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Transforming Data in the Cloud : Deciphering differences between Reports and Dashboards

As the horizon of the data expands, the need for cloud base analytics is becomes a compelling necessity, for the companies to save unnecessary overhead costs and logistics associated with it.

Today, we will talk about end to end dashboard creation in Power BI service, highlighting the key differences between a Dashboard and a Report in Power BI cloud environment.

Creating a dashboard requires getting the data, identifying key metrics, data modeling, creating reports and reflecting necessary information in the dashboard. We will perform all of these steps in the cloud! An important point worth mentioning here is Power BI service has limited options for data modeling and there is no Power Query Editor in the service. This platform is mainly used for creating dashboards which are linked to the underlying reports, collaborating in workspaces, creating and sharing apps and dashboards, implementing row-level security, etc. All of these feature depend on different licensing options.

  1. Getting the Data, after requirement gathering

Power BI can connect to various data sources such as Data marts, Databases, flat files, SharePoint online, SQL server and many more, but data sources are limited as compared to Desktop. The semantic model of the report which we publish from the desktop is also published along with the report in Power BI service and stored in One Lake Data hub. We can create our own dataset in the cloud, paste the data or pick an already published semantic model. There is also an option for picking a dataset which has been published by other people in the organization and you have the right to use that dataset.

In this blog, I will be using an already published dataset and create another report on top of that dataset.

I will be using the first one for demonstration purpose, the COVID dataset. It will give two options, first is, Auto-create report, second is, create a blank report. When Auto- create report is selected, Power BI uses Artificial Intelligence to create a report for the user, which can later on be pinned to the dashboard.

When create a blank report is selected by the user, Power BI opens up a Desktop view, where we can create our own Report.

Let's see both in action.

Auto-create report:

Power BI will give quick summary of the data analysis.

The report auto created by Power BI is:

This report may or may not answer the question that you want. It gives a basic idea about the analysis of your dataset. However, to answer the specific questions, according to client's needs, you have to select the option of creating a blank report.

Create a blank report:

It will open up Desktop view of Power BI in the cloud, where we can create our own visuals and pin the necessary ones in the dashboard.

2. Data Transformation: Transforming data option is not available in power BI service, as of now and creating calculated columns, measures and calculated tables, is a part of Preview feature as of now. The dataset which we want to work on should be cleaned in the Desktop app and then brought here for team collaboration.

3. Data Modeling: Editing the data model is a preview feature as of now. Data Modeling also has limited options in service as compared to Desktop. However, in preview feature, users can edit the model, and for doing so, they have to enable the preview feature in Power BI service. For semantic models, the preview feature is automatically kept as on in the workspace.

4. Selection of visuals and creating reports

I will create a small report and show how we can link visuals to the dashboard.

Below is the report created in Power BI service.

When you will save the report, it will ask to select the workspace where you want to save the report.

Now the report is created in Power BI, we will see how we can create a dashboard from this report.

5. Pin the visuals to the dashboard

When we create a report in Power BI service, a small pin icon is attached to every visual and is seen when we hover over the visual. We can pin the visual to the dashboard. Here we can specify if we want to pin to the existing dashboard or new dashboard.

Below is the dashboard view, with the visuals pinned from the report.

We can also save a copy of the dashboard in our local system.

When we click on the visuals in the dashboard, it directs us to the main report page for drilling through the insights.

6. Sharing the dashboards:

We can now share the dashboards with the end users by providing the email addresses. To access the dashboard, recipients should have at least Pro licensing of Power BI.

In conclusion, understanding the differences between a dashboard and a report is of crucial importance to take full advantage of this tool. Dashboard highlights only the important aspects of a report, whereas for drilling down, we have to look at the linked reports.

Thanks for reading !

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