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Power BI Tutorial: Analyze Data and Prepare Insightful Reports

  • Denys Arkharov

    • Understanding the Power BI ecosystem

    Power BI is one of the most popular services for businesses and analysts, with over 90% of Fortune 500 companies using it. It is a powerful and budget-friendly suite for getting ideas from thousands of rows in a spreadsheet through a simple drag-and-drop dashboard. 

    This Power BI tutorial will explain how the platform works in everyday scenarios, using examples to help you understand it better. We’ll also discuss pricing and its features. By the end, you’ll know how to make the most of this tool for your needs. Let’s begin!

    What is Microsoft Power BI?

    Power BI (or Power Business Intelligence) is Microsoft’s platform, specializing in data visualization and business intelligence for marketing. Its main purpose is to help users gather, analyze, and visualize data to see the results of the marketing activities and make decisions about the next steps.

    With the help of this tool, you can upload raw data from various sources, such as CSV files, spreadsheets, databases, cloud services, and apps, and then transform it to create interactive reports and dashboards. These reports show data in an easy-to-understand way, helping companies deal with complex information.

    What are the main goals of Power BI as a business intelligence tool?

    Power BI is a versatile business intelligence tool that lets you connect to various data sources, transform data, and visualize it interactively. 

    Moreover, although many experienced data analysts and data scientists find Tableau’s user interface super sleek, Power BI is considered more user-friendly for learners and non-data analysts due to its intuitive interface.

    Overall, Power BI’s robust set of features, ease of use, agility, and seamless integration with other systems make it excellent at data visualization and business intelligence for marketing. This also makes it an indispensable tool in the fields of big data and data science.

    Power BI history

    It started as a set of add-ons for Microsoft Excel known as Power Query, Power Pivot, and Power View. These tools allowed users to connect to different data sources, clean up data, and create interactive visuals. While they were popular, they had limitations because they needed the desktop Excel software.

    In 2013, things changed with the introduction of a cloud-based version of Power BI. Now, only with access to a browser, people could create and share reports and dashboards. 

    In 2015, Microsoft made a big update, introducing the Power BI Desktop software for more advanced modeling and visualization. They also launched a mobile app so people could view and interact with data on phones.

    Since then, Power BI has kept growing. In 2016, they added Power BI Embedded for developers, and in 2017, introduced Power BI Report Server. In 2019, the Premium version arrived with even more features and capabilities.

    Since then, it has become a must-have tool for businesses worldwide, used in many industries like banking, retail, insurance, oil and gas, and manufacturing.

    Power BI Desktop vs. Service and other tools

    Let’s take a look at 5 important tools to help you work with data, make reports, and share it with your colleagues. These tools are essential for anyone, whether you’re just starting or want to improve your skills:

    Tool #1. Power BI Desktop is the main development and business intelligence tool for creating reports and dashboards. It is also a no-cost software you install on your computer. It allows you to connect to various data sources, shape your data, and create a data model by combining them.

    Tool #2. Power BI Service is a cloud-based platform that allows you to publish, share, and collaborate on your reports and dashboards. Among its features are automatic data refresh, role-based access control, and creating and managing content packs for sharing with others.

    By using both Power BI Desktop and Power BI Service, you can create comprehensive data analysis workflows that leverage the strengths of each platform.

    Power BI DesktopPower BI Service 
    – This is a free Windows-based desktop application. 
    – To start using it, you need to first download and install it on your computer.
    – Power BI Desktop allows you to create reports that you can then publish to Power BI Service.
    – While both the desktop and online versions allow you to create reports, the desktop version has more powerful data manipulation capabilities and supports more data sources.     		– This cloud-based platform allows you to share, collaborate, and access your reports and dashboards via web browsers. 
    – To access Power BI Service, navigate to https://app.powerbi.com/. You’ll need to have a Power BI account.
    – The service is particularly useful for teams and organizations as it facilitates real-time collaboration and ensures that everyone can access the latest data insights.

    Tool #3. Power Query Editor helps you transform and shape your data before it’s loaded into Power BI. It allows you to clean, filter, and merge data from various sources, ensuring your dataset is well-prepared for analysis.

    Tool #4. DAX (Data Analysis Expressions) is a formula language that helps you create custom calculations and measures. With DAX, you can do advanced calculations, create complex relationships between tables, and generate insights from your data. Learn more about Power BI DAX functions in our guide.

    Tool #5. Power BI mobile app allows you to access and interact with reports and dashboards on your mobile devices, ensuring that data insights are available on the go. It works offline, so you can view reports even without an internet connection.

    Power BI architecture and features

    The main component of Power BI is its data modeling engine, which organizes and optimizes data to ensure efficient analysis.

    Once your data is prepared, you can upload it to Power BI Service, a cloud-based platform. Here, you can collaborate on reports and dashboards with your team. It also provides the Power BI gateway for integrating on-premises data sources securely. 

