Introduction to Power BI
Power BI is an industry-leading Microsoft BI tool that essentially functions to provide insights into your data, insights that can then be used to shape your business decision-making. If you are taking this course, you are likely new to the wonders of Power BI, and are here to learn how to get the most out of your data through the use of this popular application.
Many new users of Power BI are familiar with the practice of organizing and analyzing data using Excel spreadsheets. Those users may ask, what does Power BI do that Excel cannot? Well, you're likely already aware that the BI in Power BI stands for "Business Intelligence." In the very simplest of terms, Power BI takes data you provide, analyzes it, and organizes it into sharable, visual reports and models that track metric trends, relationships, potential outcomes, and much more. It is also designed to be integrated seamlessly into other Microsoft products such as Power Apps and SharePoint.
Who uses Power BI? Anyone who wants to! At one point in time, BI tools were used only by specific professionals. That has changed thanks to widely available, user-friendly data applications like Power BI. Now individuals and companies of all backgrounds can use Power BI to gauge the effectiveness of their business decisions, explore "what-if" scenarios, and even build customizable visuals based on questions they themselves build into the AI.
Before we jump in, let's discuss what you can expect over the next few weeks. First, be prepared to work primarily in Power BI desktop. Power BI is a unique product in that some users prefer to operate mainly in Power BI Service, while others spend more time building reports in Power BI Desktop. The truth is, neither of these approaches are wrong. It comes down to preference and purpose. Our goal is to create the most accurate reports possible, based on your data. And while it's true many functions can be performed in Power BI Service, Power BI desktop is where the most powerful data analyzing, data transformation, data modeling, and report visualizations features can be accessed.
Understand Power BI
At first glance, Power BI can appear, well, intimidating to anyone not familiar with BI visual data. But the basic components of Power BI are really very simple!
The first thing to understand about Power BI is that there are two main platforms: the Power BI desktop application, and the Power BI Service. (A mobile platform also exists, but for the purposes of this course we will be focusing solely on Power BI Desktop and Power BI Service.) The Power BI Desktop application is where it all begins. Reports are created by you in the Desktop application; from there they are published to the Power BI Service.
In the Power BI service, you can view workspaces, create dashboards and apps, and - most importantly - share your data with others.?
If the full capabilities of Power BI are are overwhelming, keep in mind: you can use Power BI to the capacity that fits your role, needs, and workplace. Not every user will need or want to take advantage of the full spectrum of its capabilities.On that note, the type of Power BI license you have will determine which features are available to you. The three types of licenses are: Power BI Free, Power BI Pro, and Power BI Premium Per User (PPU).
Understand Power BI Licenses
The kind of Power BI license you have will determine capabilities and features you will have access to. Take a minute to review the information below to learn about these tiers and their capacities or limitations:
Power BI License Types and Capabilities
Free
Power BI Pro
Power BI Premium Per User (PPU)
How to Check Your Power BI License
Import Data or Connect to a Data Source
Okay. You know the difference between Power BI desktop and Power BI Service now, and you are aware of which license you have. Now let's get started on the first step towards building your report: importing data. This is the obvious first task when operating within Power BI, because without a dataset, there can be no report!While you can import data directly into the Power BI Service, the options for importing methods are more limited than the options in Power BI desktop. You also cannot fully format your report canvas in Power BI Service.When working inside of the Power BI desktop application, you can import data from a variety of different locations. You can connect to a data source by using the Get Data feature on the home tab. Or you can import from locations such as Excel, a web page, Power BI datasets, and more.Be advised, when you connect to a Power BI dataset, you are using data that has been previously uploaded to the Power BI service. When this option is selected, the Data view icon disappears. When that happens, don't be alarmed! The icon has only disappeared because you are using the cloud instead of local data.
Understand the Data View
Now that you understand how to get data or import data into your Power BI desktop application, let's talk about the different ways you can view that data!
The Power BI desktop application gives you full access to all features for creating reports. You can transform, model, and visualize your data in one application.??
When you first open the Power BI Desktop application, you will see three icons on the left-hand side. There’s Report, Data, and Model. Although these icons are ordered one way in the application, we'll explore each view in the order that you will use when working in Power BI, which is data, model, and then report.
