Lesson 1.2: Power BI Components

Objective: Understand the key components of Microsoft Power BI and how they work together to turn data into interactive insights.

Content:

• Power BI building blocks (datasets, visualizations, reports, dashboards, tiles)
• Power BI Desktop, Service, and Mobile
• Interactive features and AI-powered insights in Power BI
• Creating and sharing dashboards (best practices and scenario)

📦 Power BI Building Blocks

Power BI is built on a few fundamental building blocks. Understanding these will help you see how Power BI pieces together data and visuals:

• Dataset: A collection of data that you import or connect to in Power BI. Power BI uses datasets as the source for creating visualizations. A dataset can be as simple as a single Excel table or a combination of multiple data sources[4]. Power BI includes many data connectors (Excel, databases, online services, etc.) to bring in data and build these datasets[4].

• Visualization: Often called a visual, it’s any visual representation of data – for example, a chart, graph, map, gauge, or KPI card. Visualizations help turn raw numbers into insights by presenting data in a visual context[4]. Power BI provides a wide range of built-in visuals (bar charts, line charts, pie charts, maps, tables, etc.) and even allows custom visuals[1]. A visual can be simple (a single number or indicator) or complex (like a color-coded map showing regional data).

• Report: In Power BI, a report is a collection of visualizations across one or more pages[4]. You can think of it as a multi-page canvas for related visuals, similar to a report in other contexts. All the visuals in a report are usually based on a single dataset. Reports allow you to arrange visuals to tell a story or explore different aspects of your data (for example, a sales report might have separate pages for revenue, customer demographics, and product performance).

• Dashboard: A dashboard is a single-page snapshot of your most important visuals, usually created in the Power BI Service (the online platform). It is often described as a canvas that contains a curated set of visuals (tiles) from one or more reports[4]. Dashboards are designed to highlight key metrics at a glance – think of it like the dashboard of a car, which shows crucial information in one view. In Power BI, a dashboard is usually what you share with others to let them monitor the business insights quickly. Important: A dashboard fits on one page (no scrolling); if you have more to show, that belongs in a report’s additional pages.

• Tile: A tile is an individual visual element displayed on a report or dashboard. It’s the boxed unit of a single visualization. On dashboards, each chart, gauge, or number you see is a tile that was pinned from a report[4]. Tiles can be moved and resized on the dashboard canvas to create an appealing layout. For example, a card showing “Total Sales = $1M” is one tile, and a bar chart next to it is another tile.

These building blocks (datasets, visuals, reports, dashboards, tiles) are the core pieces you will work with in Power BI[5][6]. Essentially, you connect to data to create a dataset, use Power BI Desktop to design a report with multiple visuals, and then pin visuals from reports as tiles on a dashboard (in the Service) for sharing.

💻 Power BI Desktop (Authoring Tool)

Power BI Desktop is the Windows application for data preparation and report creation[1]. It’s the tool where you will spend time connecting to data and designing your data model and visuals. Key aspects of Power BI Desktop include:

• Data Connectivity: Desktop can connect to numerous data sources: Excel files, CSVs, databases (SQL Server, Oracle, etc.), cloud services (Azure, Salesforce, SharePoint, etc.), and many more[1]. You can load multiple tables from different sources into one dataset.

• Data Transformation (Power Query): Power BI Desktop includes Power Query Editor for cleaning and transforming data. This allows you to shape data without writing code – you can filter rows, split columns, change data types, create calculations, etc. (all transformations are recorded as steps). Power Query uses the “M” formula language under the hood, but you don’t need to code in M for most tasks; the interface lets you apply transformations with clicks. This step is crucial to prepare raw data for analysis[1] (for example, you might remove duplicates or create a “Month” column from a date).

• Data Modeling: In Desktop, you can define relationships between tables (if you import multiple tables). For instance, you might relate a Sales table to a Product table by a ProductID. You can also create calculated columns or measures using DAX (Data Analysis Expressions) – a formula language for advanced calculations (e.g., year-to-date sums, growth percentages). Basic modeling ensures your visuals can slice and aggregate data correctly (this topic may be explored in later lessons).

• Creating Visualizations and Reports: Power BI Desktop provides a drag-and-drop canvas to create interactive visualizations like charts, maps, and slicers. You can add multiple pages to a report, each with its own set of visuals. All visuals on a page are interactive by default – if you click on one chart, it can highlight or filter the others (more on this interactivity below)[2]. You can also add visual elements like text boxes, shapes, and images to make the report clearer or branded.