    Let’s take a look at the key components:

    Visualization

    Power BI offers a wide range of visualization types, from simple number displays to complex gradient-colored maps.

    Datasets

    Datasets are the raw materials for creating your data insights. They are basically collections of numbers used to make visualizations. A dataset can be as straightforward as a single table from an Excel spreadsheet or a blend of data from various sources, offering a unique cluster of information. Filtering data within datasets allows you to focus on specific information relevant to your analysis.

    Reports

    A report is a collection of visualizations that present data. They can spread across several pages. You can create different visual elements, arrange multiple pages, and structure them to better communicate your message. Whether you’re analyzing quarterly sales, monitoring product growth, or examining wildlife migration patterns, reports help you gather and showcase your data insights in a clear and shareable manner.

    Dashboards

    Dashboards serve as the front door to your reports and visualizations. They consist of selected visuals that offer immediate insights, are limited to a single page, and provide end users with an interactive gateway to explore underlying reports. For more details, see our article on Power BI dashboard examples.

    Tiles

    Tiles are individual visuals within a dashboard, which contain specific visualizations and can be moved, resized, or arranged to create a connected and informative dashboard. While users can interact with tiles when using shared dashboards, they cannot change their layout or size.

    For more details, see our article on Power BI architecture.

    How does Power BI work?

    It connects to data sources, then extracts and transforms the data, and finally creates reports and dashboards. Users can then publish these reports to Power BI Service for sharing and collaboration. Let’s take a look at the process in more detail:

    1. Data integration. You can connect to different sources, including databases (SQL Server, Oracle, etc.), cloud services (Azure, AWS), spreadsheets, and web services. 

    Explore how to connect API to Power BI.

    2. Data transformation. After connecting to data sources, the Power Query component will extract and transform numbers. It can delete duplicates, standardize, and combine data from different places, making it pretty for analysis.

    Example: 

    Suppose you have a dataset with different date formats (e.g., “01/15/2023” and “15-Jan-23”). Power Query can be used to standardize these formats into a consistent one, making it easier to work with.

    3. Data modeling engine allows users to create relationships between different tables, define hierarchies, and calculate new measures using Data Analysis Expressions (DAX). This step creates a structured data model for analysis.

    4. Report creation. After that, you can generate reports and dashboards in Power BI Desktop, selecting from various charts (i.e. column chart, bar chart), tables, and maps.  

    5. Publishing. Once reports and dashboards are ready, you can publish them to Power BI Service, Microsoft’s cloud-based platform for sharing with team members. 

    6. Data refresh. It supports scheduled data refreshes, guaranteeing that reports always display up-to-date information. It automatically updates the data from the original sources based on defined schedules.

    7. Sharing and collaboration. Reports and dashboards can be securely shared with colleagues or stakeholders through Power BI Service. At the same time, as an owner, you can control access to the content through role-based permissions.

    8. Mobile access. Mobile apps for iOS and Android devices give access to reports and dashboards on smartphones and tablets. They are optimized for smaller screens and offer offline access.

    9. Integration and embedding. For more advanced KPIs, developers can use Power BI Embedded to integrate reports and dashboards into custom applications or websites.

    Getting started: Installation and first steps with Power BI

    In this step-by-step tutorial, we’ll walk you through the essential steps of installing and using Power BI to create reports and share them. We’ll also explore features like Power Query Editor and data refreshing to help you get started with data analytics.

    How to install Power BI

    The default window where you can access all needed features is called “Report” view.

    How to create your first report

    Power BI data connections and data sources

    Power BI integrates seamlessly with Microsoft products and services like Azure SQL Database, Azure Synapse Analytics, SQL Server, Excel, SharePoint, etc. These integrations make it highly compatible with the tools many businesses already use. In fact, it’s one of the key reasons why Power BI has a very large user base.

    To see the data sources, go to the Home tab and choose Get Data > More. In the Get Data window, you can browse through the list.

    It’s also important to note that Power BI supports non-Microsoft data sources, such as BD2, MySQL, BigQuery, and many online services like Salesforce, Google Analytics, and Databricks. 

    See also our complete list of Power BI data sources and how to connect to them.

    At the same time, a great many marketing apps like Facebook Ads, Mailchimp, Google Search Console, and others are not natively supported. However, you can resolve this with Coupler.io. It is a reporting automation tool that lets you connect 60+ cloud data sources to Power BI to create self-updating reports. Coupler.io also offers dynamic dashboard templates in Power BI that can be a game-changer for reporting efficiency. Try it yourself – choose the needed data source from the drop-down list and follow the in-app instructions to set up the connection.

    The art of data preparation using Power BI

    Once you’ve connected to your data sources, the next step is to prepare and transform your data for analysis. This is where Power Query comes into play. Power Query is an ETL (Extract, Transform, Load) tool embedded within Power BI that allows you to clean, reshape, and transform your data before it is loaded into Power BI.