Overview of the Data View in Power BI. The Data View in Power BI Desktop allows you to view and edit your data after it has been loaded into the application. This view can help you make informed decisions about your report by allowing you to review the data down to the row level and examine it from different angles. Since the Data View shows data after it has been loaded, note the data icon will not be visible if all data sources are based on direct query. Use the fields pane to select or search for a table you want to view. The data grid displays all columns and rows from the table. You can right-click a column for options such as sorting.
To filter a column, select the Filter icon. Once you have reviewed your data, you can perform certain actions to add or edit data directly from the Data View. From the Home tab, you can select new data sources to connect, to navigate to the Power Query Editor to transform the data and refresh the visuals in the report with the latest data from the original data source. From the Table tools tab, you can edit the table and manage relationships. In the calculations group, you can create new measures, columns, and tables with DAX expressions or formulas. In the formula bar enter DAX expressions for measures and more.
The new measures, tables and calculated columns you create will appear in the fields pane. From the column Tools tab, you can edit a columns name and how it is formatted. In the properties group, you can set the summarization and data category options for a particular column which will impact how the data is displayed in all visualizations in the report. For instance, you may want to display the count of a column or categorize a column as a city, so that Power BI treats it accordingly. From the sort by column drop-down, you can choose a column to define the sort order of another column in the reports visualizations. Additionally, you can combine multiple values into one by creating new data groups. Use the Data View to gain further insights into your data and make well-informed decisions about your report
Clean and Transform Data
You have learned how to import data into Power BI, and how to view it in the data view. Now let's discuss how to prepare, clean, and transform your data in order to create accurate models and reports.
The quote above, taken from well-known "Data Doc" Thomas Redman, gets to the heart of an absolute truth concerning data-driven business decisions: those decisions - and ultimately their outcomes - will only be as sound as the data they're based on.
When importing data into Power BI, seemingly minor errors can mean the difference between accurate reports leading to success, and flawed reports leading to failures. Even something as simple as random misspellings - San Diego vs San Diago - can cause the app not to detect crucial relationships that could have significantly affected your reports and the directions they lead in.
This is why it's imperative to clean, shape, and transform your data before moving on to the modeling and report visualizations phases of the Power BI process.Microsoft's engine for data preparation and transformation is called Power Query.
Power Query extracts your data and performs the importation from the data source, and then Power Query Editor applies necessary transformations. Power Query is a fantastic tool for these purposes, because it can not only connect to, and clean, a wide variety of data types, it can also store reshaped data in many locations. Power Query Editor has an additional benefit of "remembering" data transformations from the original extraction of your data, and applying the same transformation process whenever you refresh or update your data set. (This is called a query.)
On that note - when you are working with Power Query Editor, it's important to know that you are never truly changing your source data. Instead, you are simply changing how the data is displayed. In an upcoming video, you will learn about the Applied Steps List, a useful tool that tracks all of the actions and changes you perform. With the Applied Steps List, you can select a step and view how the data is displayed up until that point, with queries working actively in the background to shape what you see. You can easily delete a step when needed or view your source data at any time.
Keep in mind, even with Power Query Editor hard at work, no BI engine is perfect. Some errors may escape its notice. Best practice for making sure your data is flawless is to do a visual overview, and make any adjustments as necessary. Things like any blank columns, misspellings, or incorrect math, can all be easily manually adjusted in data view!
Now that we've reviewed the importance of data transformation, watch the next few videos to learn more about Power Query, the Applied Steps List, and ways you can manually clean up your data.
Access the Power Query Editor
Access the Power Query Editor. The Power Query Editor allows you to transform, edit, and select data before loading it into Power BI. Select the Home tab. In the queries group, click the, "Transform Data drop-down". Select, "Transform data". In the queries pane, select the query that you want to edit. The Power Query Editor is now open and ready for further use.
Overview of the Applied Steps List in Power Query Editor
Overview of the applied steps list in power query editor. When you're working in the power query editor, you may notice that every transformation you apply to your data is recorded in the applied steps list. The applied steps list functionality is valuable because it lets you see how the data was shaped and cleaned. Moreover, your source data is never truly changed, it is just displayed according to the steps you add.
You can easily switch between your source data and steps by clicking source or the step that you want to see.When you select a step to view,you will see the data transformations up until that step and nothing after.For example if you're viewing the upper cased text step,you will see all steps up until that step but not those that come afterwards like merged columns.You can easily delete a step by right clicking on a step and selecting delete or by clicking the delete icons next to the step.You can also create a new query based on the applied steps by selecting extract previous.
