Dashboards serve critical role in providing real time insights into an organization’s performance. They’re designed to monitor key metrics, track progress toward goals, and facilitate data driven decision making.

Reports on the other hand are comprehensive documents designed to convey in depth information, detailed analysis, and insights. Reports provide comprehensive analysis, communicate complex information, and provide in depth exploration.

Differences between Dashboards and Reports

Process of creating a Dashboard/Report

Creating a dashboard involves several steps to design, develop, and deploy a data visualisation tool that effectively conveys information to its intended audience. The steps include;

1. Planning: Clearly define the purpose of the dashboard and identify the target audience tailor the dashboard’s content and design to their needs.

2. Data gathering and presentation: Identify data sources that’ll be used for the dashboard, ensuring that data is accurate, relevant, and up to date.

3. Visualisation: Select dashboarding tool, design the layout and choose the appropriate charts and graphs to use.

4. Interactivity: Enhance the dashboard with interactive features such as filters, slicers, etc.

5. Testing and validation: Test the dashboard for accuracy and functionality, ensure that data updates correctly, interactive features work as intended,and visuals are error-free.

6. Deployment and maintenance: Continuously monitor and maintain the dashboard to ensure it remains accurate, relevant, and functional.

Dashboarding and Reporting tools

Some common dashboarding and reporting tools include the following:

1. Microsoft Power BI: This is a versatile tool for data visualisation and analytics, suitable for businesses of all sizes.

2. Tableau: Known for its interactive and user-friendly features, Tableau is ideal for exploring and presenting data.

3. Google Data Studio: Free tool for creating interactive reports and dashboards using data from various sources.

4. Qlik View: A tool that offers robust data analytics and visualisation capabilities, with a focus on self-service analytics.

Components of a Dashboard in Power BI

1. Widgets and visualisations: There are widgets such as charts, graphs, and tables that visualise data in a meaningful way.

2. Data sources: Dashboards rely on one or more data sources, which can be databases, spreadsheets, or real-time feeds, to provide up-to-date information.

3. Filters: Dashboards can’t be filtered. However, dashboard tiles can be filtered in focus mode, but you cannot save the filter.

Components of a Report in Power BI

1. Widgets and visualisations: Like dashboards, reports feature visualisations such as charts, graphs, and tables that allow users to analyse data.

2. Multi-page documents: Reports often have numerous pages that analyse various aspects of the dataset.

3. Filters and interactivity: Reports include interactive elements like filters and slicers, allowing users to drill down into specific data subsets for deeper analysis.

Power BI as a dashboarding tool

Power BI was launched in 2014 as a business analytics tool and has evolved over the years to become more user-friendly.

It offers wide range of visualisation options, ensuring creation and sharing of detailed reports. The platform enables data professionals communicate findings effectively with features that facilitate development of insights through interactive visualisations and reports.

Power BI components

There are three pivotal components of Power BI, each delivering key features over different platform types:

• Desktop

This is the primary space for designing and publishing reports. It can connect to various data sources, both locally and on the cloud. It also offers tools for transforming and shaping data for enhanced data analysis.

• ServiceIn

A secure online service that enables data sharing and collaboration. Enable users create and view reports with interactive and dynamic visualisations. It also enables one to schedule data refreshes to keep reports up to date.

• Mobile

Offer on-the-go access to view and interact with reports on mobile devices. Moreover, you can access real-time data and insights from anywhere with the ability to also receive notifications and alerts based on data changes.

Power BI Interface

Toolbars

1. Ribbon: Just like Microsoft Word, the ribbon has variety of tabs filled with commands and tools essential for developing reports.

2. Canvas: Its like a central workspace where we develop and edit data visualisations.

3. Page tabs: This helps with navigating different pages of a report, supporting the organization and accessibility of various visualisations.

Navigation Pane

4. Filter pane: This is a dynamic tool to select which visualisation we’d like to display, to focus and tailor the data analysis

5. Visualisation pane: This allows us select and modify visual elements, allowing us customize reports to best communicate out findings.

6. Data pane: This displays the available tables, columns, and measures of the connected data sources.

Workspace

7. Report view: Here we can design and develop detailed reports, using tools and features to create comprehensive and clear visualisations.

8. Data view: Data view provides a detailed look at the report’s underlying data, with tools to inspect and refine this data.

9. Model view: This allows us to observe and manage the data model used in the reports, showing the structure and organisation of the data.

Building blocks of Power BI

1. Visualizations

These are visual representation of the data such as graphs and plots of the data, including charts and maps, providing clear and concise insights of the data.

2. Tiles

These are individual clickable elements that act as a focused view of a particular insight. A single visualisation in a report or a dashboard is a tile.

3. Reports

Report is a distinct and clear view of the dataset through visualisation, providing in-depth findings and insights.

4. Dashboards

They are used for high-level insights and monitoring, and often has less interactivity than reports.

5. Datasets

This is a collection of the data we use to create visualisations, report, and dashboards.

6. Queries

Queries are instructions or requests we use to process data. We can craft them to filter, shape and aggregate data to extract insights for visualisations.

Data management in Power BI

• Data import

Power BI facilitates importing data from a variety of sources, including databases and cloud services. It’s also compatible with different datasets for comprehensive and detailed analysis.

• Data connection

Power BI establishes and maintains a live connection to a variety of data sources to access real-time data accurately. Connecting to current and accurate data creates reliable reporting.

• Data transformation

Power BI offers tools for data cleaning, processing, and transformation. Data structures and formats are designed to meet most analytical objectives and requirements.

• Data refresh

Power BI provides scheduled or automatic data refresh mechanisms to keep data up to date. This ensures reports and control panels show the most recent information as well as supporting reliable and credible insights to make decisions from the data.

Getting data into Power BI

Since users need to be able to access their data in Power BI, they must be able to connect their data to Power BI.

Connecting to a database in Power BI involves series of steps that ensure data is extracted, transformed, and loaded into the data model.

1. Data source connection: Select the appropriate data source connector, provide the connection details, and ensure the necessary permissions.

2. Data transformation: Clean the data by removing duplicates, filtering out irrelevant data, and addressing data quality issues. Create calculated columns, and establish hierarchies if necessary.

3. Data import/DirectQuery: Decide whether to import the data into Power BI or use DirectQuery. Loading data is suitable for small to medium-sized datasets, while DirectQuery allows large datasets to remain in the source.

4. Report and Visualisation: Use Power BI Desktop to generate reports and visualisations, using the designed data model. Maintain synchronization with the source database periodically.

Importing and connecting to data

Importing data involves copying data from the source into the Power BI file. The data is stored within the Power BI model, and we can create transformations and calculated columns within Power BI.

Importing is suitable for smaller datasets or when we need to combine data from multiple sources.

DirectQuery is a method where data remains with the source, and Power BI connects to it in real time. This is beneficial for larger datasets, frequently updated data, or when data should not be duplicated within Power BI.

Power BI supports a wide range of formats such as Excel, CSV, and text files. It also offers connectors to various relational databases like SQL server and MySQL as well as NoSQL.

Since data is copied and stored within Power BI, the data will only be updated when we manually refresh the dataset or set up a scheduled refresh.

Power BI also offers connectors to online services and applications like SharePoint, Google Analytics, Salesforce, and relational and NoSQL databases.

Connecting to applications and services typically requires specific connection details such as API keys, URLs or authentication methods.

Managing data storage in Power BI

Managing data helps in optimizing report performance and ensuring data validity and accessibility. It involves the following:

• Import mode

Advantages

Suitable for relatively small to medium-sized datasets that doesn’t change frequently.

High performance, enhanced modelling, and offline access to data, allowing quick querying and visualization, and ability to create calculated columns and hierarchies even without a live connection to data.

Disadvantages

Large datasets can consume significant memory slowing down performance.

• DirectQuery mode

Advantages

Suitable for large datasets where data is frequently changing, or require real time reporting.

Doesn’t have a data storage limit, as it doesn’t consume Power BI memory.

Disadvantages

Modelling capabilities are limited since some complex calculated columns, measures, or hierarchies cannot be built in Power BI and query performance depends on the underlying data source and network speed.

• Dual mode

Advantages

Allow combination of real-time data with in-memory benefits, allowing us create composite models that combine data from multiple sources and connections.

Complex transformations and calculations can be done in import mode while real-time data remains in DirectQuery mode.

Disadvantages

Managing a dual model can be more complex and a compromise between performance and functionality must be established.

Data import issues and resolutions

Common data import issues to expect can include the following:

1. Regional settings: Data may not display correctly due to regional date and number formatting differences between the data source and Power BI.

   Resolution: Ensure Power BI’s regional settings match the data source’s locale. This includes date format, decimal points, and currency symbols.

2. Data type mismatch: Data import errors may occur when data types in the source doesn’t match the data model.

   Resolution: Use data type conversion functions to match data types, and handle errors or missing values appropriately.

3. Data quality and cleanup: Inconsistent data quality can lead to import errors and incorrect visualisations.

   Resolution: Use data transformation and cleaning techniques to address data quality issues, such as handling missing data or correcting anomalies.

4. Data volume: Large datasets and complex data models with many tables can lead to slow import times and report performance.

   Resolution: Optimize data sources by reducing the number of tables and relationships.

Publishing in Power BI

To understand publishing in Power BI, let’s first explore the differences between Power BI Desktop and Power BI Service.

Considerations when publishing in the Power BI service

When publishing in the Power BI service, the following have to be considered.

1. Data source availability: Ensure the data source used in the report are accessible and available in the Power BI service. The source credentials and permissions should also be properly configured.

2. Security and permission: Understand the data security and privacy implications of publishing to the Power BI service, considering dynamic security roles, and defining permissions for reports.

3. Version control and backups: Implement version control practices for Power BI Desktop reports to track changes and revert to previous versions.

4. Data refresh and schedule: Determine how often data should be refreshed in the Power BI service, considering the data update frequency to ensure that reports reflect the latest data.

5. Report design and layout: Optimize report layout and visuals for web browser viewing, as Power BI service may render reports differently than desktop.

6. Testing and validation: Test the report in Powe BI service to ensure visuals, interactions, and data refresh work as expected, and verify that data matches the desktop version.

Considerations when publishing to web

Publishing to web is important for sharing Power BI reports, but its important to exercise caution, especially when it comes to security. Here are the considerations;

1. Data sensitivity: Carefully evaluating the content of the report is important before publishing to web to avoid sharing sensitive, confidential, or private data.

2. Expiration: The link or embed code generated using “Publish to web“ doesn’t expire automatically, and we should therefore, manage access.

3. Limitations: Be aware of limitations of “Publish to web”. The reports are read only and some features like editing, filtering, etc may be disabled in the embedded report.

4. Data refresh: Reports published with “publish to web” may not refresh automatically. Always consider whether this limitation is acceptable for your use case.

Building and using apps in the Power BI service

Power BI apps are bundled and branded reports, dashboards, and datasets for specific audiences, to enhance user experience, streamline content sharing, and provide structured insights for organizations.

To build an app, follow these steps:

1. In the workspace list view, select “Create app” to start the process of creating and publishing an app from the workspace.

2. Customize the appearance of the app, including its name, logo, colors, and branding to help create a consistent and user-friendly experience.

3. Select the relevant content that we would like to add from the current workspace.

4. Organize the content within the app workspace to create a logical navigation structure using the Power BI app navigation to design menus, sections, and landing page.

5. Define the audience, which are multiple groups with different access to content.

6. Manage each audience’s access and add or conceal content from them in order to make it unavailable or visible in the published app.

7. Finally, publish the app and copy and share the link.

How users can interact with the app

1. They can access the app in the “Apps” section of the Power BI service or find them in the Power BI mobile app.

2. The app offers ability to explore various content such as reports, dashboards, datasets, guided by its navigation and structure.

3. Users can interact with visuals, apply filters, drill into data, and gain insights from the reports and dashboards within the app.

4. The app may include collaboration features like comments and email subscriptions. Users can engage in discussions and receive updates.

5. Users can access app content on mobile devices using the Power BI mobile app, making it convenient for the on-the-go insights.

YouTube Link

Here is a more detailed video on Introduction to Power BI.

Intro to Power BI