    Some key features of Power Query:

    By leveraging Power Query, you can ensure that your data is optimal for analysis. This step is crucial for efficient data analytics in Power BI, as clean and well-structured data leads to more accurate and insightful results.

    How to clean data with Power Query Editor

    When you’re working in Power Query Editor, every action you take to shape your data gets recorded as a step. These steps are automatically applied each time the query connects to the data source, ensuring your data remains consistently formatted according to your specifications. 

    What are Power BI dataflows?

    Power BI dataflows are a self-service data preparation tool that enables users to easily extract, transform, and load (ETL) data from various sources into the Power BI service. They centralize and standardize data preparation tasks and also allow you to easily create reusable data transformation processes.  

    Key features of Power BI dataflows

    How do dataflows work and store in Power BI?

    Power BI dataflows consist of tables created and managed within workspaces in the Power BI service. These tables, akin to database tables, have columns for storing data. Essentially, they serve as an advanced version of Power Query Online, allowing you to perform all Power Query operations independently in the cloud.

    In dataflows, data from various sources is stored as entities in Azure Data Lake Storage without requiring an additional Microsoft Azure subscription. This allows you to access dataflows directly from your Power BI datasets, reports, or dashboards.

    For instance, managing operations like sorting or replacing data across multiple datasets can be challenging. With Dataflows, you can carry out ETL operations in the Power BI service and then use them in Power BI Desktop. This centralization and reuse of tables simplify the creation of Power BI reports across multiple projects.

    How to create reports and visualizations in Power BI

    Creating insightful reports and dashboards is at the heart of Power BI’s capabilities. This section will provide an overview of the essential steps to create, publish, and share your reports effectively.

    Create basic reports

    To create a report, click the Get Data button to import data from a data source. 

    After you see a preview of your data, you can directly Load it to Power BI. If you want, use Power Query to clean and transform your data before loading it by clicking the Transform Data button (see the following screenshot): 

    Now, it’s time to build visuals. Click a chart icon from the Visualization pane. For example, click the Card icon to create a scorecard. Then, drag and drop the appropriate field into the Fields area. 

    In the Format visual tab, you can customize the visual by adjusting properties like colors, labels, and formats.

    Common visualization types

    Power BI offers a wide range of options to help you present data in the most impactful way. Here are some common types of charts/graphs you can use for effective data visualization:

    Choosing the right visualization type is crucial for effective data analytics using Power BI. It helps enhance the readability and insightfulness of your reports.

    How to publish to Power BI service

    Create dashboards

    Using dashboards enhances your data analytics in Power BI by offering a streamlined and easily accessible high-level overview of your key metrics.

    While you can create reports in either Power BI Desktop or Service, you can only create dashboards specifically in Power BI Service:

    Enhance your reports with advanced features

    With interaction in reports, it’s easier to understand, track, and manipulate complex data. There are different ways to achieve interactive filtering but we’ll only focus on drill through here. In this guide, we’ll explain how to set up and use drill through reports in Power BI.

    What is Drill Through in Power BI?

    Drill through is an interactive filtering feature that allows report consumers to expand on some data items. An overview report page is meant to contain a summary of all your datasets so it’s advised to create other pages for the breakdown of certain categories. It makes it possible for report consumers to drill from one visual to another report page. The action also connects all filters that apply to the visual to the drill-through page. Power BI drill through helps to keep your report organized and improves the report experience.  

    Example of Drill Through in Power BI

    In case you haven’t seen drill through before, we’d like to show what drill through actually looks like. First, you’d have a main page that contains the primary visuals for your data. Here’s a contact page featuring a slicer visual for the First Name data field.

    When you right click on each first name, you should see a dropdown with a list of actions. If drill through is working, you’ll see the target page name when you hover. 

    The contact details page has specific information about each contact. This is very useful in a situation where you have to summarize multiple aspects of your data on a page. It allows you to simply use a drill through to explore the required details on another page. 

    In the next chapters, we’ll provide a step-by-step tutorial on how you can add and set up drill through in Power BI. 

    How to set up Drill Through in Power BI?

    Drill through allows you to navigate from one report page to another. You just have to pick a data point that needs to be explored, design the standalone page and connect. It can be done with just a couple of clicks on either Power BI service or Power Bi desktop. Before we describe the process in full, here’s a list of the main steps for drill through:

    What are Drill Through fields in Power BI?

    Drill through is an interactive feature that allows report consumers to navigate from a summary or overview report to detailed report pages by right-clicking on a data point. A drill through field helps to filter the detailed report and show the information pertaining to that data field. You can add drill through fields in Power BI to explore breakdown of specific visuals or data points.

    Here’s an image of drill through fields that have been added to a Power BI report. 