When you use this option, the new query will have all steps that occurred before the step you selected. The new query will appear in the queries pane. Notice how the new query contains all of the steps that were applied before the selected step and how the original query contains just the selected step. Think of the extract previous feature as if you're splitting the applied steps list into two queries. You can also reorder steps as needed by dragging and dropping them to a new position. Know that reordering steps may cause issues if one is dependent on another. You can also rename a step by selecting rename from the shortcut menu or select properties to change the name of the step and enter a description. Descriptions can help you a colleague or someone who will work with the back end code of your query to better understand the purpose of the step. You can view and edit the code for your steps by clicking Advanced Editor on the home tab. The code displayed in the Advanced Editor and working behind the scenes in Power query editor is a scripting language called m. If you are an advanced user, you can use the Advanced editor and the m language to edit the code to get the results, you need. Any step descriptions that were entered will also display in this area. The applied steps list will always record transformations regardless if the query settings pane is closed or open. To reopen the query settings pane and view the applied steps list, select the view tab and click query settings.
To summarize, Power query editor will always record transformations as steps in the applied steps list. Your source data will not be altered and can always be revisited when needed. The applied steps list allows you to go back and see how your query was transformed so that you can backtrack easily and view how the query displays at different points
Execute Basic Math Operations to a Column
You can easily switch between your source data and steps by clicking source or the step that you want to see. When you select a step to view, you will see the data transformations up until that step and nothing after. For example if you're viewing the upper cased text step, you will see all steps up until that step but not those that come afterwards like merged columns. You can easily delete a step by right clicking on a step and selecting delete or by clicking the delete icons next to the step. You can also create a new query based on the applied steps by selecting extract previous. When you use this option, the new query will have all steps that occurred before the step you selected. The new query will appear in the queries pane. Notice how the new query contains all of the steps that were applied before the selected step and how the original query contains just the selected step. Think of the extract pr: Added to Selection. Press [CTRL + S] to save as a note
Execute basic math operations to a column. The standard feature allows you to add, subtract, multiply, or divide all column values by a specified number, and more. In the queries pane, select the query that you want to use. Select the column that you want to use. Click the Transform tab. In the number column group, select the standard drop-down. Select the calculation that you want performed on all values in the column. The steps will differ depending on the option you select. In the value field, enter the additional value needed for the calculation. Click Okay. The new values are displayed in the column.
Round Values in a Column
Round values in a column. You can quickly round all values in a column up, down, or to a specified number of decimal places by using the rounding feature. In the queries pane, select the table that you want to use. Select the column that you want to round, click the "Transform" tab. In the number column group, select the Rounding drop-down. Select the rounding option that you want to apply. Round column values efficiently by using the rounding feature.
Replace Errors in a Column
Replace errors in a column. You can replace errors in a table by inputting the value you want to be shown instead. This doesn't fix the source of the error, but it will fix how the values are displayed. In the Queries pane, select the table that contains errors. Select the column with the error value displayed. Click the "Transform" tab. In the Any Column group, select the Replace Values, drop-down. Select Replace Errors. In the Value field, enter the value that you want to replace the error. When finished, click "Okay". The column errors are now replaced with the entered value name.
Introduction to Data Modeling, Report View, and Visualizations
Believe it or not, if you were amused at all by the comic above then you're already in the right frame of mind to understand data modeling! Data modeling is the visual imaging of relationships and trends in your data. By now you know the entire purpose of Power BI is to turn data into decisions, and data modeling is a crucial component to this. In order for data modeling to be useful, it must tell a story - a story of relationships, of performance, of baseline measures versus goals, etc. - the kind of story and its relevancy will vary, and is widely dependent on objectives. The point is, your data visuals must help you see how your business is performing, and where the opportunities exist to do better.
You've learned about Power Query, Power BI's built-in data transformation tool. Now let's discuss Power Pivot and Power View, and their relationship with data modeling and reporting in Power BI.
Power Pivot is Power BI's built-in data modeling tool. It seizes on your data's most noteworthy relationships and calculations, and translates them into card visualizations using DAX (Data Analysis Expression) language. You'll interact the most with Power Pivot while in the Model views, which we will discuss more in the next reading.
Once Power Pivot has turned your data's relationships and calculations into models, it "passes the baton," so to speak, to a technology called Power View. Power View takes data models and turns them into the powerful forms of visualizations that truly bring data to life. Sophisticated, real-time digital charts, graphs, even 3-D maps, can all be used to tell your data's story. You'll interact the most with Power View while in the Report view and in Power BI Service dashboards.
