Reporting Tools Summary

Once an analytics project is up and running, there is often a strong need to have reporting tools that give project collaborators, as well as external stakeholders, insight into the ongoing performance or results of the project.

DSS has a number of tools that serve this purpose.

This lesson provides an overview of:

• Dashboards in DSS
• R Markdown reports
• Visualization plugins

Dashboards in DSS

Dashboards allow users to publish elements of a DSS project (such as a table, chart, or model report) to a dashboard, and then share these dashboards not only with project collaborators, but a wider group of stakeholders, while still maintaining control over permissions.

Every DSS project has a default dashboard, but an arbitrary number of new dashboards can be created.

• A dashboard is made up of slides.
• Each slide is made up of tiles, which the user can arrange on a grid as needed.
• Each tile on a dashboard slide holds an insight from the project.

Many types of insights, such as charts, model reports, Jupyter notebooks, web apps, metrics, and even macros, can be added to dashboards.

Insights generally reference DSS objects. This chart, for example, can be traced back to the source dataset in the Flow.

Dashboard Permissions and Authorizations

In many cases, you will want to have fine control over how a dashboard is created and shared with fellow collaborators, colleagues from separate teams, and other stakeholders. DSS employs a groups-based permissions model equipped for this challenge.

The basic principle is that admins assign users to groups. Project creators share projects with groups and designate specific permissions of that group with respect to the project at hand.

The reference documentation provides a full accounting of all possible permissions, but here we can illustrate a few examples of how this framework can be put to work as it relates to dashboards.

Project builders

As shown below, fellow project collaborators may be members in a group, such as data_team, and have a wide range of permissions including the ability to “Write project content”. This implies the permissions to read, write, and moderate dashboards.

Dashboard builders

Also shown above, another group of users, perhaps called the viz_team, may be responsible for creating dashboards once the data team is finished building the project Flow.

It might be reasonable to grant users in this group the ability to moderate dashboards, which implies the permission to read and write dashboards, and the permissions to manage dashboard permissions and dashboard users. They may also require the ability to read, but not write project content.

With the set of permissions above, users in the viz_team group can create new dashboards or they can add new slides and tiles to an existing dashboard from DSS objects.

When adding new insights, DSS issues warnings when new objects are added to the dashboard that have not previously been shared with dashboard-only users.

When the dashboard is ready to be discovered by more users, the project owner, an admin, or a user with the “Moderate dashboards” permission can switch a dashboard from the default private to a public setting. This allows those with the correct permissions to find the dashboard on their DSS homepages.

Dashboard readers

Finally, you may also have users who should only be able to access a completed dashboard, but not modify its contents in any way.

Although an admin could assign this restricted level of permission to any group, a user with the permission to “Moderate dashboards” can also grant dashboard access to specific users on the instance.

A dashboard-only user cannot create a new project. They cannot see the project Flow. They can view but not edit, copy, or delete the dashboard. They cannot inspect the source objects behind any insight.

Exporting Dashboards

This gives an introduction to the many ways in which you can share dashboard access with DSS users, but often you may need to share a dashboard, or pieces within it, externally, outside of DSS.

For this, you can export a dashboard as a PDF or PNG file. Not only can you do this manually through the interface, but also automatically through a scenario so that exports arrive by mail or are stored in a managed folder.

R Markdown Reports

R Markdown reports are another useful reporting tool integrated into DSS.

For those comfortable coding in R, R Markdown documents allow you to weave together narrative text and code to produce reproducible and elegantly formatted output, such as reports or presentations.

Similar to the interface for building web apps in DSS, you will find a tab to interactively edit the source code of the report, alongside its output.

The integration with DSS enables you to publish these documents on project dashboards, share them with other DSS projects, or download them in a wide variety of formats, such as HTML, PDF, or Microsoft Word.

As is the case for dashboards, you can also automate the download of R Markdown reports using a scenario.

Visualization Plugins

In some cases, the output of a project is not a dashboard or a report, but a dataset itself. In addition to being able to download a DSS dataset as a CSV file (and other formats) or export it to another DSS project, you can also find plugins that make it easy to share datasets with external software tools.

For example, DSS has plugins that make it easy to export DSS datasets to visualization and business intelligence platforms like Qlik, PowerBI, and Tableau.

Here, with the Microsoft Power BI plugin, we see the option to export a DSS dataset to Power BI using credentials or an access token.