• Interactive Filtering & Drill-down: While building a report, you can enable features like drill-down (e.g., clicking on a year in a chart to see quarterly or monthly details) and use slicers/filters. Slicers are special visuals (like drop-downs or checkboxes) that let users filter the report by criteria (for example, a slicer on “Region” to filter all visuals to a selected region). These make the report an interactive experience for the end-user.

• Publishing Reports: Once your report looks good, Power BI Desktop lets you publish the report to the Power BI Service (if you have an account set up). With one click, the .pbix file (Power BI Desktop file) is uploaded to the cloud, where you and others can view it[1]. This is how you share your work with colleagues or your team after building it in Desktop.

📝 Note: Power BI Desktop is free to download and use. Beginners typically start here – importing a sample dataset (like an Excel file) and creating a simple report with a few visuals. No programming is required to use Desktop; it’s designed with a friendly interface for dragging fields onto charts and building visuals.

☁ Power BI Service (Cloud Platform)

The Power BI Service (accessible via your web browser at powerbi.com) is the online platform for Power BI. Think of it as the hub where you store, view, and share your dashboards and reports. Key features of the Service include:

• Dashboards & Reports in the Cloud: When you publish reports from Desktop, they reside in the Power BI Service. Here you can create dashboards by pinning visuals from those reports. A dashboard can mix visuals from different reports or datasets if needed. Dashboards are useful for a high-level view, and each tile on the dashboard links back to the full report for detail. In the Service, you can also open reports in a web editor to make minor changes or create new reports from existing datasets (if you have permission).

• Sharing and Collaboration: Power BI Service enables you to share content with others. You can share a dashboard or report directly with coworkers (they get an email invite or can see it in Power BI if they have access)[1]. You can also create workspaces – collaborative areas where a team can collectively work on a set of reports/dashboards. Within workspaces or your organization, you can control access using role-based security (so people only see data they should). Collaboration also extends to integration: you can discuss reports in Microsoft Teams or embed dashboards in SharePoint or other apps easily[1].

• Scheduled Refresh: If your dataset is connected to an external data source (like a database or an online service), the Service allows you to schedule automatic data refreshes. For example, you can set a dataset to refresh every day at 8 AM so that the latest data is loaded into the report before the workday starts. This ensures your dashboards are always up-to-date without manual intervention[1]. Power BI can connect live to some data sources (like Azure SQL or Salesforce) for near real-time updates, and it supports streaming data sets for real-time dashboards [1].

• AI-Powered Insights: The Service incorporates some AI features to help users find insights. One popular feature is Q&A, which is a natural language query tool – you can literally ask a question in a text box (for example, “What were our sales this month by product?”) and Power BI will attempt to generate the answer in the form of a visual or chart[1]. This uses natural language processing to interpret your question and pull data from the dataset. The Service also has a feature called Quick Insights that can automatically scan a dataset for interesting patterns or outliers (like identifying a sudden spike in sales for a region). These AI features lower the barrier for non-technical users to engage with the data.

• Data Alerts: On dashboards, you can set alerts on tiles (for gauge, KPI, or card visuals) if the data meets a condition. For example, an alert can email you if a sales KPI falls below a threshold. This is a simple AI-driven feature to keep you proactive about changes.

• Security and Governance: For completeness, note that the Service manages authentication and data security. Features like row-level security can be applied to datasets (so that, e.g., each regional manager sees only their region’s data when viewing the same report)[1]. The Service is also where administrators manage permissions, usage monitoring, and deployment of apps (packaged collections of dashboards/reports for end users). As a beginner, you mainly need to know that sharing requires the appropriate Power BI license and careful setup by your admin if dealing with sensitive data.

In summary, Power BI Service is your dashboard delivery platform – you use it to view what you created in Desktop, to share with others, and to keep data updated. It turns static reports into live, updated dashboards accessible from anywhere.

📱 Power BI Mobile (On-the-go Access)

Power BI also provides mobile apps (for iOS and Android, and also available for Windows tablets) so that you can interact with your data on phones or tablets. Power BI Mobile lets users view live dashboards and reports on the go[3]. Key points about the mobile component:

• The mobile app is primarily a viewing tool. Users log in with their Power BI accounts and can see the dashboards and reports that have been shared with them, in a view optimized for smaller screens.