Understand the Model View
o help us understand the model view, let's take a look at a real world scenario:
Kate runs a regional sales team for Toys 4 Kids, Inc. In the recent fiscal quarter, there was an upward trend in sales of toys in the 0-3 years-old age group in her region. Sensing an opportunity, Kate decides to run the data through Power BI to find relationships that may be contributing to the ascending sales numbers. During her review, Kate runs queries in the Model View and discovers connections exist between specific postal codes and buyer age demographics.
Her team then takes that information and further discovers that the postal codes in question comprise an expanding sector of a major city that, due to job growth, is attracting young families in significant numbers. Kate and her team decide to take advantage of this information by targeting the growing region and its demographic of young family buyers.
As a result, their overall sales revenue increases steadily over the next quarters!
This is just one example of how modeling relationships in Power BI can, and should, have significant impact on data-driven decision-making. In any data sets, relationships always exist. Power Pivot can help you extract the right relationships to base professional goals on.
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How does Power Pivot accomplish this? By organizing your data into individual cards, or tables, and highlighting relationships that exist between them in the Model view. From there, you can easily run queries based on overlapping criteria, and come away with accurate, insightful reports!
Overview of the Model View in Power BI
Overview of the model view in Power BI. The model view in Power BI shows all of the tables, columns, and relationships in your model. Access the model view by clicking the Model icon in the left navigation pane. This view helps you understand complex data models with ease because it visually displays connections between tables with relationship lines. Power BI attempts to automatically detect relationships based on column names when you query multiple tables at once. But it's important to understand how the relationships were formed and create new ones as needed. When you work with data from multiple tables in the same visualization, relationships must be established between the tables using a common column, otherwise known as the joining field so that the data displays correctly. For example, the BU column in the BU table and the BU key column in the fact table match. They are the joining fields in the relationship. Since Power BI knows they are related, you can create visualizations with both tables as if they were one. On this page, we have a table that includes a column from the fact table and BU table displaying the correct COGS amount for each business unit. If the relationship was not established or deleted, Power BI would not recognize that the two tables were related, which would cause the data to display incorrectly.
Notice how now the relationship is deleted. Only the total value displays in every row of the table. While Power BI automatically detects relationships between tables by default, there may be times you need to create or edit an existing relationship. For this reason, report designers should use the model view to understand how the relationships between tables in the model are configured and adjust them as necessary. You can double-click a relationship line to view more details about it and edit it. In this example, the customer and state tables are related because the state and state code columns both contain state abbreviations. In the cardinality drop-down, select if the joining fields have unique or multiple instances per value. This relationship's cardinality is many-to-one, meaning the state column has many instances of a state abbreviation, while the state code column has only one instance of each state abbreviation. The cross filter direction refers to the direction the filter propagates.
In this case, it is set to single, which means filtering choices will work only on the table where values are being aggregated. When the cross filter direction is set to both, the filter can work on both sides of the relationship. Bidirectional relationships can cause performance issues, so they should only be used when you need to perform calculations on both sides of the relationship. You can create new relationships by clicking "Manage Relationships". From the Manage Relationships window, you can trigger Power BI to auto-detect new relationships, navigate to the Edit Relationships window, or delete relationships. Additionally, you can make a relationship active or inactive. The model view also allows you to edit the properties of a table or data field.
Adding field properties such as synonyms and descriptions in the Properties pane helps Power BI accurately generate Q&A visualizations. While editing a data field in the Properties pane, you can adjust how it appears throughout all of the pages and visuals in the report, including how the field is sorted, categorized, and summarized. Use the model view to see and edit the relationships between tables to ensure the data in your report is displayed correctly. Remember that relationships must exist between two tables to work with them in a visualization.
View, Add and Edit Relationships in Model View
As we've established, once your data is loaded, cleaned, and transformed, you can switch to the Model view to identify and edit relationships between the tables in your data model. This step is especially important if you're working with large data sets or multiple queries. Sometimes these relationships are automatically detected thanks to queries, while sometimes they will have to be added, edited, or even deleted.
In the coming sets of videos, you will learn how to work more with relationships in data modeling so you can create insightful data visualizations in the report phase.
