Creating dashboards in Power BI

Last updated: 11. 05. 2026

Tips for Clear and Interactive Reports

In data-driven companies, well-designed dashboards are key to fast and high-quality decision-making. A Power BI dashboard can transform vast amounts of data into a clear and actionable view that immediately shows managers what to focus on. In this article, we’ll focus on best practices for creating clear and interactive reports in Power BI. Our advice is aimed primarily at technical managers with experience in Microsoft Azure and the Power Platform who want to design effective BI solutions. You’ll learn why clear dashboards are important, what data visualization principles to follow, how to leverage Power BI’s interactivity, how the dashboard fits into the broader data architecture (Azure SQL, Data Lake, Azure Data Factory, Microsoft Fabric), how to properly publish and share it, and finally, we’ll include practical tips from Data Mind projects.

The Importance of Clear Dashboards for Decision-Making

A clear dashboard isn’t just a pretty picture—it’s primarily a tool for informed decision-making. Companies that leverage data achieve better results in terms of profit and growth. A well-designed report allows managers to quickly understand the company’s performance and respond to current developments. The main benefits include:

• Data-driven decisions: With deeper insights into the data, managers can make decisions based on facts rather than guesswork. The updated dashboard serves as a single source of truth and eliminates guesswork during meetings.

• Real-time tracking of goals and KPIs: The dashboard makes it easy to monitor key performance indicators (KPIs) on a daily basis. If any metric deviates from the set targets, you’ll see it immediately and can take timely action. This eliminates the need to wait for monthly reports—you can respond to problems or opportunities right away.

• The big picture and the details: Managers need an overview of the business, but they also need the ability to drill down into the details. A clear dashboard provides aggregate figures for the entire company as well as a breakdown by product, division, or region, so performance can be managed at all levels.

• Early identification of trends and anomalies: The dashboard helps identify shifting market trends, changes in customer behavior, or new opportunities before they impact financial results. This allows you to adapt your strategy flexibly. It also alerts you to anomalies and errors in the process—built-in alerts can immediately flag a problem, whether in production or in a digital campaign, allowing you to take immediate action.

In short, high-quality dashboards in Power BI deliver the right data to the right people at the right time, which is essential for modern business management. Clear charts and tables are just as important to a business as healthy cash flow—they provide an up-to-date picture of the company’s performance in near real time.

Principles of Good Data Visualization

For a dashboard to be truly useful, it must be visually intuitive. When designing a report, follow these principles of data visualization:

• Choose the right chart type: Every metric deserves the right visualization. When comparing values across categories, use bar or line charts instead of complicated 3D or pie charts. For example, revenue trends over time belong in a line chart, while product shares of the total can be represented by a pie chart (but only if there are few categories, ideally fewer than 6–8). Don’t overload the visual with unnecessary effects—spatial 3D charts or an excess of exotic visualizations generally reduce readability.

• Minimalism and clarity: Follow the motto “less is more.” Limit distracting elements such as unnecessary grids, shadows, or decorations. Every element on the screen should have a clear purpose. Remove unnecessary labels if the meaning is clear from the graph’s axes. At the same time, ensure there is enough white space—visuals must not be crammed onto the page without gaps. Arrange elements so that the most important information stands out. Remember that most people read content from top to bottom and left to right—so place key metrics at the top left, and more detailed data toward the right or bottom. Ideally, all important content should fit on a single screen without scrolling.

• Clear labels and context: Give each chart a descriptive title and, if necessary, a brief caption so that the user can understand at a glance what it shows. Put numerical values into context—for example, display the percentage of the plan achieved or a year-over-year comparison next to the number, rather than letting the number stand alone. When presenting absolute numbers in millions or thousands, use abbreviated notation (e.g., 3.4 million instead of 3,400,000) for better readability.

• Consistent design: Maintain a unified visual identity. Use consistent colors throughout the report—for example, the company’s color palette and the same colors for the same categories in all charts. Similarly, maintain a consistent scale for axes when visualizations compare similar data. Choose a font that is easy to read, with consistent sizes for headings and labels. The user should focus on the data, not on stylistic differences.

By following these principles, you can ensure that your visualizations are easy for your audience to understand and do not distort the story being told by the data. The goal is for the dashboard to convey its main message within a few seconds of viewing, without the need to decipher what each chart means.

Interactivity of dashboards in Power BI

One of Power BI’s greatest strengths is its interactivity. It allows users to explore data through their own clicks and selections, rather than passively viewing static charts. Key interactive features include:

• Filters and slicers: Every report should allow users to filter data by various dimensions (time, product, region, etc.). Interactive slicers in Power BI allow users to intuitively limit the displayed data to only what interests them (e.g., select a specific year or department). Unlike fixed, static reports, this gives each user a customized view.Filters and slicers: Every report should allow users to filter data by various dimensions (time, product, region, etc.). Interactive slicers in Power BI allow users to intuitively limit the displayed data to only what interests them (e.g., select a specific year or department). Unlike fixed, static reports, this gives each user a customized view.