• Interactive Touch Experience: The mobile apps support the same interactive visuals – you can tap on a chart to highlight data, use slicers, and even use the Q&A feature by voice or text to ask questions about the data. The interactivity is similar, just adapted to touch input.

• Real-Time Alerts: Mobile devices can receive push notifications from Power BI. For example, if a data alert (set in the Service) is triggered – say sales drop below a target – the Power BI mobile app can notify your phone immediately[3]. This way, decision-makers can be alerted to important changes even when away from their desks.

• Access Anywhere: With the mobile app, dashboards are truly portable. A regional manager traveling can pull out their phone and see the latest metrics. The data can come from cloud or on-premises sources and still be securely accessed via the app[3] (on-prem data would route through a secure gateway). The apps also offer an offline mode for cases where you need to view a snapshot of a dashboard without internet connection (you must have accessed it earlier for the data to cache)[3].

• Ease of Use: The mobile interface is simplified – for example, dashboards might be viewed in a phone layout (authors can design a special phone-optimized layout for each dashboard). You can favorite frequent dashboards for quick access. The design ensures even on a small screen you can read charts and numbers clearly, with the ability to zoom or rotate as needed.

In short, Power BI Mobile ensures that the insights from your dashboards are available anytime, anywhere. It is especially useful for executives or field teams who need to monitor numbers on the fly. As a beginner, once you publish a report and create a dashboard in the Service, you can easily access it on your phone by installing the Power BI app and signing in – no extra work needed.

🔄 Interactive & Real-Time Features

One of the strengths of Power BI is the ability to interact with your data directly in the visuals, as well as see data update in (near) real-time:

• Interactive Filtering & Cross-Highlighting: Power BI reports are highly interactive by default. If you select a data point on one visual (for example, click on a bar in a bar chart), other visuals on the same report page respond — related data points highlight or filter in those visuals[2]. For instance, clicking an “East Region” segment in a pie chart could filter a line chart and show only data for the East region. Similarly, using a slicer (like a year or product dropdown) will filter all charts on the page to the selected value[2]. This interactive filtering allows users to explore the data dynamically, uncovering insights by focusing on subsets of data without writing queries. You can always clear selections to return to the full view. (Report designers can control how visuals interact or disable an interaction if it’s not needed, but by default everything is interactive.)

• Drill-Down and Drill-Through: Related to interactivity, Power BI supports drilling down into hierarchies. For example, a bar chart might show Sales by Year; you can double-click a year bar to drill down to Sales by Quarter for that year, and further down to Months. This is set up by designers using hierarchies in the data (e.g., Date hierarchy). Drill-through is another feature where you can right-click on a data point and go to a detailed page filtered for that context (e.g., from a high-level sales dashboard to a page with detailed transactions for that region). These features help in data exploration, letting the user navigate between summary and detail easily.

• Real-Time Dashboards: Power BI can display real-time data if configured with streaming data sources. For example, you might have a dashboard showing sensor readings or live website traffic. Through streaming datasets or direct query connections, visuals on a dashboard can update automatically with new data without manual refresh. A common scenario is a warehouse dashboard with live updates of inventory or a social media sentiment dashboard updating every few seconds. Setting up real-time dashboards is a bit more advanced (involves using the Power BI API, streaming analytics, or pushing data), but know that the platform supports it[1]. For most beginners, real-time might simply mean using DirectQuery connections (where visuals query the database on-the-fly when you slice or refresh, so you always see current data) or scheduling frequent data refreshes.

• Scheduled Data Refresh: As noted earlier, for data that isn’t live-streamed, you can schedule refreshes in the Service. This is crucial for keeping your data current. Power BI Free allows daily refresh (up to a certain number), while Power BI Pro allows more frequent (up to 8/day) and Premium even more. You set the schedule (say, every morning at 9am and every hour until 5pm) and Power BI will import new data from your sources at those times[1]. If your data source is on-premises (like a local database), you’d use a Gateway application to facilitate the refresh securely. The result is automated updates – your team always sees fresh data on the dashboard. If something goes wrong (e.g., a data source is unreachable), the Service will send you an alert about a failed refresh.