View Relationships in the Model View
View relationships in the model view. The model view displays all of the tables, columns, and relationships in your model. In the left navigation pane, click the, model icon. Table cards are displayed illustrating the relationship between each table in the model. Related tables are connected by relationship lines. To view all columns in a table click, ''Expand.'' To enhance the view select the zoom in icon. To decrease the view, click the, ''Zoom Out'' icon to fit the whole relationship on your screen select the, ''Fit to Screen'' icon. All table cards are fully displayed in the model. To show the columns used in a relationship hover over our relationship. The related column is highlighted. You can interpret a relationships cross filter direction by the arrowheads along the relationship line. A single arrowhead represents a single direction filter in the direction of the arrow head.
A double arrowhead represents a bi-directional relationship. In model view, you can also interpret a relationships cardinality type by looking at the indicators on either side of the relationship line. The one indicates the one side of the relationship, while the asterisk indicates the many side of the relationship. For instance, the relationship between the fact and scenario tables is many-to-one. To view more information and edit the relationship, double-click the, relationship line. In the edit relationship window, the column information is displayed under each table and the related columns are highlighted. You can also view and edit the cardinality and cross filter direction of the relationship here. To exit the edit relationship window, click the, ''Close icon. '' View and interpret relationships in the model view to gain further insights into your data.
View relationships in the model view. The model view displays all of the tables, columns, and relationships in your model. In the left navigation pane, click the, model icon. Table cards are displayed illustrating the relationship between each table in the model. Related tables are connected by relationship lines. To view all columns in a table click, ''Expand.'' To enhance the view select the zoom in icon. : Added to Selection. Press [CTRL + S] to save as a note.
Edit or Remove Relationships
Edit or remove relationships. Power BI can automatically detect relationships when first importing data, but sometimes relationships between tables need to be edited. To view the relationships, click the Model icon. In the Relationships group, select "Manage relationships". From the Manage Relationships list, select the appropriate relationship. To edit the relationship, click "Edit". From the table drop-downs, select the appropriate tables. In the column areas, click the corresponding columns. To set the cardinality, select the Cardinality drop-down. Select the appropriate option. From the Cross filter direction drop-down, select the appropriate setting.
To make the relationship active, click the ''Make this relationship active'' checkbox. To assume referential integrity, select the "Assume referential integrity" check box. This selection enables queries on the data sources to use inner join statements rather than outer join, which improves efficiency. To apply a security filter in both directions, select the "Apply security filter in both directions" checkbox. Note this option only applies if the cross filter is bidirectional. To apply the edits, click ''Okay''. To delete the relationship, click ''Delete''.
When finished, click ''Close''.
Edit relationships to determine how your data is displayed and interacts with each other.
Add a New Relationship Between Tables and Columns
Add a new relationship between tables and columns. Power bi I relationships propagate filters applied on the columns of model tables to other model tables. Filters will propagate so long as there is a relationship path to follow. To add a relationship, click the model icon. In the relationships group, click Manage Relationships. Click New. From the select tables and columns that are related drop downs, select the tables that are related. In the columns area, select the related columns.
To set the cardinality, select the cardinality drop down. Relationship, cardinality determines whether the relationship will be many to 1 1 to 1 1 to many or many to many. Click the appropriate option. From the cross filter direction drop down, select a default direction. To treat both tables as a single table for filtering purposes, select both. To set the filter direction on the one side of the relationship, select Single for 1-1 relationships. The cross filter direction is always from both tables. To have the relationships serve as the active default relationship, click the make this relationship active checkbox.
To enable running more efficient queries against the data source. When using direct query, select the assume referential integrity check box. To apply a security feature in both directions, select the apply security filter in both directions checkbox.
When finished, click OK. The new relationship appears in the managed relationships list. To activate the new relationship, select the Active Checkbox. To exit the managed relationships window, click Close. Once you have created a relationship between two tables, you can begin working with them as if they are a single table Add a new relationship between tables and columns. Power bi I relationships propagate filters applied on the columns of model tables to other model tables. Filters will propagate so long as there is a relationship path to follow. To add a relationship, click the model icon. In the relationships group, click Manage Relationships. Click New.
From the select tables and columns that are related dropdowns, select the tables that are related. In the columns area, select the related columns. To set the cardinality, select the cardinality drop down. Relationship, cardinality determines whether the relationship will be many to 1 1 to 1 1 to many or many to many. Click the appropriate option. From the cross filter direction drop down, select a default direction. To treat both tables as a single table for filtering purposes, select both. To set the filter direction on the one side of the relationship, select Single for 1-1 relationships. The cross filter direction is always from both tables. To have the relationships serve as the active default relationship, click the make this relationship active checkbox. To enable running more efficient queries against the data source. When using direct query, select the assume referential integrity check box. To apply a security feature in both directions, select the apply security filter in both directions checkbox. When finished, click OK. : Added to Selection. Press [CTRL + S] to save as a note.
