen.

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Access the data table screen by selecting Data from the top of any Catalytic screen.

For each Data Table, you can see its name, who created it, and when it was created. Select a data table to open, view, and edit the data in a spreadsheet style view.

Create a new data table

From the data tables screen, create a new data table with the  button. This opens up a new page to name, configure, and create a new Data Table. Any new data table can be associated with a Pushbot as an add-on by using the Data Table ID somewhere in a Pushbot’s actions.

Change data table permissions

Set data table permissions to restrict which users can see select data tables. This data table permissions is set at the data table level and is never overwritten by the permission levels of any instances or data table fields it is shown or used in.

  1. From the top navigation bar, select Data, then select a data table.
  2. Select the button and select Permissions. This opens a new screen to change permissions.

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  3. From the permissions page, toggle Enforce table permissions on or off. If disabled, any user on the team may access or edit the data table.
  4. Add or remove the individual users who can view the table. Any user not on the list cannot see or access the data table.

Sort, filter, and search through your data tables

Organize and sort your data tables using filters. By default, the data tables page will show an alphabetized list of all data tables that are from active Pushbots from your team.

You can filter the data tables by type or status.

In addition to sorting by A-Z, you can sort by relevancy or by created date.

Quickly filter and search through data tables by entering parameters into the filter items search box. Enter terms like “Sep” or “Aug” to return all data tables with those terms in their name or description.

Archive a data table

Archived data tables will no longer appear in the default list of tables. Archiving a data table is reversible and not permanent, though archived tables cannot be used as part of a Pushbot.

  1. Select in the top right corner and select Archive
  2. Select

To unarchive a data table, return to your list of data tables, then change the status filter on the data table page from Active to Archived to view all archived data tables.

Working with data tables

Using data table specific actions

Tips

  • To access a Table ID, open the data table to find the table ID. When viewing the table, its ID is available at the end of the URL.
    • Example: For https://example.pushbot.com/tables/5dbdd4b4-8898-4004-ae85-1e98f6f64372, the table ID is 5dbdd4b4-8898-4004-ae85-1e98f6f64372
  • Use a data table as the basis for a predictive model to automatically predict or recommend field values based on a machine learning algorithm.