• Data Alerts & Notifications: Dashboards can be set to monitor certain thresholds. If a KPI goes beyond a limit, Power BI can email the user or ping the mobile app. This isn’t exactly “real-time analytics” but helps users respond in real-time to the information. For example, if inventory falls below X units, or if daily sales exceed a goal, you can get notified immediately.

All these interactive and real-time capabilities help make Power BI reports and dashboards living tools rather than static charts. Users are encouraged to click, filter, and drill to answer their own questions. And with data refresh automation, the content stays relevant without manual effort.

🤖 AI-Powered Insights in Power BI

Power BI incorporates AI features that can aid in data analysis, even for beginners who might not know advanced analytics techniques:

• Q&A (Natural Language Queries): On Power BI dashboards (and reports in reading mode), there’s a Q&A bar where you can type a question in plain English (or other supported languages). For example, you could ask “Total sales by category for 2023”. Power BI’s Q&A uses natural language processing to interpret your question and will generate a visual (maybe a bar chart by category) to answer it, if the data is available[1]. It is surprisingly effective for straightforward questions based on fields in your dataset. This means even a user who doesn’t know how to slice the data themselves can just ask, and often get a quick insight. Report designers can improve Q&A by adding synonyms (so the engine knows, for instance, that “revenue” corresponds to the “sales” field).

• Quick Insights: In the Power BI Service, you can run Quick Insights on a dataset. Power BI will apply some machine learning algorithms to detect anomalies or trends. For example, it might find that a particular segment had an unusually high value one month, or that two variables have a correlation. It then presents these findings as auto-generated visuals with brief descriptions. This can surface things you might not have looked for manually. It’s a great way for beginners to leverage AI to see “what stands out” in their data.

• Decomposition Tree & Key Influencers (AI Visuals): Power BI offers special visualizations like the Key Influencers chart, which uses AI to analyze what factors most impact a particular metric (for instance, what factors most influence customer satisfaction score). There’s also a Decomposition Tree visual that let users break down a metric by different dimensions, with the ability to have AI find the next best split. These visuals provide a guided analysis experience and are part of Power BI’s built-in visuals.

• Smart Narratives: Another feature is the Smart Narrative, which can automatically generate a natural language summary of visuals or dashboard stats. For example, it can output a sentence like “Total sales increased 5% compared to last month, with Product A having the highest growth.” This uses AI to turn data points into text commentary, helping users interpret dashboards.

• Integration with Azure AI: For advanced scenarios, Power BI can integrate with Azure cognitive services and AI models. For instance, you can create a query that calls a sentiment analysis on text data, or perform image detection as part of data flow. This is beyond a beginner scope, but it shows that as you grow with Power BI, you can bring in sophisticated AI for custom analysis.

In a nutshell, AI-powered features in Power BI make it easier to get insights without manual slicing and dicing. They are like having a little data analyst inside the tool, prompting you with interesting findings or answering your questions. As a beginner, try the Q&A feature on a sample dashboard – it’s an intuitive way to engage with your data using simple questions.

📝 Dashboard Design Tips (Best Practices)

Creating an effective dashboard isn’t just about putting all the charts on one page. It requires some thought to ensure it’s clear, focused, and helpful to the audience. Here are a few best practices for designing Power BI dashboards:

• Focus on Key Insights: Keep your dashboard focused on the most important metrics. Because a dashboard is a one-page overview, avoid cluttering it with too many visuals. It’s best if everything fits in one screen without scrolling[5]. Remove any visual that isn’t essential to the story you’re telling. Ask yourself: “If I were the viewer, what 3-5 things would I really need to see right away?” and prioritize those.

• Use Clear Visuals: Choose the right type of visualization for your data, favoring clarity over novelty. For example, use bar or column charts for comparisons rather than 3D pie charts, which can be hard to read accurately[5]. Keep colors consistent (e.g., if “West Region” is blue in one chart, use the same blue for West in all charts) to help the viewer make associations[5]. Avoid too many different chart types on one dashboard – consistency makes it look professional and easy to interpret.

• Highlight Important Numbers: If a number or KPI is crucial, make it stand out. Use a Card visual or KPI visual for single important figures (like total sales, # of customers, etc.) and place it prominently (top-left is often where eyes go first)[5]. You might use larger font or a distinct color for this key number. The goal is that a viewer glancing at the dashboard immediately catches the top one or two takeaways.

• Consider Your Audience (and Device): Design with the end-user in mind[5]. Are they executives who just want high-level numbers, or analysts who might click through to details? Tailor the content accordingly. Also consider where they will view it: if many will use phones or small screens, use fewer, bigger tiles (and take advantage of Power BI’s phone layout feature)[5]. If it’s mostly on widescreen monitors, you can safely include more detail. Always try to view the dashboard as if you’re the audience: is it understandable at a glance?

• Tell a Coherent Story: Arrange visuals in a logical order. Perhaps group related metrics together. Use text boxes or headings on the dashboard to provide context if needed (e.g., a title or a short description at the top). A viewer should grasp the “story” – for example: “Sales are trending up this year (see line chart), our top product is X (see bar chart), and we are above our target (see KPI card).” If any visual’s purpose isn’t clear, consider adding a title or label, or remove it.

• Test Interactions: If your dashboard relies on the user clicking something (for example, a map that filters other visuals when a region is clicked), make sure that’s obvious or intuitive. You might need to provide a note or design the report page (since dashboards themselves are just view mode) such that the interaction is easy. Remember, dashboard tiles themselves don’t cross-filter each other (interactivity works fully in Reports). For interactive exploration, users will click a tile to go to the report. So ensure your underlying report is well-structured for analysis too.

• Use Consistent Design: Maintain consistency in formatting – use the same font style and size for similar elements, a consistent color palette that aligns with any organizational brand if applicable, and uniform sizing of charts if possible. This makes the dashboard look clean and professional.

By following these tips, your dashboard will be more readable and impactful[5]. The aim is to enable viewers to get the information they need quickly and clearly. Remember, often less is more – it’s better to have a few great visuals than a dozen confusing ones.

💡 Example: Using Power BI Components Together

To illustrate how these components come together, let’s walk through a simple scenario:

Example: A finance analyst wants to track daily sales performance. Using Power BI Desktop, the analyst connects to two data sources: an Excel file with this year’s sales transactions and a CSV export of the sales targets. They use Power Query to clean the data (for example, formatting dates and removing canceled orders) and create a relationship between the sales data and targets by date. Next, the analyst builds a report with a line chart showing Actual vs. Target sales by day, a card visual showing Total Sales to Date, and a bar chart of sales by product category. After fine-tuning the visuals, they publish the report to Power BI Service[1].

In the Service, the analyst creates a dashboard called “Daily Sales Dashboard”. They pin the total sales card and the line chart to this dashboard. The dashboard now gives a one-page view of key metrics: today’s sales (on the card) against the target line. The analyst shares the dashboard with the sales manager and sets a data alert on the card visual to notify if sales go below a certain threshold (ensuring the manager gets an email if something’s off).

The sales manager, on their side, opens the dashboard in the morning via the Power BI Mobile app on their phone while commuting[3]. They see the total sales number and the trend line immediately. If they want to investigate more, they tap the line chart tile which opens the full report, where they can interact — for example, filter to see only a specific product category’s trend. Because the data is scheduled to refresh every hour from the Excel and CSV (which are updated by an automated process), the manager is always looking at near real-time numbers without doing anything manually[1].

This example highlights how Power BI components work together: Desktop for creation, Service for dashboard and sharing, Mobile for on-the-go access, dataset connections for bringing in data, visuals (card, line, bar) to communicate information, and features like scheduled refresh and alerts to keep things up-to-date and relevant. All of this empowers both the analyst and the manager to stay on top of daily sales in an efficient way.

📘 Scenario: From Data to Dashboard (Beginner’s Journey)

Let’s consider a relatable scenario for a beginner using Power BI:

Scenario: Meet Carlos, a marketing specialist at a retail company. Carlos has data in different places – an Excel file with last quarter’s web traffic and an online database that stores sales leads. He wants to create a dashboard to share with his team, showing how website visits are converting into sales leads, and to do it without writing code.

• Getting Data: Carlos opens Power BI Desktop and uses the Excel connector to import the web traffic data, and the SQL Database connector to import the leads data (Power BI has built-in connectors for both Excel and SQL)[1]. In Power Query Editor, he notices the date formats differ between the two sources, so he transforms the date columns to a standard format and merges the datasets on the date field, essentially creating a single dataset he can use.