Understand the Report View
So what are visualizations? There's no trickery involved in the name here! Visualizations, also called visuals, are exactly what they sound like: they are the visual representations of your data. They include every kind of graph, chart, and treemap you can imagine. Furthermore, they are customizable, making these graphics one of the most impactful features Power BI has to offer, because they help you and your audience interpret and discover new insights about the data set.
There are 4 main types of visualizations in the Report View:
Now how exactly do visualizations work? That depends on your selections. For example, say you want to display your data in an area chart like the one pictured here:
The area chart is a chart visualization. A chart visualization is best used when measuring two or more values against one another, since a prominent feature of most chart visualizations is an X-axis and Y-axis. In the example above, the Legend, or the categories each individual data set belongs to, are the names of the team members. The Y-Axis is the month and the X-Axis is sales revenue percentages. Put all together, this area chart is measuring the total monthly sales revenues of each team member. Given the nature of the chart and the items involved, there will be different visual options that can be manipulated. With this area chart, settings can be customized for visuals such as gridlines, markers, and data labels; but Small multiple title and Small multiple grid visuals are grayed out; meaning, they are not options for customizing your visualization.
Overview of the Report View in Power BI
Overview of the report view in Power BI. In Power BI Desktop, the report view is where you create dynamic and informative report pages with visualizations. The report view is the default view when you first open Power BI Desktop. But it can also be accessed by clicking the report icon as you navigate between views. From the Data group in the Home tab, you can connect to many different data sources to build your report. After you import data or connect to a data source, you can work with all of the data fields and tables from the fields pane. You can create new measures, groups, filters, and more with the selected data in the fields pane.
In the visualizations pane, you can select visualizations and customize them to tell a compelling story with your data. Take some time to familiarize yourself with the available visualizations as each visualization type is best suited for different contexts. Select or drag data fields from the fields pane to populate the visualizations. Review the formatting options that are available to customize the visual you selected. To add reference lines and focus on important trends or insights in your visual, click the analytics icon. Not all visualization types will support every analytics feature. But the options can include reference lines for goals, averages, means, and more, depending on the visualization being used. From the filters pane, you can apply filters to specific visuals, the current report page, or all pages within the report. Applying filters allows you to focus on specific categories in your data at the visual level or across many visualizations to get a full picture.
Note that the filters you apply as a report designer will remain when you close the report, and they will become the default filter state for readers once a report is saved. From the view tab, you can change the look and feel of a Report Canvas. You can select the theme to update the colors and formatting of the visualizations in your report. You can also select the page view, report page options, and the pains that are visible while editing the report. Once you have finished creating your report, you can publish it to the Power BI service by clicking Publish on the Home tab. Use the report view to create informative and engaging reports that tell a story with your data.
Adding Visualizations to Report Canvases
We've established that the visualization you choose will depend on the story you want to tell with your data. Now let's cover how to add a visualization to your report canvas.
Once you have selected a visualization type from the Visualization pane, you can customize it with various formatting options and filters. Selecting data to display in a visualization is as simple as selecting the checkbox for the relevant data fields in the Fields pane, or just dragging the data to the appropriate fields in the Visualizations pane (you will see this demonstrated in the next video).?
Add a Visualization
Add a visualization. Visualizations and Power BI are visual representations of your data such as charts, maps, and gauges. To add a visualization, expand the fields pane. In the fields pane, select a data field to use for the visualization. Alternatively, you can select the type of visualization to use first. In the visualizations pane, select a visualizations type. In the fields pane, select the appropriate data field. To move the visualization, click and drag it to the appropriate area. Discover new insights into your data with visualizations and Power BI.
Overview of Format Features for Visualizations
Overview of format features for visualizations. While editing your report, there are numerous formatting features that vary by visualization type. Select the visualization that you want to edit. In the visualizations pane, click the format icon. The format options available for the selected visual appear.
Note when a visualization is not selected, the format pane will display formatting options for the entire report page instead. Certain features can be enabled or disabled, when you set the appropriate toggle button to on or off. Once enabled, you can expand the section to view and select the formatting options you want to apply. You can also search for formatting options in the search field.