• Drill-through: The drill-through feature allows you to navigate from a dashboard overview page to a more detailed report page with a filter applied to the selected value. In practice, this works by the user, for example, right-clicking on a specific product or customer and selecting "Drill-through." Power BI will take them to a detailed report (e.g., a page with detailed statistics for that product) and automatically pass the filter context. This combines the advantage of a concise overview with the ability to look up detailed explanations of trends without having to leave the dashboard.

• Drill-down and hierarchies: In addition to navigating to a different page, you can also use hierarchical breakdowns within a single visualization. For example, a bar chart of revenue might have a hierarchy of Year > Quarter > Month. Users can then click to drill down to a lower level of detail or return to the previous level. This functionality again supports the discovery of causes and details without cluttering the main view.

• Bookmarks and buttons: Power BI allows you to create bookmarks—saved views of a report—that can be linked to buttons. Using bookmarks, you can switch between different states of the report (e.g., switch between different scenarios, views, or language versions) with a single click. Bookmarks can also be used to create simple navigation within a report or to highlight a specific data segment using a button such as "Highlight Anomalies," etc.

• Cross-filtering and highlighting: All visuals in Power BI are interconnected—clicking on a data point in one chart automatically filters and highlights relevant data in the other charts. For example, if you click on the “Product A” segment in a pie chart, the other charts on the page will be filtered to show only Product A. This allows users to easily see connections across different metrics.

Interactivity transforms a passive report into a dynamic analytical tool. Users can drill down into the data as needed, find answers to specific questions, and explore "what-if" scenarios—for example, using the What-If parameter for interactive hypothesis modeling. For technical managers, this means that a single well-designed report can cover both an overview for management and detailed data for analysts—all in one solution.

Solution Architecture and the Role of the Dashboard in the Data Platform

A successful dashboard doesn’t stand alone—it’s part of a broader data architecture and corporate BI strategy. Before creating the visuals themselves, it’s essential to have a solid “backend” for the data solution:

• Data Sources and Integration: High-quality input data is essential. This data can come from various systems (ERP, CRM, databases, IoT sensors, etc.). To consolidate and clean this data, integration tools such as Azure Data Factory or Power BI data flows are typically used. These tools regularly retrieve data from sources and store it in a central repository.

• Data storage (Data Lake, database, data warehouse): A Data Lake or data warehouse is used to store large volumes of data and historical records. In the Azure environment, Azure Data Lake Storage is often used for raw data, while Azure SQL Database or Azure Synapse Analytics (SQL data warehouse) is used for structured, aggregated data. Microsoft’s modern approach, the Microsoft Fabric platform, even integrates the data lake, data warehouse, and Power BI into a single environment for easier management. It is crucial to have a single source of truth—well-designed central data models and tables from which reports will draw. If every department uses different numbers or metric definitions, the dashboard loses credibility. That is why Data Mind emphasizes data quality and consistent metric definitions across the company.

• Data Model and Analytics Layer: In Power BI (or Azure Analysis Services), a semantic model is built on top of the data store—for example, in the form of a star schema (fact and dimension tables). A well-designed data model is the foundation for fast and accurate reporting. Optimizing the data model (e.g., using appropriate relationships, aggregations, and calculated measures) ensures that users won’t have to wait for pages to load and that visualizations won’t stutter. As our experience and Microsoft’s best practices show, when leveraging the unlimited capacity of the Azure cloud with a well-designed model, reports will never be slow.

• Operations, Management, and Security: The dashboard is the tip of the pyramid—beneath it, you need to ensure data access management, security, and solution scalability. Azure provides top-tier security and reliability for data storage. By combining Power BI with Azure, performance and capacity can be easily scaled—as the company grows, the data platform grows with it and never hits a hard limit. Access rights management is also crucial: row-level security (RLS) can be configured directly in the data warehouse or model so that each user sees only the data they are authorized to view (e.g., only their region or department). This ensures both data democratization (every employee has access to information relevant to their work) and data security—“everyone sees what they’re supposed to see, and nothing more.” In addition, organizations should designate data owners and curators for individual data domains to prevent uncontrolled duplication of reports and metrics.

From an architectural perspective, creating a dashboard in Power BI also involves close collaboration with data engineers and administrators. The dashboard itself is merely the final link in the chain: it builds upon a well-executed ETL integration, a high-quality data model, and established data management processes. At Data Mind, we ensure that our BI solutions fit seamlessly into the client’s existing infrastructure—for example, we use Azure services (Azure SQL, Data Factory, Data Lake) for a robust backend and Power BI or Microsoft Fabric for convenient visualization and distribution of information to users.

Publishing and Sharing Reports in Power BI

Creating a good report isn’t the end of the process—you need to get it to the right users and maintain it. Power BI offers several options for publishing and sharing your finished dashboard within your organization:

• Power BI Service (cloud): Most interactive reports are published to the Power BI Service cloud service. Report authors upload a .pbix file (report with a data model) to a workspace in the cloud. From there, the report can be shared with specific users or groups (based on Azure Active Directory) or integrated into the Power BI app (a suite of reports and dashboards for end users). Power BI Service ensures that users always see the latest version of the dashboard. You can set up scheduled updates for the dataset (e.g., hourly or daily) or enable real-time data ingestion from streaming sources.

• Security and Access Rights: When sharing reports, it is essential to restrict access to authorized users only. Power BI allows you to define roles (e.g., Executive Role, Regional Manager Role, etc.) and assign users to roles that have different permissions and filter visible data (see the aforementioned RLS – Row-Level Security). Thanks to integration with Azure AD, you can leverage existing user groups and single sign-on credentials. Data Mind, for example, recommends that clients share sensitive financial reports only with members of senior management and use two-factor authentication for access, thereby increasing security. In addition, it is advisable to use quality labels in Power BI (certification or promotion of reports) to distinguish official reports from working versions—this allows users to easily verify the credibility of the content.

• On-premises and alternatives: If a company cannot or does not want to store data in the cloud, Power BI Report Server is available for on-premises deployment of reports within the internal network. However, it does not include all cloud features or mobile access. The Microsoft Power Platform environment also offers integrations—such as embedding a dashboard in Teams for easy access, or using Power BI Embedded to integrate reports into your own applications.

• Version and Development Management: For larger projects, we recommend implementing procedures for managing report versions and deployments. You can use multiple workspaces (development, testing, production) and tools such as Azure DevOps or Git for versioning .pbix files. This ensures that published reports have undergone quality control and approval. Power BI also enables commenting and sending alerts—users can set up data alerts in the Power BI Service that notify them via email or message if a metric exceeds a specified threshold. This significantly increases the proactive use of the dashboard in daily operations.

Proper report publishing therefore ensures not only that the dashboard reaches users, but also that it is delivered securely, with up-to-date data and clear context. Managers can then share consistent figures across teams and devote their time to interpreting the data rather than gathering and verifying it.

Practical Tips from Data Mind Projects

In conclusion, here are a few insights and recommendations that have proven effective in practice when implementing BI dashboards for management:

• Start with a decision-making question: Every dashboard should have a clear purpose—what decision or question it is meant to help answer. We’ve seen that reports full of effects and visuals can look impressive, but if they lack focus, managers will ultimately make decisions based on gut feeling anyway. That’s why you should define right from the start which KPIs and goals the dashboard should track and how management will act based on them. Tailor your data selection and report design accordingly.

• Less is sometimes more (beware of information overload): Management dashboards work best when they highlight just a few key metrics—typically the 3–5 most important KPIs on the home page. If you try to display dozens of charts and numbers at once, the important information will get lost in the noise. Too many filters, visuals, and dozens of indicators will cause cognitive overload for the user. Therefore, keep the report as simple as possible and guide the user to key findings, rather than overwhelming them with all the data at once. You can hide detailed information on additional pages or in drill-throughs, outside the main dashboard area.

• Visualizing Predictive Models: In today’s world of advanced analytics, we often integrate outputs from AI/ML models into reports—such as demand forecasts, customer scores, risk estimates, and the like. In our experience, the best approach is to visualize predictions alongside actual values and add explanations. For example, we display predicted demand as a line extending beyond the current period, ideally with a confidence interval (band of uncertainty), so that management can see both the trend and the degree of certainty of the prediction. For classification models (e.g., customer churn prediction), it has proven effective to include a list of top-risk customers or an aggregate metric (e.g., expected quarterly churn) in the dashboard, accompanied by a recommended action. It is important that model outputs are presented clearly—for example, instead of a technical description of the model, show specific recommendations or a “what-if” scenario. Power BI can retrieve machine learning results from a database or through integration with Azure Machine Learning, and at Data Mind, we ensure that this advanced information is presented in the report in a way that even business users can understand (e.g., in the form of a simple chart or an indicator with a verbal description).

• Common mistakes to avoid: We see recurring mistakes in BI report design across projects. One of them is poor data quality—a beautiful dashboard built on unreliable data can do more harm than good. It is essential to have well-maintained data sources and metric definitions in place before you start visualizing. Another mistake is unstructured “self-service” reporting without governance—if every team creates its own reports without central rules, the result is confusion (different numbers in different reports, and no one knows what to trust). That’s why we recommend establishing clear rules and BI governance (see “Governance” above). Also, watch out for slow reports—often caused by overly complex queries or models. The solution is to optimize queries, pre-aggregate data, or use more powerful backends (e.g., materialized views in SQL or aggregation tables in Power BI). And finally, don’t forget the users—even the best dashboard is useless if the target users aren’t trained on how to use it or aren’t even aware of it. That’s why training and internal promotion are part of a successful implementation: show the team how to work with the dashboard and highlight the benefits.

We hope these tips will help you design and implement your own dashboards in the Microsoft Power BI environment. Let’s summarize the main idea: a great dashboard is created by combining a well-thought-out data architecture, clean visualization design, and interactive elements tailored to specific business needs. By combining the capabilities of Power BI, Azure SQL, Azure Data Factory, and the entire Power Platform (or the modern Microsoft Fabric environment), you can create a comprehensive BI solution that provides your managers with a reliable compass for strategic decisions in the digital age.