    When you drag the data field to the drill through section, it automatically allows the user to drill through to the next page when any of the entities have been selected. For example, you can right-click on Amarilla wherever the “product” data point is found, and you’ll be navigated to a destination page that displays more information or KPIs concerning the Amarilla product. This provides the opportunity to focus on each data entity and analyze each of them separately.

    Note: Power BI automatically creates a back button visual to return to the source page. It becomes a button in the published report and you can also use your own image for it.

    Power BI Drill down vs Drill Through

    Power BI has interactive features that let you organize data in a way that shows the relationship of general data to more comprehensive details. The most popular filtering options in Power BI reports are drill down and drill through. The first one, drill down, is an analytical feature that allows report consumers to visualize lower levels of a data point by nesting additional variables within the same chart or visualization.

    You can add all your data to a report and only reveal them when the drill down button is clicked. It allows you to hide and show other report items when needed. Drill down in Power BI allows you to understand data and extract insights without leaving the page.

    On the other hand, the drill through feature in Power BI allows you to visualize detailed data about a specific data point or KPI in a separate report. Although drill down and drill through are quite similar, they can’t replace each other. It’s best to use the drill down feature when you need to provide extra details within a chart or data set. But Power BI drill through is preferred when you’d like to explore other relevant aspects of the data.  

    Automate data exports to Power BI with Coupler.io

    Get started for free

    Use dashboard templates for efficient data analytics in Power BI

    Benefits of using Power BI dashboard templates:

    Let’s look at some Power BI dashboard examples by Coupler.io that can help you analyze and report data more efficiently. Remember, all of these are dashboard templates that you can use for free. They’re also easy to set up, allowing you to get live dashboards with your data in just a few minutes. 

    SEO dashboard template 

    This SEO dashboard template provides detailed insights into your website’s SEO performance. It lets you monitor click-through rates (CTR), impressions, clicks, and other important metrics indicating your website’s visibility in search engines. The dashboard is designed to be dynamic with interactive reports. The visuals also update automatically based on the Impressions/Clicks button selected.

    With this dashboard, you gain a real-time view of your site’s SEO health, making it easier to spot trends and adjust strategies as needed.

    Get the free dashboard template

    Facebook Ads dashboard template

    This Facebook Ads dashboard template provides a detailed overview of your advertising campaigns’ performance. This dashboard is equipped with a no-code Facebook Ads connector for Power BI, allowing you to connect and pull your data for analysis effortlessly. 

    The clean and organized layout makes it simple to navigate through various metrics, providing a comprehensive view of your advertising efforts. 

    Get the free dashboard template

    This Google Ads dashboard lets you track the performance of your Google Ads advertising campaigns and track the monthly dynamics of key metrics.

    Get the free dashboard template

    Web analytics dashboard template

    This web analytics dashboard template provides comprehensive insights into your website’s traffic acquisition, audience, and conversions with Google Analytics 4 data seamlessly connected to the dashboard. 

    This dashboard provides a unified view of all your campaign metrics with clear visualizations that simplify complex data.

    Get the free dashboard template

    PPC dashboard template

    This PPC multi-channel dashboard template is designed to track your pay-per-click campaigns across multiple platforms. It consolidates all relevant metrics to provide a unified view of your PPC performance.

    This dashboard integrates seamlessly with all major ad platforms, ensuring you have a comprehensive understanding of your PPC efforts in one place. 

    Get the free dashboard template

    Brand awareness dashboard template

    This brand awareness dashboard helps you understand how your advertising campaigns on Facebook, Google, and Bing correlate with branded searches in Google to optimize your brand awareness.

    Combining data from different ad platforms, this dashboard reveals the correlation between your advertising campaigns and branded searches.

    Get the free dashboard template

    Predictive analytics with forecasting in Power BI

    Forecasting and time-series modeling are essential techniques for predicting future trends based on historical data. Power BI provides built-in analytics tools to help you make accurate forecasts and analyze time-series data.

    Forecasting in Power BI uses a time-series model to analyze historical data, incorporating seasonality into predictions. This feature is crucial for industries with seasonal fluctuations.

    Here’s how you can access the Power BI forecasting feature:

    Learn more about Power BI predictive analytics and the value of first-party data for advanced performance analysis.

    Power BI uses Exponential Smoothing as its built-in forecasting model. However, you can also use linear regression, ARIMA, and other models by integrating these models using DAX or scripts in R or Python.

    If you have a Premium per-user or per-capacity license, you can build ML models directly in Power BI using Azure Machine Learning and AutoML. The steps include creating a dataflow with input data and then choosing a model like Binary Prediction, Regression, or General Classification.

    Power BI forecasting for marketing: An overview

    Forecasting in Power BI works by analyzing historical data using a time-series model. It also incorporates seasonality into predictions. This is crucial for marketing, where many industries experience seasonal fluctuations. Whether it’s holiday shopping spikes or summer slumps—understanding and anticipating these patterns allows for better strategic planning.

    By doing marketing data analytics and forecasting with Power BI, marketers can analyze various interactions in the funnel and predict who is ready to purchase and who isn’t. This will contribute to decision-making. Marketing efforts can be directed more efficiently, targeting those most likely to convert.

    How to access the Power BI forecasting feature

    The feature of forecasting in Power BI can be found within the Analytics pane in the Visualizations area. To access the Analytics pane, you’ll need to select a visual first, then expand the Forecast section of the Analytics pane.

    There are several points to keep in mind:

    Note: This article uses the Power BI Desktop version of April 2024. Future versions of Power BI may have the forecasting options consolidated in the Format pane. These changes in the Visualization tab are currently still in preview. If you want to see the updated Visualizations and Format pane, you can enable the ‘On-object interaction‘ feature by selecting it from File > Options and settings > Options > Preview Features > On-object interaction.

    How to do forecasting in Power BI: A step-by-step tutorial

    Forecasting in Power BI is designed to be straightforward. It’s easy for both experts and beginners. Here’s a friendly guide to get started.

    Step 1: Get your data

    Forecasting in marketing typically begins with collecting data from various sources, like CRM systems, marketing tools, web analytics, social media, and ad platforms. This information builds a digital profile for each prospect, reflecting their interactions, like email activities and website visits. By comparing these profiles with patterns from successful sales, marketers can predict potential purchases. 

    You can use Power BI built-in connectors by clicking the Get data button. Select a connector to import data from sources like Excel, CSV, SQL Server, and many more.

    However, Power BI does not provide enough built-in connectors to marketing data sources. So, Coupler.io can be an alternative way of bringing your data into Power BI. This reporting automation solution is a multi-source connector that allows your data transfer to be more manageable in a single place.

    Getting started with Coupler.io is super easy. Select your data source in the form below and click Proceed.

    You’ll need to create a Coupler.io account for free and then configure your data source connection. For instance, if you want to make a forecast using GA4 data, you’ll need to connect to your GA4 account and select the metrics and dimensions to be included in your report.

    You can organize your data by applying transformations, such as filtering data, hiding unnecessary columns, and creating custom columns with formulas. Also, Coupler.io lets you join data from multiple sources. 

    Once the report is ready, load it to Power BI following the in-app instructions. Do not forget to schedule the refresh of your source data in Power BI. For this, you simply need to enable the Automatic data refresh and configure the desired schedule. 

    That’s it! You can now create Power BI reports and apply forecasting.

    Try Coupler.io for free

    Step 2: Create a line chart

    Select the line chart icon from the Visualization pane. Remember to use time-series data for your X-axis value. In the Y-axis, add the column with the metric you want to forecast. This could be website users, ad clicks, open rate, click-through rate, and so on.

    Step 3: Apply forecast

    To apply a forecast, click on your line chart to select it. Then, click the Analytics pane in the Visualization area. You’ll find the Forecast section. Toggle the forecast button to On. 

    After applying a forecast, you’ll see the gray area in the visual. The middle line within this area is the forecast value. It’s surrounded by upper and lower bound lines that indicate the level of uncertainty associated with the forecast. A wider band suggests greater uncertainty, while a narrower band implies more confidence in the predictions.

    Step 4: Adjust your forecast

    Adjusting your forecast is crucial for fine-tuning its accuracy and relevance to your specific needs. Here are some forecast options you can adjust:

    After adjusting these forecast settings, you’ll see updated forecast data visualized on the line chart. 

    If you want, you can customize the Forecast line. This includes changing its style, color, and transparency, allowing for a more tailored visual representation of your forecast data.

    What Power BI forecasting model is used by default?

    Power BI is a business intelligence tool with a robust feature for time series forecasting. It uses Exponential Smoothing as the built-in forecasting model. 

    Check out our Power BI full tutorial.

    However, you have the flexibility to implement other forecasting models, such as linear regression and ARIMA. Power BI supports integrating these models through DAX for simpler cases or R/Python scripts for more advanced analysis.

    Exponential Smoothing

    This is the built-in forecasting model in Power BI, integrated within the line chart forecasting options. Exponential Smoothing is simple and excellent for handling time series data. It can be applied to smaller datasets and still produce reliable forecasts. 

    Exponential Smoothing automatically detects seasonal patterns in your data as you start a forecast using a line chart. This model uses two different algorithms to accommodate different data characteristics: the ETS AAA algorithm (also known as the Holt-Winters algorithm) for seasonal data and the ETS ANN for non-seasonal data.

    While this model is efficient and powerful, it might perform poorly for complex forecast data patterns that go beyond simple seasonality.

    ARIMA (AutoRegressive Integrated Moving Average)

    ARIMA offers greater flexibility compared to Exponential Smoothing when your data has irregular fluctuations without a clear pattern or trends that change over time. 

    To use ARIMA in Power BI, add an R or Python script visual to implement it. Depending on the visual you choose, you’ll need to write scripts using R or Python to apply ARIMA forecasting to your data.  

    Linear regression

    This model is a basic yet powerful way to predict future values by understanding relationships between variables. It works best when your data is linearly related and you want to forecast an outcome based on new input data.

    One convenient way to implement linear regression in Power BI is to use DAX. Functions such as LINEST or LINESTX can calculate slope and intercept, helping you create a linear equation for predicting future results. 

    ML models

    You can build ML models in the Power BI service for forecasting without deep programming knowledge or expertise in machine learning. However, this requires a Premium per-user or per-capacity license. 

    Through Power BI’s integration with Azure Machine Learning and its AutoML feature, you can build ML models directly in Power BI and apply them to your datasets.

    The steps include creating a dataflow with the input data. You can then choose a model such as Binary Prediction, Regression, or General Classification to apply to a table in your dataflow. Depending on your data, some model types might not be available. 

    Dataflow in Power BI

    How to create data flows in Power BI

    To create dataflows in Power BI, you need a premium per-user trial license. 

    Create a new workspace in the Power BI service. To do so:  

    You can now see the newly created space. Click on the +New option. A drop-down section will appear, and you can select the Dataflow.

    You will see a dashboard with 4 options to create a new dataflow:

    Define new tables

    In Power BI, dataflows allow you to define new tables and connect to various data sources. Then, you can transform and load data into tables used in Power BI reports and dashboards. 

    Link and tables from other dataflows

    Linking tables from other dataflows in Power BI can be very useful for reusability, maintaining consistency, and reducing redundancy. 

    To link tables from other dataflows in Power BI, click on Add linked tables.

    Set up the connection or keep the settings as they are for the Azure active directory organizational account. 

    Now you can find access to the previously created or existing dataflows on the left sidebar. 

    Import dataflows as JSON

    To import dataflow in Power BI, select the import model 

    Export dataflows as JSON

    Exporting and importing dataflows as JSON enables easy backup, sharing, and migration of dataflows across different environments or workspaces. Here’s how you can export and import Power BI dataflows.

    Create a dataflow by using a CDM folder

    Creating a dataflow using a CDM folder standardizes data schemas and facilitates smooth data integration. However, it requires the full path to the CDM file stored in ADLS Gen 2. Let’s see how to do it:

    When to use dataflows in Power BI

    Let’s explore the scenarios of when and where you can use Power BI dataflows to transform data management strategies for business intelligence.

    To set the scheduled refresh

    Dataflows in Power BI integrate data from sources that frequently update. For instance, transactional databases, web APIs, or daily operational systems, typically require scheduled refreshes. 

    Dataflow comes with a Schedule refresh feature to ensure that the data in your Power BI reports remains current and reflects the latest information from these dynamic data sources.

    To perform data integration and ETL

    Dataflows simplify data integration and ETL tasks, crucial for organizations such as healthcare providers. They consolidate patient records, financial data, and operational insights from various systems into Power BI. 

    This also enables cohesive analysis of patient demographics, treatment outcomes, financial performance, and operational efficiencies. Overall, using dataflows supports easier decision-making and strategic planning.

    To reuse data tables

    Dataflows simplify the process of creating consistent and reusable data tables across different reports and analyses. For example, a retail chain can create a dataflow to aggregate daily sales data from each store location into a standardized format. 

    This can then be reused in various reports that analyze sales trends, inventory management, and store performance metrics. By maintaining a single source of truth for sales data, you can ensure consistency and accuracy in reporting across all departments and stakeholders.

    To analyze sales performance

    Dataflows streamline sales data analysis by centralizing and standardizing data from various channels and regions. For example, a global consumer goods manufacturer can consolidate sales data from distributors, retailers, and e-commerce platforms in Power BI. 

    This integration provides insights into regional sales performance, product preferences, pricing strategies, and market trends. It also helps sales teams identify growth opportunities, optimize inventory, and refine targeted marketing efforts.

    To measure marketing campaign effectiveness

    To assess marketing campaign impact across digital platforms, you need to combine data from social media, Google Analytics, email campaigns, and surveys in Power BI dataflows. This allows you to analyze metrics like reach, engagement, conversion rates, and ROI to optimize strategies and budget allocation for maximum effectiveness.

    Integrating dataflows with Power BI reports

    You can integrate dataflows with Power BI reports for consistency, accurate data centralization and transformation. This will help to create an organized and structured report for real-life cases. In addition, integrating with Power BI reports also improves collaboration by providing a single source of truth for all team members.  

    Follow a few simple steps to integrate dataflows in Power BI reports:

    It will show the created dataflows in the Power BI service. Select the dataflow, choose the table, and click on Load. The selected data will appear on the Power BI desktop dashboard.

    Go to the table view icon, and you can see the table. You can perform operations like creating a date table, cleaning, filtering, and adding columns by DAX function and Power Queries. Once your report is complete, save your Power BI Desktop file (.pbix) locally.