For instance, if you want to edit the colors of all the elements in the visualization, you can search color instead of expanding each section. Depending on the visualization type, you can edit the colors of the legend, axes, rose, lines, data colors, and labels, shapes, and more. To create a custom color, select more colors. If you do not like the colors or formatting options, you've applied in a specific section, click revert to default.
Take some time to explore the available options for each visualization in your report. While charts have sections to edit the axis and line styles. A map visualization, will have sections for the map controls and style. Once familiar with the available formatting options, you can create beautiful and engaging visualizations to tell a story with your data
Overview of format features for visualizations. While editing your report, there are numerous formatting features that vary by visualization type. Select the visualization that you want to edit. In the visualizations pane, click the format icon. The format options available for the selected visual appear. Note when a visualization is not selected, the format pane will display formatting options for the entire report page instead. Certain features can be enabled or disabled, when you set the appropriate toggle button to on or off. Once enabled, you can expand the section to view and select the formatting options you want to apply. You can : Added to Selection. Press [CTRL + S] to save as a note.
Change the Visualization Type
Change the visualization type.
Some visualization types are not visually compatible with presenting certain sets of data. In Power BI I desktop, It's easy to switch the visualization type to find the right fit visual to represent your data. In the report view, click the visualization to change. In the visualizations Pane, select the appropriate visualization type. The new visualization type is applied.
Understand Text Visualization Types
Text visualizations put greater emphasis on the plain text in your data sets. Oftentimes, text visualizations display that data using a combination of text and numerical or chart formats. If you are wondering if any text visualizations ought to be present on your report canvas, ask yourself: What do I want my audience to know? Do you want to sum up certain data points for them using text? Perhaps you want to highlight data points like relationships using a context line of some kind? (Example: "When [competing product] was introduced, sales decreased by 15%-30% across regional store locations.") If the answer to either of those question is yes for any key fields, then representing the fields in question with a text visualization is likely to be appropriate for your report.
Card visualizations
One commonly used, easy-to-format text visualization are the card visualizations. These visuals display data points, one row at a time. There are two kinds: single number, and multi row. A single number card is most likely to be used if there is one very important data point to be highlighted, such as total annual sales. A multi row card is used when there is more than one data point to highlight, such as annual sales by age demographics.
Table visualizations
A table should be very familiar to most report designers. It's a grid of rows, columns, and headers which contain and organize related data. They work best in instances where values grouped into various categories need to be compared quantitatively.
KPI visualizations
Another commonly used text visualization is the KPI chart. Depending on your level of experience with business data analytics, you may already know the essential term key performance indicator. To put it simply, a key performance indicator (KPI) is any measurable value that can tell you how your data's actual performance measures up against the goals you have set for your business. The KPI chart visualization is the visual representation of where your data falls within the parameters of your goals.
In order to create a KPI visualization, you must have a base measure value, a target measure value, and a goal (also called a threshold).
For example, let's consider Kate from the previous module. You may remember that she met her goal to increase the overall sales of "Toys 4 Kids" products in her region by targeting a promising demographic of buyers. To make her goal measurable, let's say her team's total contributions to the company's overall revenue for the fiscal year came in at $1.13M, while her target had been a 21% increase in regional sales from a base of $747.1K.
To create a KPI based on her values, Kate enters them into the Field pane on the Report view (you will see this action performed later in a video). She then converts the values into a KPI visual by selecting the KPI visualization in the Visualizations pane, pictured here:
Note the resulting KPI chart below. The field parameters have been adjusted to show the overall sales for the fiscal year. To the team's delight, the chart shows they in fact surpassed their goal!
Key Influencers visualizations
You may also remember that Kate's team discovered that the upward trend in sales was related to a surge of young family buyers in specific postal codes. This is called a key influencer, another fundamental BI analytics value. The definition of a key influencer is "a factor that drives a metric you're interested in." In other words, key influencers are factors that contribute to important data trends, and oftentimes can be things like buyer demographics, geographical region, or the performances of competitive brands.
Here is an example of a key influencers chart:
In the case of Kate's team, the discovery of young family buyers as key influencers of the sales increase came about after appropriate queries discovered relationships in the data modeling stage. Ultimately, the team likely created a key influencers chart to properly showcase the relationship, using the key influencers chart option in the Visualization pane, pictured here: