2020.7 - DataClarity What's New and Release Notes

Data Preparation

Explore dataset (data catalog for datasets)

The number of organizations’ datasets keeps growing, and so does the demand for data cataloging features to help users quickly find the right datasets for further in-depth analysis. The new DataClarity’s dataset exploration feature is a detailed inventory of a dataset that allows you to review the dataset’s technical metadata, process metadata, and business metadata and decide if it suits the given analytical purpose. Identifying the dataset’s nuances at a glance will result in increased efficiency, a better understanding of the data, and faster analysis.

Using the Explore dataset pane, you can review the dataset by navigating through the following sections:

Overview tab

Get a comprehensive overview of the important dataset information gathered in one place: the business description, tags, the date of the last update, the endorsement information, the list of users with authoring or management permissions, the list of top users, and the dataset popularity (in views).


If the dataset has row-level security applied, the corresponding icon appears on this tab.

The users who can edit the dataset can update the description and tags right in this pane and have the Edit button visible in the upper-right corner.

Data tab

Explore the dataset’s actual data values, evaluate the data from different perspectives, review the distribution of values, group, filter, and pivot the data columns.

Columns tab

Get the technical details on each column in the dataset, the data role, data type, how popular it is (the number of views), and a business description.

Calculations tab 

Review all the calculated columns in detail: the formulas, the columns used for calculations, the data role, the output data type of the calculated column, the description, and its popularity.

Filters tab

Get insights on what filters are applied to the dataset. Review the columns used and filtering conditions in SQL.

Joins tab

For the datasets with more than one table, you can review all the joins used to connect the tables. If a join uses multiple columns, hover over the join icon to see the list of columns used for the join.

Lineage tab

The lineage graph reveals from which data connection each table comes. You can also view where data transformations took place. The calculation and filter icons are shown over the respective tables.

Add dataset description

Dataset description is an important type of metadata that can educate users about the business aspects of the dataset, its specifics on usage, and any details to evaluate the dataset for any analytical purpose.

Dataset owners and users with the Edit permission can now provide the dataset description under the More actions menu by selecting Modify > Add description or directly in the Explore dataset pane, on the Overview tab.

The description and dataset tags are displayed in the tooltip when hovering over a dataset tile or row.


Adding a description or additional explanation for some columns, especially calculated columns, may facilitate understanding of the data and its analysis. In this release, dataset modelers can assign descriptions to individual dataset columns on the Step 3 – Join and preview page when editing a dataset with more than one data source table.

To add a column description, next to the column name, point to More options, and then click Column info.

The description is displayed in a tooltip when hovering over the column name.

Moreover, the column description is displayed in a tooltip when using the dataset advanced options in the widget settings.


Manage columns within the preview table

In this release, users can manage columns right within the preview table on the last step (Step 3) of the dataset preparation process.

To access column options, hover over a column header and select the Column options menu.

Connect to a dataset in Power BI, Qlik, or Cognos

You can use DataClarity datasets in any BI tool by using the PostgreSQL connection. When creating this connection, you need to enter connection details to the dataset. For users’ convenience, in this release, all these connection details are displayed in one place so that users can quickly copy and paste them.

For a dataset that you need, point to More actions > Analyze with, and select the tool where you want to open the dataset. In the dialog that appears, hover over a field and click Copy that appears to the right.


Duplicate data connections

In this release, you can benefit from the new capability to duplicate data connections. No need to manually recreate connections, just click the Duplicate menu option under More options for any existing data connection to create its copy. Then, modify any details that you need. The duplicated data connection has the same structure and properties as the original one except for categorizing details like color, favorite, and tags.

For the shared data connection, the new option appears only if the data connection owner or manager granted the Duplicate permission. The new permission is now included in the Viewer role in the Data connection permissions dialog.

Duplicate AI connections

New capability to duplicate a connection allows you to quickly create a copy of any existing AI connection without manually entering all the details. To do so, select a connection, and click Duplicate under More options. Then, you can modify any details as needed. The duplicated AI connection has the same structure and properties as the original one except for categorizing details like color, favorite, and tags.

For the shared AI connection, the new option appears only if the AI connection owner or manager granted the Duplicate permission. The new permission is now included in the Viewer role in the AI connection permissions dialog.


Export and import storyboards

In this release, storyboard management capabilities include the options for storyboard export and import. This way, users can save time and reuse the storyboard in different environments.

To export a storyboard, in the Storyboards pane, select the storyboard, and under the More actions menu, click Export. The storyboard with all underlying datasets, data sources, connections, and AI connections will then be saved into a ZIP file.

To import a storyboard, you will need to upload a corresponding ZIP file. In the Storyboards pane, clicking the Import button will open the dialog for the file import. You can specify whether to overwrite the storyboard if the same element has already been imported previously. If the same storyboard already exists, then the storyboard will not be imported.

View column descriptions

If a dataset column or a calculation has a description assigned to it in Data Preparation, you can take advantage of it in Storyboards. In the widget settings, click advanced options, and in the Datasets pane that appears on the left, hover over the column name to view its description.

If the description is lengthy, go to More options > Column info and review the full description in the Column info dialog.

Visualize with minimized SQL

The visualization’s performance has been improved with the new option called Minimized SQL added on the data tab of the widget settings, under the Data view options section. Users can now choose how to generate the SQL query when visualizing the data in a widget.

When Minimized SQL is turned on, the system generates SQL with the minimal set of tables and joins needed to obtain values for the selected columns. The new option is set to ON by default, ensuring optimized queries and faster data analysis. If the data structure requires to use all the tables and respective joins, turn this option off.

Totals row in the Table widget

Users can now display the row with the total values in the Table widget. The Totals section has been added to the appearance tab of the widget settings pane. Users can define the label for the totals row and its appearance settings.

Refresh data in Table

The Refresh data option has been added under More options for the Table widget to quickly update the data if any changes occurred since the last data visualization.

User Access

Customize the look & feel of the platform

Administrators can now configure the logo and branding of the DataClarity platform in Access Manager, in the Settings pane, on the new Look & Feel tab.

To style the applications, you can customize for the following user interface elements:

  • Application/platform name
  • Application logo (upload an image)
  • Top toolbar appearance (background, text, and icon colors)
  • Sidebar appearance (background, text, and icon colors)


UX improvements

Previously, to get more storyboards, datasets, data connections, or AI connections, users needed to click the Show more button. The action is simplified―users just scroll down to automatically load the next set of items on the respective panes. Also, the system now loads 35 items instead of 20.


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