    Advanced data analysis techniques in Power BI

    Power BI’s advanced features enable you to delve deeper into your data, uncover valuable insights, and make more informed decisions. This section will explore two of these powerful features—Data Analysis Expressions (DAX) and the forecasting capability—and explain how you can use them for in-depth data analytics in Power BI.

    DAX language, formulas, and functions

    Data Analysis Expressions (DAX) is a formula language used in Power BI to create custom calculations and aggregations. DAX is similar to Excel formulas but is much more powerful and designed for data modeling and advanced analytics within Power BI. 

    Two key uses of DAX are for creating calculated columns and measures: 

    You can use DAX functions within formulas. They allow you to perform various analytical tasks like aggregation, filtering, and data manipulation. With many pre-built DAX functions in Power BI, your advanced analytics tasks become much easier.

    See also the practical examples of useful DAX functions in our top 10 Power BI functions for marketers.

    Publication and sharing reports/dashboard in Power BI

    Publishing to the Power BI Service 

    Once you are done with creating your report, share it with others by publishing it to Power BI Service:

    1. Save your report: Ensure your report is saved in Power BI Desktop.
    2. Publish: Click the Publish button in the Home tab. Then, select your workspace for publishing.

    Once published, your report will be available online and accessible via web browsers. Collaborations and real-time updates are now also possible.

    Sharing reports or dashboards 

    Collaboration is crucial for data-driven decisions. Power BI makes sharing easy:

    1. Share directly: Use the Share button in Power BI Service to invite colleagues to view your reports or dashboards. You can control permissions to ensure the right level of access.
    2. Embed: Embed your report or dashboard in an internal website or app using the embed code provided by Power BI.
    3. Export: Export reports to Excel, PDF, or PowerPoint for offline sharing or presentations.

    By leveraging these sharing options, you can ensure that your insights reach the right people in a timely manner.

    Keeping data fresh: Automation and scheduling in Power BI

    What are the options to schedule refresh in Power BI dataset?

    The case introduced above consists of two stages where I need to automate data flow:

    1. From a cloud source to the report in Power BI desktop
    2. From the Power BI desktop to the published report in the Power BI service

    Scheduled refresh in Power BI desktop

    You can’t schedule refresh in Power BI desktop. Even if you import data or connect directly to the data source using DirectQuery, you have to refresh the data manually.

    Moreover, there are not enough built-in connectors to marketing and analytics apps such as Google Search Console, Facebook Ads, and others. So, it’s better to use a third-party solution that can solve both tasks. With Coupler.io, you can connect 60+ cloud sources and schedule data refresh in Power BI without any coding or complex setup. I’ll show you how to use it in the next section.

    Scheduled refresh in Power BI service

    The only natively scheduled refresh in Power BI is available between your desktop app and the Power BI service (app.powerbi.com). To implement this, you need to establish a gateway connection. You can check out the user-friendly Microsoft documentation to learn how to do this… just kidding, of course ? Even software engineers struggle with it sometimes. So, I’d better share precise and practical instructions in the respective section. Let’s go!

    How to schedule refresh in Power BI desktop with Coupler.io

    As I mentioned, Coupler.io is a reporting automation solution that allows you to connect different apps to Power BI. For our example, let’s get data from Google Search Console to create a dynamic SEO report. 

    For this, I need to add a new importer and select the source and destination apps: GSC and Power BI, respectively.

    You will most likely have another source for your project so feel free to select it in the form below and click Proceed. You’ll be offered to create a Coupler.io account for free and then configure the connections.

    1. Connect source

    Follow the instructions to connect your data source account. You’ll need to either enter the data source credentials or log in to your account. Then, specify the data to load. This could be raw data or reports. The latter can be predefined or customized by selecting metrics, dimensions, report period, etc. 

    Here is what it looks like for my report based on the Google Search Console data.

    2. Organize data

    The next step is to transform or organize your data. Once you click the Transform data button, Coupler.io loads up to 500 rows for preview. You can:

    3. Load data to Power BI

    Next, you’ll need to load data to Power BI. Follow the in-app instructions, which are as follows:

    Power BI will establish the connection with Coupler.io, and you’ll see your source data in the Power Query Editor. You can now load it to Power BI. 

    4. Schedule refresh

    Get back to Coupler.io since we have some jobs to complete. Click Set up a schedule to go to the final step. Toggle on the Automatic data refresh and configure the schedule to your liking.

    Click Run importer to finalize the setup. From now on, Coupler.io will refresh the data from your source in Power BI according to the specified schedule. 

    You can then create your dynamic reports or use the predesigned report templates as I did. 

    I used this SEO report template by Coupler.io. It’s a white-label solution with a prebuilt connector to Google Search Console which allows you to start using the report in a matter of minutes. Follow the instructions on the Readme page to connect your data and load it to the predesigned template for the Power BI desktop. You don’t need to separately schedule refresh in Power BI since this functionality is enabled by default here.

    Try Coupler.io for free

    In addition to 60+ ready-to-use Power BI integrations, Coupler.io allows you to create custom ones without coding. Learn how to connect API to Power BI.

    How to schedule refresh in Power BI service with Coupler.io

    Earlier, I mentioned that you can schedule refresh in app.powerbi.com using a gateway connection. This is true if your semantic model (source of data ready for reporting and visualization) is on-premises. 

    In our case, Coupler.io extracts data from the cloud source – Google Search Console. Therefore, you can schedule refresh of the online published reports without gateways. Let me show you how to do this.

    1. Publish your desktop report to Power BI service

    In a few moments, your report will be available online. 

    2. Schedule data refresh in Power BI

    Open your recently published report. Go to More options => View semantic model.

    Do not forget to click Apply to confirm your selection.

    Congratulations! Now, your report will refresh automatically on a schedule!

    Note: Make sure you align your schedule refresh frequency in Coupler.io and Power BI service to always have the recent data in your reports.

    How to schedule data refresh in Power BI service using gateway

    And what if the data source for your report is not in the cloud but on-premises? In this case, you need Power BI gateway. In our article, we explained what it is, how to install and use it. So, if you haven’t worked with the standard or personal gateway in Power BI before, then you’d better check out this information first. 

    So, if you publish a report with the on-premises data source, you need to:

    1. Connect to Power BI gateway data source

    If everything is set up correctly, you will see a message saying the connection was created.

    2. Map the gateway’s data source to your report’s semantic model (dataset)

    Power BI pricing and licensing

    Power BI offers flexible pricing options to meet various organizational needs:

    Free Account (Microsoft Fabric preview):

    What is Power BI Pro?

    Power BI Pro is a paid plan for Power BI that gives you extra features compared to the free version. It is extremely useful if you work as a data analyst because you can share reports, schedule data refreshes for up-to-date information, and add more advanced things to your data analysis, like complex calculations and custom reports.

    What are the differences between the pro and free version?

    Power BI Premium (per user):

    Power BI Premium per Capacity (includes Microsoft Fabric):

    Power BI Premium per Capacity (Includes Microsoft Fabric – Fabric SKUs F64 and above):

    If you’re a small group or just starting out, you can use the Free Account to try basic Power BI features. If you’re a small to medium-sized business and want to work together and share data insights, Power BI Pro is a good choice. For bigger companies with more complex data needs, there’s Power BI Premium. And for very big enterprises with lots of data, there’s Power BI Premium per Capacity, which offers even more advanced sharing abilities. Your choice depends on how big your company is, how complex your data is, and how much collaboration you need.

    Troubleshooting and FAQs

    Can I learn Power BI by myself?

    Yes, you can learn Power BI on your own. There are many online resources, tutorials, and courses available to help you get started and advance your skills. If you’re a newbie, check this Power BI tutorial for beginners for building your first report within Power BI. 

    How long does it take to learn Power BI?

    It depends on your existing knowledge and the expertise you want to achieve. For beginners, you can grasp the basics within a few days or weeks of consistent learning. To become proficient and handle more complex tasks, it may take several months of regular practice and learning.

    Is Power BI harder than Excel?

    Power BI and Excel have some things in common, but they’re used for different things. Power BI is great for handling big datasets and making advanced visualizations, although it can initially seem tricky. Excel is versatile and can do many tasks, including data analysis. The one you pick depends on what you need and how complicated your data is.

    Power BI – clear Drill Through Filters

    Drill through in Power BI helps to navigate the end user to a different page in the same report or to another report, i.e. cross-report drill through. It ensures that you do not clutter a report page with too many details. Rather, you can click to get these details when required. 

    If you want to clear drillthrough filters, all you have to do is click on the x symbol next to each field name to remove the filter. You’d repeat to completely clear multiple fields. You can return to the source page to check if the drill through action is no longer active.

    What to do if Power BI Drill Through is not working?

    Power BI drill through may not work if you added the wrong drillthrough filters. You have to know the data point and the target page it should lead to. If you notice that Power BI drill through isn’t working after adding the right filters, you should clear them and start over. Else, you should save your report, close Power BI, and open it again. 

    Next steps in your Power BI journey

    Power BI is a powerful suit for data analysis and visualization, good for users of all levels. It simplifies data modeling and report creation, making it accessible and user-friendly. Furthermore, as it can connect to various data sources, work with large volumes of information, and provide regular updates, companies of any size and from any industry will benefit from this tool.

    Check our articles on Power BI alternatives to learn how Power BI compares with other popular data viz tools. Or you can have a more detailed comparison overview in Power BI vs Tableau and Looker Studio vs. Power BI.

    For more tips on using Power BI, check our article on building a Power BI Gantt chart.

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