Exploring, Visualizing, and Sharing Data and Deploying Solutions

In this chapter, you will journey through the key skills needed for data analysis and sharing the results with your audience. We’ll start by exploring semantic models and using various techniques to reveal valuable insights. Then, we’ll learn how to visualize these insights with various functionality provided by Power BI, along with packaging these outputs for your audience. Next, we’ll focus on the various methods we can use to publish and share Power BI content so that it reaches end users effectively. Finally, we’ll conclude by covering deployment strategies, refreshing schedules, and configurations so that you can seamlessly integrate data for your solutions.

This chapter deals with the second part of data analysis. The first part, Chapter 5, focused on bringing data from various places, putting it together, and making it available for consumption through semantic models. This chapter...

Technical requirements

For this chapter, you will require the following:

• Power BI Desktop
• Power BI Service
• A Power BI Pro license
• A Power BI Premium Per User (PPU) license or Premium capacity
• Power BI gateway standard mode
• SharePoint Online
• SQL Server or Azure SQL Database

Exploring data

To make decisions on the direction of your business, you need to know where you stand. To do that, you will need answers to the questions you have. Sometimes, the answers that you receive prompt you to ask even further questions – questions that you didn’t have before but are suddenly important. That’s why exploring data is very effective when it comes to decision-making: you get to dynamically take your path to the insights. However, to make it an effective process, you need to have the data ready and shaped in the way you want. Having the data shaped in the way you want is what we looked at in Chapter 5, where we designed and built a semantic model. It provides multiple ways of looking at a certain piece of data. We will look at getting the data ready in the last section of this chapter. First, we’ll learn how to explore data using Power BI.

Exploring data with Power BI

Let’s start by exploring data ad hoc. This is because we...

Visualizing data with Power BI

In the previous section, we explored data using Power BI. Our exploration ended with the discovery of insights. These insights are helpful for decision-making. Hence, it is always a good idea to structure your analysis and its results for later use. For instance, if you were looking for some specific insights, or if you found some interesting insights after ad hoc analysis of a hunch you were following, there’s a good chance that you would want this information to be updated with the latest data each month.

Creating Power BI reports

When we explored data in the previous section, we inadvertently used the Report view. This is the same canvas that we would use to turn the analysis that we have into a report. As we discussed in the previous section, it is a good idea to have reports independent of their models.

Creating a report against a published model can be done by using Power BI Desktop or directly from Power BI. However, the Desktop...

Publishing and sharing data in Power BI

The Power BI content that you create must be published before it can be consumed by others. This content, such as the semantic model and the reports that you create on Power BI Desktop, need to be published to Power BI before they can be shared with others. Content that you create on Power BI, such as reports, dashboards, and scorecards, are published when you save them by default.

Note

Sharing Power BI Desktop (PBIX) files through regular file-sharing methods is not the intended way of sharing Power BI content.

Next, we’ll look at what needs to be in place before you can publish your report.

Understanding workspaces

Power BI workspaces are the first step toward publishing your content. All published content goes to live in a workspace. You can think of a workspace as a collection of content that shares a common theme, such as a specific requirement, a business process, or a team of individuals that share a common interest...

Deploying your solution

A Power BI solution, once built and published, should be able to continually refresh the latest data from its sources to its elements, such as reports and dashboards. The solution also should go through a standard development life cycle where the solution is tested and then deployed to a production environment that works without breaking. In this section, we’ll look into deploying a Power BI solution to various environments and ensure data connectivity.

Exploring semantic model settings

When you build your semantic model using Power BI Desktop, the connections that you create to the data sources are local to the computer that you do this on. The moment that you publish your semantic model to Power BI, that connection is no longer there. This is because the service, unlike your development computer, has not been provided with connectivity information. You will notice this when you try to refresh the dataset for new information.

Hence, once you...

Q&A

The second part of developing a Power BI solution is to explore and visualize data, after which you will share and deploy your data. Hence, it is good to look at some questions that you may potentially be asked during your interview.

Question 1: How is data consumed once you build a semantic model in Power BI?

Answer: Data can be consumed in many ways. The natural and most intuitive step would be to explore it using Power BI itself, though if users are not too familiar with it, they can analyze data with Excel using pivots. One method I usually use is to look at measures at a high level, such as for the current year and for the region I operate in. I use the Power BI canvas and use the appropriate visuals to draw the picture. I look for whether the measures have met their targets or are on their way to meeting them. If I notice that this is not the case, I dive deeper to see where things may have gone wrong by adding further dimensions, or attribute levels of dimensions...

Summary

In this chapter, you learned how to consume the content that you created in the previous chapter. This involved exploring the content in different ways to arrive at insights using methods such as management by exception and Pareto analysis. We learned that different users can use different methods and even different tools, such as Microsoft Excel, to explore the data on your semantic model. Exploration gives way to visualizing when you start crafting a story using the insights and analysis you came up with. To visualize data, you must create Power BI reports to visually represent the analysis compellingly. We also learned of other means of visualizing data, including using dashboards, paginated reports, and scorecards. Next, we learned how the content that we’ve created can be packaged, published, and shared with other users. We learned how users can interact with these solutions to gain deeper insights. Finally, we learned about the importance of deploying a Power BI...