• Creating the Report: Carlos drags and drops fields to create visuals: a line chart for Website Visitors over time, a line chart for Leads over time, and a stacked bar chart that shows leads by source (organic search, ads, etc.). He adds a slicer for Region so that team members can filter the report to their region. He also uses Power BI’s formatting options to make the charts match the company’s color theme. On one page, he has the “Web Traffic & Leads Report” with these visuals arranged clearly.

• Interactivity: He verifies that when he selects a specific region in the slicer or clicks on a particular month in the visitors chart, all visuals update (the leads chart filters to that region or month) – this interactive filtering will allow his teammates to explore the data easily during team meetings[2].

• Publishing and Dashboard: After double-checking everything, Carlos publishes the report to the Power BI Service under a workspace that his marketing team uses. Next, he creates a dashboard titled “Marketing Overview”. He pins the key visuals: the website traffic line chart and the leads line chart (since those two together show the conversion trend). He also pins a card visual that he created in the report showing the total leads this quarter. Now on the dashboard, he arranges these three tiles in a logical order and adds a title textbox.

• Sharing: Carlos uses the Share feature to share the “Marketing Overview” dashboard with his team, including his manager. He gives them view access. The team members receive an email with a link. Because everyone has a Power BI Pro license in his organization, they can all view the dashboard on the Service and on their phones.

• Using the Dashboard: In the next team meeting, they project the dashboard on the screen. The dashboards’s tiles are live – as of that morning, showing the latest data (Carlos had set up a daily refresh schedule). One teammate asks about a particular dip in leads last month. With a click, Carlos opens the report via the dashboard and uses the drill-through feature to jump to a detailed page he prepared, showing lead details for that month. They identify that a form on the website was down for a day, causing the dip. This insight is captured, and after the meeting, Carlos adds a text box note on the dashboard about that explanation for everyone’s context.

• Outcome: Over the quarter, this dashboard becomes the go-to place for the marketing team to check their performance. Team members can even use Q&A on the dashboard to ask things like “show leads by campaign” and get quick visual answers without needing Carlos every time[1]. Carlos has effectively used Power BI to connect multiple data sources, create an interactive report, and deliver a sharable dashboard – all as someone who is a beginner at Power BI.

This scenario shows that with Power BI’s components, a beginner like Carlos can quickly become productive: connecting data, building visuals, and sharing a live dashboard that replaces manual spreadsheets and slides. Each component – dataset (web & leads data), report (the multi-visual page Carlos built), dashboard (the one-page summary), and the platforms (Desktop to create, Service to share, Mobile/Meeting to consume) – plays a role in this workflow.

🔁 Lesson Recap: Power BI Components

In this lesson, you learned that:

✅ Power BI’s fundamental components are its building blocks: visualizations, datasets, reports, dashboards, and tiles[4][4]. Visualizations are individual charts or graphs; datasets are the data behind the scenes; reports organize visuals over multiple pages; dashboards are single-page collections of key visuals (tiles) for quick insights.

✅ Power BI Desktop is the application used to connect to data sources, transform and model the data, and create interactive reports[1]. It allows you to prepare your data with Power Query and design visuals and pages. Power BI Service is the cloud platform where those reports are published for viewing and sharing as dashboards[1]. In the Service, you can set up scheduled refreshes to keep data current[1], and leverage features like sharing, security, and Q&A. Power BI Mobile apps enable users to access dashboards and reports on mobile devices, ensuring data is available on the go[3].

✅ Interactivity and real-time data are key advantages of Power BI. All report visuals are interactive by default, enabling cross-filtering and drill-downs with simple clicks[2]. Dashboards can display up-to-date information, either through frequent scheduled refresh or live connections for real-time updates[1]. This means decision-makers can always see the latest data and explore it dynamically.

✅ AI-powered features in Power BI (like the Q&A natural language query and Quick Insights) help users discover insights easily[1]. Even without advanced analytical skills, users can ask questions of their data or let Power BI detect notable trends and outliers. These smart features make data exploration more accessible to beginners.

✅ To get started as a beginner: you can import a simple dataset (e.g., an Excel file) into Power BI Desktop, create a few visuals on a report page, and publish it to the Power BI Service to share as a dashboard. This end-to-end process – from raw data to a sharable dashboard – can often be done in minutes, demonstrating the power of Power BI’s components working together to deliver insights from data.