Power BI

Imagine effortlessly asking your business intelligence dashboard any question and receiving instant, insightful answers. This is not a futuristic concept but a reality unfolding through the power of Large Language Models (LLMs).

Descriptive analytics is at the core of this transformation, turning raw data into comprehensible narratives. When combined with the advanced capabilities of LLMs, Business Intelligence (BI) dashboards evolve from static displays of numbers into dynamic tools that drive strategic decision-making. 

LLMs are changing the way we interact with data. These advanced AI models excel in natural language processing (NLP) and understanding, making them invaluable for enhancing descriptive analytics in Business Intelligence (BI) dashboards.

 

 

In this blog, we will explore the power of LLMs in enhancing descriptive analytics and its impact of business intelligence dashboards.

Understanding Descriptive Analytics

Descriptive analytics is the most basic and common type of analytics that focuses on describing, summarizing, and interpreting historical data.

Companies use descriptive analytics to summarize and highlight patterns in current and historical data, enabling them to make sense of vast amounts of raw data to answer the question, “What happened?” through data aggregation and data visualization techniques.

The Evolution of Dashboards: From Static to LLM

Initially, the dashboards served as simplified visual aids, offering a basic overview of key metrics amidst cumbersome and text-heavy reports.

However, as businesses began to demand real-time insights and more nuanced data analysis, the static nature of these dashboards became a limiting factor forcing them to evolve into dynamic, interactive tools. The dashboards transformed into Self-service BI tools with drag-drop functionalities and increased focus on interactive user-friendly visualization.

This is not it, with the realization of increasing data, Business Intelligence (BI) dashboards shifted to cloud-based mobile platforms, facilitating integration to various data sources, and allowing remote collaboration. Finally, the Business Intelligence (BI) dashboard integration with LLMs has unlocked the wonderful potential of analytics.

 

Explore the Top 5 Marketing Analytics Tools for Success

 

Role of Descriptive Analytics in Business Intelligence Dashboards and its Limitations

Despite of these shifts, the analysis of dashboards before LLMs remained limited in its ability to provide contextual insights and advanced data interpretations, offering a retrospective view of business performance without predictive or prescriptive capabilities. 

The following are the basic capabilities of descriptive analytics:

Defining Visualization

Descriptive analytics explains visualizations like charts, graphs, and tables, helping users quickly grasp key insights. However, this requires manually describing the analyzed insights derived from SQL queries, requiring analytics expertise and knowledge of SQL. 

Trend Analysis

By identifying patterns over time, descriptive analytics helps businesses understand historical performance and predict future trends, making it critical for strategic planning and decision-making.

However, traditional analysis of Business Intelligence (BI) dashboards may struggle to identify intricate patterns within vast datasets, providing inaccurate results that can critically impact business decisions. 

Reporting

Reports developed through descriptive analytics summarize business performance. These reports are essential for documenting and communicating insights across the organization.

However, extracting insights from dashboards and presenting them in an understandable format can take time and is prone to human error, particularly when dealing with large volumes of data.

 

 

LLMs: A Game-Changer for Business Intelligence Dashboards

Advanced Query Handling 

Imagine you would want to know “What were the top-selling products last quarter?” Conventionally, data analysts would write an SQL query, or create a report in a Business Intelligence (BI) tool to find the answer. Wouldn’t it be easier to ask those questions in natural language?  

LLMs enable users to interact with dashboards using natural language queries. This innovation acts as a bridge between natural language and complex SQL queries, enabling users to engage in a dialogue, ask follow-up questions, and delve deeper into specific aspects of the data.

Improved Visualization Descriptions

Advanced Business Intelligence (BI) tools integrated with LLMs offer natural language interaction and automatic summarization of key findings. They can automatically generate narrative summaries, identify trends, and answer questions for complex data sets, offering a comprehensive view of business operations and trends without any hustle and minimal effort.

Predictive Insights

With the integration of a domain-specific Large Language Model (LLM), dashboard analysis can be expanded to offer predictive insights enabling organizations to leverage data-driven decision-making, optimize outcomes, and gain a competitive edge.

Dashboards supported by Large Language Mode (LLMs) utilize historical data and statistical methods to forecast future events. Hence, descriptive analytics goes beyond “what happened” to “what happens next.”

Prescriptive Insights

Beyond prediction, descriptive analytics powered by LLMs can also offer prescriptive recommendations, moving from “what happens next” to “what to do next.” By considering numerous factors, preferences, and constraints, LLMs can recommend optimal actions to achieve desired outcomes. 

 

Read more about Data Visualization

 

Example – Power BI

The Copilot integration in Power BI offers advanced Business Intelligence (BI) capabilities, allowing you to ask Copilot for summaries, insights, and questions about visuals in natural language. Power BI has truly paved the way for unparalleled data discovery from uncovering insights to highlighting key metrics with the power of Generative AI.

Here is how you can get started using Power BI with Copilot integration;

Step 1

Open Power BI. Create workspace (To use Copilot, you need to select a workspace that uses a Power BI Premium per capacity, or a paid Microsoft Fabric capacity).

Step 2

Upload your business data from various sources. You may need to clean and transform your data as well to gain better insights. For example, a sample ‘sales data for hotels and resorts’ is used here.

 

 

Step 3

Use Copilot to unleash the potential insights of your data. 

Start by creating reports in the Power BI service/Desktop. Copilot allows the creation of insightful reports for descriptive analytics by just using the requirements that you can provide in natural language.  

For example: Here a report is created by using the following prompt:

 

 

Copilot has created a report for the customer profile that includes the requested charts and slicers and is also fully interactive, providing options to conveniently adjust the outputs as needed. 

 

 

Not only this, but you can also ask analysis questions about the reports as explained below.

 

 

The copilot now responds by adding a new page to the report. It explains the ‘main drivers for repeat customer visits’ by using advanced analysis capabilities to find key influencers for variables in the data. As a result, it can be seen that the ‘Purchased Spa’ service has the biggest influence on customer returns followed ‘Rented Sports Equipment’ service.

 

 

Moreover, you can ask to include, exclude, or summarize any visuals or pages in the generated reports. Other than generating reports, you can even refer to your existing dashboard to question or summarize the insights or to quickly create a narrative for any part of the report using Copilot. 

Below you can see how the Copilot has generated a fully dynamic narrative summary for the report, highlighting the useful insights from data along with proper citation from where within the report the data was taken.

 

 

Microsoft Copilot simplifies Data Analysis Expressions (DAX) formulas by generating and editing these complex formulas. In Power BI, you can easily navigate to the ‘Quick Measure’ button in the calculations section of the Home tab. (if you do not see ‘suggestions with Copilot,’ then you may enable it from settings.

Otherwise, you may need to get it enabled by your Power BI Administrator).

Quick measures are predefined measures, eliminating the need for creating your own DAX syntax. It’s generated automatically according to the input you provide in Natural Language via the dialog box. They execute a series of DAX commands in the background and display the outcomes for utilization in your report.

 

 

In the below example, it can be seen that the copilot gives suggestion for a quick measure based on the data, generating the DAX formula as well. If you find the suggested measure satisfactory, you can simply click the “Add” button to seamlessly incorporate it into your model.

 

 

There can be several other things that you can do with copilot with clear and understandable prompts to questions about your data and generate more insightful reports for your Business Intelligence (BI) dashboards.  

Hence, we can say that Power BI with Copilot has proven to be the transformative force in the landscape of data analytics, reshaping how businesses leverage their data’s potential.

 

 

Embracing the LLM-led Era in Business Intelligence

Descriptive analytics is fundamental to Business Intelligence (BI) dashboards, providing essential insights through data aggregation, visualization, trend analysis, and reporting. 

The integration of Large Language Models enhances these capabilities by enabling advanced query handling, improving visualization descriptions, and reporting, and offering predictive and prescriptive insights.

This new LLM-led era in Business Intelligence (BI) is transforming the dynamic landscape of data analytics, offering a glimpse into a future where data-driven insights empower organizations to make informed decisions and gain a competitive edge.

Data Analysis Expressions (DAX) is a language used in Analysis Services, Power BI, and Power Pivot in Excel. DAX formulas include functions, operators, and values to perform advanced calculations and queries on data in related tables and columns in tabular data models. 

 The Basics of DAX for Data Analysis 

DAX is a powerful language that can be used to create dynamic and informative reports that can help you make better decisions. By understanding the basics of Data Analysis Expressions, you can: 

• Perform advanced calculations on data 
• Create dynamic filters and calculations 
• Create measures that can be used in reports 
• Build tabular data models 

Creating DAX Tables, Columns, and Measures 

Data Analysis Expression tables are similar to Excel tables, but they can contain calculated columns and measures. Calculated columns are formulas that are applied to all rows in a column, while measures are formulas that are calculated based on data in multiple columns. 

To create a DAX table, right-click on the Tables pane and select New Table. In the Create Table dialog box, enter a name for the table and select the columns that you want to include. 

To create a calculated column, right-click on the Columns pane and select New Calculated Column. In the Create Calculated Column dialog box, enter a name for the column and type in the formula that you want to use.

To create a measure, right-click on the Measures pane and select New Measure. In the Create Measure dialog box, enter a name for the measure and type in the formula that you want to use. 

Executing DAX Operators 

Data Analysis Expressions operators are used to perform calculations on data. Some common DAX operators include: 

• Arithmetic operators: These operators are used to perform basic arithmetic operations, such as addition, subtraction, multiplication, and division. 
• Comparison operators: These operators are used to compare two values and return a Boolean value (true or false). 
• Logical operators: These operators are used to combine Boolean values and return a Boolean value. 
• Text operators: These operators are used to manipulate text strings. 

Read more –> Data Analysis Roadmap 101: A step-by-step guide

Discussing Basic Math & Statistical Functions 

DAX includes a wide variety of mathematical and statistical functions that can be used to perform calculations on data. Some common mathematical and statistical functions include: 

• SUM: This function returns the sum of all values in a column or range. 
• AVERAGE: This function returns the average of all values in a column or range. 
• COUNT: This function returns the number of non-empty values in a column or range. 
• MAX: This function returns the maximum value in a column or range. 
• MIN: This function returns the minimum value in a column or range. 

Implementing Date & Time Functions 

Data Analysis Expressions includes many date and time functions that can be used to manipulate date and time data. Some common date and time functions include: 

• DATEADD: This function adds a specified number of days, months, years, or hours to a date. 
• DATEDIFF: This function returns the number of days, months, years, or hours between two dates. 
• TODAY: This function returns the current date. 
• NOW: This function returns the current date and time. 

Using Text Functions 

DAX includes several text functions that can be used to manipulate text data. Some common text functions include: 

• LEFT: This function returns the leftmost characters of a string. 
• RIGHT: This function returns the rightmost characters of a string. 
• MID: This function returns a substring from a string. 
• LEN: This function returns the length of a string. 
• TRIM: This function removes leading and trailing spaces from a string. 

Using calculate & filter functions 

Data Analysis Expressions includes several calculate and filter functions that can be used to create dynamic calculations and filters. Some common calculate and filter functions include: 

• CALCULATE: This function allows you to create dynamic calculations that are based on the current context. 
• FILTER: This function allows you to filter data based on a condition. 

Summing up Data Analysis Expressions (DAX) 

Data Analysis Expressions is a powerful language that can be used to perform advanced calculations and queries on data in Analysis Services, Power BI, and Power Pivot in Excel. By understanding the basics of DAX, you can create dynamic and informative reports that can help you make better decisions. 

Are you geared to create a sales dashboard on Power BI and track key performance indicators to drive sales success? This step-by-step guide will show you through connecting to the data source, build the dashboard, and add interactivity and filters.

Creating a sales dashboard in Power BI is a straightforward process that can help your sales team to track key performance indicators (KPIs) and make data-driven decisions. Here’s a step-by-step guide on how to create a sales dashboard using the above-mentioned KPIs in Power BI: 

 

Step 1: Connect to your data source 

The first step is to connect to your data source in Power BI. This can be done by clicking on the “Get Data” button in the Home ribbon, and then selecting the appropriate connection type (e.g., Excel, SQL Server, etc.). Once you have connected to your data source, you can import the data into Power BI for analysis. 

Step 2: Create a new report 

Once you have connected to your data source, you can create a new report by clicking on the “File” menu and selecting “New” -> “Report.” This will open a new report canvas where you can begin to build your dashboard. 

Step 3: Build the dashboard 

To build the dashboard, you will need to add visualizations to the report canvas. You can do this by clicking on the “Visualizations” pane on the right-hand side of the screen, and then selecting the appropriate visualization type (e.g., bar chart, line chart, etc.).

Once you have added a visualization to the report canvas, you can use the “Fields” pane on the right-hand side to add data to the visualization. 

 

Read more about maximizing sales success with dashboards by clicking on this link.

 

Step 4: Add the KPIs to the dashboard 

To add the KPIs to the dashboard, you will need to create a new card visualization for each KPI. Then, use the “Fields” pane on the right-hand side of the screen to add the appropriate data to each card. 

Sales Revenue:

To add this KPI, you’ll need to create a card visualization and add the “Total Sales Revenue” column from your data source. 

Sales Quota Attainment:

To add this KPI, you’ll need to create a card visualization and add the “Sales Quota Attainment” column from your data source. 

Lead Conversion Rate:

To add this KPI, you’ll need to create a card visualization and add the “Lead Conversion Rate” column from your data source. 

Customer Retention Rate:

To add this KPI, you’ll need to create a card visualization and add the “Customer Retention Rate” column from your data source. 

Average Order Value:

To add this KPI, you’ll need to create a card visualization and add the “Average Order Value” column from your data source. 

Step 5: Add filters and interactivity 

Once you have added all the KPIs to the dashboard, you can add filters and interactivity to the visualizations. You can do this by clicking on the “Visualizations” pane on the right-hand side of the screen and selecting the appropriate filter or interactivity option.

For example, you can add a time filter to your chart to show sales data over a specific period, or you can add a hover interaction to your diagram to show more data when the user moves their mouse over a specific point.

 

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Step 6: Publish and share the dashboard 

Once you’ve completed your dashboard, you can publish it to the web or share it with specific users. To do this, click on the “File” menu and select “Publish” -> “Publish to Web” (or “Share” -> “Share with specific users” if you are sharing the dashboard with specific users).

This will generate a link that can be shared with your team, or you can also publish the dashboard to the Power BI service where it can be accessed by your sales team from anywhere, at any time. You can also set up automated refresh schedules so that the dashboard is updated with the latest data from your data source.

 

 

Ready to transform your sales strategy with a custom dashboard in Power BI?

By creating a sales dashboard in Power BI, you can bring all your sales data together in one place, making it easier for your team to track key performance indicators and make informed decisions. The process is simple and straightforward, and the end result is a custom dashboard that can be customized to fit the specific needs of your sales team.

Whether you are looking to track sales revenue, sales quota attainment, lead conversion rate, customer retention rate, or average order value, Power BI has you covered. So why wait? Get started today and see how Power BI can help you drive growth and success for your sales team! 

Data is an essential component of any business, and it is the role of a data analyst to make sense of it all. Power BI is a powerful data visualization tool that helps them turn raw data into meaningful insights and actionable decisions.

In this blog, we will explore the role of data analysts and how they use Power BI to extract insights from data and drive business success. From data discovery and cleaning to report creation and sharing, we will delve into the key steps that can be taken to turn data into decisions. 

A data analyst is a professional who uses data to inform business decisions. They process and analyze large sets of data to identify trends, patterns, and insights that can help organizations make more informed decisions. 

 

Who is a data analyst?

A data analyst is a professional who works with data to extract insights, draw conclusions, and support decision-making. They use a variety of tools and techniques to clean, transform, visualize, and analyze data to understand patterns, relationships, and trends. The role of a data analyst is to turn raw data into actionable information that can inform and drive business strategy.

They use various tools and techniques to extract insights from data, such as statistical analysis, and data visualization. They may also work with databases and programming languages such as SQL and Python to manipulate and extract data. 

The importance of data analysts in an organization is that they help organizations make data-driven decisions. By analyzing data, analysts can identify new opportunities, optimize processes, and improve overall performance. They also help organizations make more informed decisions by providing insights into customer behavior, market trends, and other key metrics.

Additionally, their role and job can help organizations stay competitive by identifying areas where they may be lagging and providing recommendations for improvement. 

Defining Power BI 

Power BI provides a suite of data visualization and analysis tools to help organizations turn data into actionable insights. It allows users to connect to a variety of data sources, perform data preparation and transformations, create interactive visualizations, and share insights with others. 

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The platform includes features such as data modeling, data discovery, data analysis, and interactive dashboards. It enables organizations to quickly create and share visualizations, reports, and dashboards with stakeholders, regardless of their technical skill level.

Power BI also provides collaboration features, allowing team members to work together on data insights, and share information and insights with others through Power BI reports and dashboards. 

Key capabilities of Power BI  

Data Connectivity:It allows users to connect to various data sources including Excel, SQL Server, Azure SQL, and other cloud-based data sources. 

Data Transformation: It provides a wide range of data transformation tools that allow users to clean, shape, and prepare data for analysis. 

Visualization: It offers a wide range of visualization options, including charts, tables, and maps, that allow users to create interactive and visually appealing reports. 

Sharing and Collaboration: It allows users to share and collaborate on reports and visualizations with others in their organization. 

Mobile Access: It also offers mobile apps for iOS and Android, that allow users to access and interact with their data on the go. 

How does a data analyst use Power BI? 

A data analyst uses Power BI to collect, clean, transform, visualize, and analyze data to turn it into meaningful insights and decisions. The following steps outline the process of using Power BI for data analysis: 

1. Connect to data sources: A data analyst can import data from a variety of sources, such as spreadsheets, databases, or cloud-based services. Power BI provides several ways to import data, including manual upload, data connections, and direct connections to data sources. 
2. Clean and transform data: Before data can be analyzed, it often needs to be cleaned and prepared. This may include removing any extraneous information, correcting errors or inconsistencies, and transforming data into a format that is usable for analysis.
3. Create visualizations: Once the data has been prepared, a data analyst can use Power BI to create visualizations of the data. This may include bar charts, line graphs, pie charts, scatter plots, and more. Power BI provides a few built-in visualizations and the ability to create custom visualizations, giving data analysts a wide range of options for presenting data. 
4. Perform data analysis: Power BI provides a range of data analysis tools, including calculated fields and measures, and the DAX language, which allows data analysts to perform more advanced analysis. These tools allow them to uncover insights and trends that might not be immediately apparent. 
5. Collaborate and share insights: Once insights have been uncovered, data analysts can share their findings with others through Power BI reports or dashboards. These reports provide a way to present data visualizations and analysis results to stakeholders and can be published and shared with others. 

 

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By following these steps, a data analyst can use Power BI to turn raw data into meaningful insights and decisions that can inform business strategy and decision-making. 

 

Why should you use data analytics with Power BI? 

User-friendly interface – Power BI has a user-friendly interface, which makes it easy for users with little to no technical skills to create and share interactive dashboards, reports, and visualizations. 

Real-time data visualization – It provides real-time data visualization, allowing users to analyze data in real time and make quick decisions. 

Integration with other Microsoft tools – Power BI integrates seamlessly with other Microsoft tools, such as Excel, SharePoint, and Azure, making it an ideal tool for organizations using Microsoft technology. 

Wide range of data sources – It can connect to a wide range of data sources, including databases, spreadsheets, cloud services, and web APIs, making it easy to consolidate data from multiple sources. 

Cost-effective – It is a cost-effective solution for data analytics, with both free and paid versions available, making it accessible to organizations of all sizes. 

Mobile accessibility – Power BI provides mobile accessibility, allowing users to access and analyze data from anywhere, on any device. 

Collaboration features – With robust collaboration features, it allows users to share dashboards and reports with other team members, encouraging teamwork and decision-making. 

Conclusion 

In conclusion, Power BI is a powerful tool for data analysis that provides organizations with the ability to easily visualize, analyze, and share complex data. By preparing, cleaning, and transforming data, creating relationships between tables, using visualizations and DAX, they can create reports and dashboards that provide valuable insights into key business metrics.

The ability to publish reports, share insights, and collaborate with others makes Power BI an essential tool for any organization looking to improve performance and make informed decisions.

In this blog, we will look into different methods of data transformation, data exploration, and data visualization using Power BI.

Prerequisites to work with Power BI: 

• Download Dataset 
• Install Power BI 

 

Downloading Data: 

 We will use an open-source dataset available on Kaggle. This link contains several other datasets, but we will use “states_all.csv” in this blog. The link contains all the column descriptions. 

Watch this video to learn Power BI end-to-end

 

 

Moving forward, let us first see how to install it on our desktop: 

 

Installing Power BI: 

 

You can download Power BI for any OS from here. The installation is relatively easier, you can click on next for every prompt you get. After you have installed it, let us open it. 

 

This will be the screen you will land on after opening it. 

 

The data we have is in a CSV file so, we can use “Import data from Excel” to view it in Power BI (remember to select All Files from the file explorer). Just navigate to the file and click on open. A new screen will open which will preview the data you selected. First, we need to do some transformations on this data, for that click on Transform data at the bottom right of this screen.  

Transformation: 

There are some columns that have null values, so we can remove them. We can do this by clicking on individual columns and then selecting Remove Columns from the upper tab. Do the same for other columns 

• OTHER_EXPENDITURE 
• GRADES_1_8_G 

• GRADES_9_12_G 
• AVG_READING_8_SCORE 

We can also remove the PRIMARY_KEY column as it is of no importance to us in the later steps.  

After doing all this, click on Close & Apply at the top left.  

 

Data visualization: 

Now we are ready to visualize the data. On the right, you can see all the imported columns from the CSV file. 

 

 

1. Clustered column chart:

 

Let us create a clustered column chart to visualize 4th grade scores per year. To do this first select clustered column chart from the Visualizations pane. After that, drag down the Year column to the X-axis and GRADES_4_G to the y-axis. 

As we can see from the graph above, the sum of all the grades lies in the same range every year 

 

2. Line chart:

 

Now Let us make a line chart showing local revenue affected every year. For that, we can select a line chart from the Visualizations pane. Select Year as the x-axis and LOCAL_REVENUE as the y-axis.  

From the above graph, we can see the local revenue increasing every year 

 

3. Pie chart:

 

If we want to see the Revenue generated by each; Local, Federal, and State. We can use a Pie Chart for that. We can select Pie Chart from the pane and drag LOCAL_REVENUE, FEDERAL_REVENUE and STATE_REVENUE to the values tab. 

The pie chart shows the sum of different amounts of revenue  

 

4. Area chart:

 

At last, we can compare any two grades to see their revenue changes during the past years. For this purpose, we can use the Area Chart from the visualizations pane and use GRADES_4_G as the y-axis and GRADES_12_G as the secondary y-axis. Drag YEAR to the x-axis.  

The Area chart shows the difference in grades of class 4 and 12 on top of each other. 

Finally, we have this report to showcase to our colleagues or friends. 

 

Conclusion: 

In this blog, we saw how to use the tool for data transformation and what are some different graphs we can use to visualize academic data. Learn more about Power BI in the course offered by Data Science Dojo and enable yourself to emulate these learnings at work. 

 

Power BI transforms your data into visually immersive and interactive insights. It connects your multiple sources of data with the help of apps, software services, and connectors.

Whether you save your data on an excel spreadsheet, on cloud premises, or on on-premises data warehouses, Power BI gathers and shares your data easily with anyone whenever you want. 

 

Who uses Power BI? 

The use of it may vary depending on the purpose you need to fulfill. Mostly, the software is used for presenting reports and viewing data dashboards and presentations. If you are responsible for creating reports, presenting weekly datasheets, or even being involved in data analysis then probably you might make extensive use of Power BI Desktop or Report Builder to create reports. Also, it allows you to publish your report to its service where you can view and share it later.   

Whereas developers use Power BI APIs to push data into datasets or to embed dashboards and reports into their own custom applications. 

 

Let’s learn how Power BI works step by step: 

 Loading dataset in Power BI 

On the dashboard, there are a number of options to use for uploading or importing your dataset. So, the first step is to import your dataset. The software supports a number of data reports formats that we discussed earlier. Let’s say you add an excel sheet to Power BI, for that click on excel workbook on the main screen and simply select the file you want to upload.  

As your data is visible now, first you need to perform data pre-processing which requires cleaning up your data and then transforming your data. As you click on transform data, you will be taken to the power query editor. 

Power Query Editor 

Power Query is the engine behind Power BI. All the data pre-processing is going to be done in this window. It cleans and import millions of rows into the data model to help you perform data analysis after. 

The tool is simple to use and requires no code to do any task. With the help of Power Query, it is possible to Extract, Transform, and Load the data. The tool offers the following benefits and simplify the tasks you perform regularly: 

• In order to access and transform data regularly, you enter a repeatable query that just needs to be refreshed in the future to get up to data.  
• Power Query provides connectivity to hundreds of data sources and over 350 different types of data transformations 
• Equipped with a number of pre-built transformation functions as simple as adding or deleting rows 

Build visuals with your data 

You can check out a number of Power BI visualizations that you can choose from the visualization pane. Simply choose from the range of visuals available in the panel. 

You can create custom data visualizations if you can’t find the visual you want in AppSource. To differentiate your organization and build something distinctive, personalize data visualizations. When they’re ready, you can share what you’ve created with your team or publish it to its community. 

Working with the eye-catching visuals increase comprehension, retention, and appeal that help you interact with your data and make informed decisions quickly. 

Watch this video to learn each step of developing visuals for your specific industry and business: 

Number of visualizations options offered by Power BI 

It is a data visualization and analysis tool that offers different types of visualizations. The most popular and useful ones are Charts, Maps, Tables, and Data Bars. 

Charts are a simple way to present data in an easy-to-understand format. They can be used for showing trends, comparisons or changes over time. A map is a great way to show the geographical location of certain events or how they relate to each other on a map. A table provides detailed information that can be sorted by columns and rows so it’s easier to analyze the information in the table. Data bars are used to show progress towards goals or targets with their height representing the amount of progress made. 

Career opportunities with Power BI

Power BI:

Analyst
Software Engineer
Senior Business Intelligence Analyst

Business Analyst
Data Analyst
Developer

Senior Software Engineer

Recently, the use of this tool has increased and has been adopted widely in multiple industries. It includes IT, healthcare, financial services, insurance, staffing & recruiting, and computer software. Some of the major companies that use the tool include:

Adobe (USA)
Conde Nast (USA)
Dell (USA)
Hospital Montfort (Canada)
Kraft Heinz Co (USA)
Meijer (USA)
Nestle (China)
Rolls-Royce Holdings PLC (UK)

The average annual salary of a Power BI professional in Unites States is $100,726 /yr.

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The advantage of this visualization tool is its ease of use, even by people who don’t consider themselves to be very technologically proficient. As long as you have access to the data sources, the dashboard, and a working network connection, you can use it to process the information, create the necessary reports, and send them off to the right teams or individuals.

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Imagine a world without birds: the ecosystem would fall apart, bug populations would skyrocket, erosion would be catastrophic, crops would be torn down by insects, and so many other damages. Did you know that 1,200 species are facing extinction over the next century, and many more are suffering from severe habitat loss? (source).

Birds are fascinating and beautiful creatures who keep the ecosystem organized and balanced. They have emergent properties that help them react spontaneously in many situations, which are unique to other organisms.

Here are some fun facts: Parasitic jaegers ( a type of bird species) obtain food by stealing it directly from the beaks of other birds. The Bassian Thrush finds its food using the most unique way possible: they have adapted their foraging methods to depend on creating a large amount of gas to surprise earworms and trigger them to start moving (so the birds can find and eat it).

Due to the intriguing behaviors of birds, I got inspired and lifted to create an app that could identify any bird that you are captivated by in real time. I also built this app to raise awareness of the heart-breaking reality that most birds face around the world.

 

Global trends of bird species survival chart

I first researched bird populations and their global trends from the data that contains the information of the past 24 years. I then analyzed this data set and created interactive visuals using Power BI.

This chart displays the Red List Index (RLI) of species survival from 1988 to 2012. RLI values range from 1 (no species at risk of extinction in the near term) down to 0 (all species are extinct).

As you click on the Power BI Line Chart you will notice that since 1988, bird species have faced a steadily increasing risk of extinction in every major region of the world (change being more rapid in certain regions). 1 in 8 currently known bird species in the world are at the threshold of extinction.

The main reasons are degradation/loss of habitat (due to deforestation, sea-level rise, more frequent wildfires, droughts, flooding, loss of snow and ice, and more), bird trafficking, pollution, and global warming. As figured, most of these are a result of us humans.

Due to industrialization, more than 542,390,438 birds have lost their lives. Climate change is causing the natural food chain to fall apart. Birds starve with lesser food (therefore must fly longer distances), choke on human-made pollutants, and end up becoming weaker. Change is necessary, and with change comes compassion. This web app can help to build an understanding and empathy toward birds.

Let’s look at the Power BI reports and the web app.

 

Power BI report: Bird attributes / Bird Recognition

As you can see in this report, along with recognizing a specific bird in real-time, interactive visualizations from Power BI display the unique attributes and information about each bird and its status in the wild. The fun facts on the visualization about each bird will linger in your mind for days.

AI web app – To create a bird recognition app

In this web app, I used cognitive services to upload the images (of the 85 bird species), tagged them, trained the model, and evaluated the results. With Microsoft Custom Vision AI, I could train the model to recognize 85 bird species. You can upload an image from your file explorer, and it will then predict the species name of the bird and the accuracy tied to that tag.

The Custom Vision Service uses machine learning to classify the images I uploaded. The only thing I was required to do was specify the correct tag for each image. You can also tag thousands of images at a time.

The AI algorithm is immensely powerful as it gives us great accuracy and once the model is trained, we can use the same model to classify new images according to the needs of our app.

1. Choose a bird image from your PC
2. Upload a bird image URL
3. Take a picture of a bird in real-time (only works on the phone app as described later in the blog)

Once you upload an image, it will call the Custom Vision Prediction API (which was already trained by Custom Vision, powered by Microsoft) to get the species of the bird.

Phone application

I also created a phone application, called ‘AI for Birds’, that you can use with camera integration for taking pictures of birds in real time. After using the built-in camera to take a picture, the name of the bird species will be identified and shown. As of now, I added 85 bird species to the AI model, however, that number will increase.

The journey of building my own custom model, training it, and deploying it has been noteworthy. Here is the link to my other blog for how to build your own AI custom model. You can also follow along with these steps and use them as a tutorial: Instructions for how to create Power BI reports and publish them to the web will also be provided in the other blog.

 

Conclusion

The grim statistics are not just sad news for bird populations. They are sad news for the planet because the health of bird species is a key- measure for the state of ecosystems and biodiversity on planet Earth in general.

I believe in Exploring- Learning- Teaching- Sharing. There are several thousands of other bird species that are critical to biodiversity on planet Earth.

Consider looking at my app and supporting organizations that work to fight the constant threats of habitat destruction and global warming today.

Our Earth is full of unique birds which took millions of years to evolve into the striking bird species we see today. We do not want to destroy organisms that took millions of years to evolve in just a couple of decades.

Sources:

• https://www.canada.ca/en/environment-climate-change/services/environmental-indicators/global-trends-bird-species-survival.html
• https://www.fws.gov/birds/bird-enthusiasts/threats-to-birds.php

 

 

Written by Saumya Soni

Power BI and R can be used together to achieve analyses that are difficult or impossible to achieve.

It is a powerful technology for quickly creating rich visualizations. It has many practical uses for the modern data professional including executive dashboards, operational dashboards, and visualizations for data exploration/analysis.

Microsoft has also extended Power BI with support for incorporating R visualizations into its projects, enabling a myriad of data visualization use cases across all industries and circumstances. As such, it is an extremely valuable tool for any Data Analyst, Product/Program Manager, or Data Scientist to have in their tool belt.

At the meetup for this topic presenter David Langer showed how it can be using R visualizations to achieve analyses that are difficult, or not possible, to achieve with out-of-the-box features.

A primary focus of the talk was a number of “gotchas” to be aware of when using R Visualizations within the projects:

• It limits data passed to R visualizations to 150,000 rows.
• It automatically removes duplicate rows before passing data to it.
• It allows for permissive column names that can cause difficulties in R code.

David also covered best practices for using R visualizations within its projects, including using R tools like RStudio or Visual Studio R Tools to make R visualization development faster. A particularly interesting aspect of the talk was how to engineer R code to allow for copy-and-paste from RStudio into Power BI.

The talk concluded with examples of how R visualizations can be incorporated into a project to allow for robust, statistically valid analyses of aggregated business data. The following visualization is an example from the talk:

Enjoy the video of Power BI!

Learn more about Power BI with Data Science Dojo

 

 

Written by Dave Langer

In my first blog, ‘Bird Recognition App using Microsoft Custom Vision AI and Power BI’, we looked at the intriguing behaviors and attributes of birds using Power BI. This inspired me to create an ‘AI for birds’ web app’ using Azure Custom Vision along with a phone app using Power Apps and an iPhone / Android platform that could identify a bird in real-time. I created this app to raise awareness of the heart-breaking reality which most birds face around the world.

In this blog, let’s go behind the scenes and take a look at the journey of how this was created.

What is Azure custom vision?

Azure Custom Vision is an image recognition AI service part of Azure Cognitive Services that enables you to build, deploy, and improve your own image identifiers.  An image identifier applies labels (which represent classes or objects) to images, according to their visual characteristics. It allows you to specify the labels and train custom models to detect them.

What does Azure custom vision do?

The Custom Vision service uses a Machine Learning algorithm to analyze images. You can submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of the submission. Then, the algorithm trains to this data and calculates its accuracy by testing itself on those same images.

Once the algorithm is trained, you can run a test, retrain, and eventually use it in your image recognition app to classify new images. You can also export the model itself for offline use.

How does it work?

1. Upload images – Bring your own labelled images or use Custom Vision to quickly add tags to any unlabeled images.
2. Train the model – Use your labelled images to teach Custom Vision the concepts you care about.
3. Evaluate the result – Use simple REST API calls to quickly tag images with your new custom computer vision model.

The Custom Vision Service uses machine learning to classify the images you upload. The only thing that is required to do is specify the correct tag for each image. You can also tag thousands of images at a time. The AI algorithm is immensely powerful and once the model is trained, you can use the same model to classify new images according to the needs of the app.

Prerequisites to create bird recognition app

Here are the prerequisites:

1. An account with Custom Vision AI; you can either use the free subscription or use your Azure account.
2. A database of images for training the model.
3. Enough data to get started.

The Journey of Creating my Custom Vision AI Model

I first visited https://customvision.ai/, then I logged in with the Azure credentials.

1. I created a new project.

2.     I added as many relevant images as possible and tagged them correctly.

3.     I trained my model with 4590 images of 85 different species of birds.

4.     Model evaluation using ‘Quick Test’

I calibrated the precision to be higher than 90%. The Precision value increases as you upload and train with more and more images.

When I trained the model with the new data, a new iteration was created. The accuracy and precision improved over time as I increased the training data set to 1200 images of 85 different species. (We should keep an eye on the precision value during various iterations.) I tested my model during this process using ‘Quick Test’ and deployed it.

Using the Model with the Prediction API

The Custom Vision AI worked as expected. Then I needed the required keys to create an application using Custom Vision AI.

So, I clicked on the “Gear Icon” (settings) and saved my project ID and prediction key. After that, I got the prediction URL from the Performance tab.

How to Experience the Custom Vision API in Power Apps, Mobile Application, & the Website

1.     Power Apps:

The Custom Vision API can be linked to the Power Apps by the “Custom Vision” connector. By providing a few details to the custom vision connector such as “Prediction Key” as well as “Site URL”, you can seamlessly use Custom Vision API in your Power App.

2.     Mobile Application (Android and iOS):

In the Flutter Application, we called the Custom Vision API by using HTTP requests as well as Dio Packages. For Power BI Reports part of the mobile app, we embedded the Power BI report iframes into the flutter app by using WebView.

3.     Website:

The Custom Vision API is connected to the website via Ajax & HTML tags. On the website, we published the Power BI Report through the HTML iframe. The generated Power BI Embedded iframe is effortlessly compatible with all the browsers.

The possibilities of Cognitive Services and Machine Learning are limitless!

If you have not tried the AI for Birds Mobile app yet, there is no better time! Both (Android & iOS) apps are available to download.

To download this app, please search “AI for Birds” in the Google Play Store, or the Apple’s App Store.

How to Improve your Classifier?

Let’s talk about the ways to improve the quality of your Custom Vision Service Classifier. The quality of your classifier depends on the amount, quality, and variety of the labelled data that you provide and how balanced the overall dataset is.

A good classifier has a balanced training dataset that represents the submitted classifier. The process of building such a classifier is iterative and it’s common to implement a few rounds of training to reach expected results.

The following is a general pattern to help you build a more accurate classifier:

1. First-round training.
2. Add more images and balance data, then retrain it.
3. Add Images with varying background, lighting, object size, camera angle, and style; retrain.
4. Use the new Image(s) to test the prediction.
5. Modify existing training data according to predicted results.

References

1. https://www.customvision.ai/
2. https://docs.microsoft.com/en-us/azure/cognitive-services/Custom-Vision-Service/overview
3. https://azure.microsoft.com/en-us/services/cognitive-services/custom-vision-service/
4. https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier

Power BI

What is Power BI and what does it do?

Power BI is a business analytics service by Microsoft. It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end-users to create their reports and dashboards.

Power BI is a business suite that includes several technologies that work together to deliver outstanding data visualizations and business intelligence solutions.

You can use the Power BI Desktop tool to import data from various data sources such as files, Azure source, online services, DirectQuery, or gateway sources. You can use this tool to clean and transform the imported data.

Once the data is transformed and formatted, it is ready for creating visualizations in a report. A report is a collection of visualizations like graphs, charts, tables, filters, and slicers.

Next, you can publish the reports created in Power BI desktop to Power BI Service or Power BI Report Server.

Pre-requisites

Here are the Prerequisites:

1. Power BI Desktop App.
2. Power BI Pro Account.

The Journey of Creating the Power BI Reports

I installed Power BI Desktop from the Windows Store. You can also download it from this URL: https://powerbi.microsoft.com/en-us/desktop/

Post-installation, I opened Power BI desktop and then clicked “Get Data” > “Text/CSV”.

Next, I selected the CSV file by browsing the required folder and then clicked “Load”.

From the visualizations pane, I selected a visual for my report. Then, from the Fields pane, I chose the required column(s) for that visual.

Then, I created a report with the collection of different visuals and slicers by adding the specific columns from the table. You can also modify the visuals, and apply filters to discover more in-depth insights.

The Process of Publishing the Power BI Report

1. In Power BI Desktop, I chose to Publish the report on the Home tab. However, you can also go to File > Publish > Publish to Power BI.

2.   I signed into my Power BI account.

3.   Then I chose the destination from the list (you can also choose “My workspace”) and clicked on the Select button.

4.   Once the publishing was complete, I received a link to my report. I selected the link to open my report using Power BI service.

How did I generate the Embed URL and the iframe?

1. To generate the Embed URL and iframe, I signed into the Power BI service (https://www.powerbi.com/).

2.   After opening the required report from the workspace, I navigated to the “Share” dropdown > “Embed report” > “Publish to web” to create the Embed URL and the iframe.

3.   Then I clicked “Create Embed Code”.

4. After generating the Embed URL, I selected the required iframe size and copied the generated iframe, so I can use the iframe in my website.

This way, using Microsoft Power BI, I was able to create a highly interactive & customizable report of various bird species from the original data set.

POWER APPS

What is Power Apps?

Power Apps is a suite of apps, services, connectors, and data platform that provides a rapid application development environment to build custom apps of your needs. Apps built using Power Apps provide rich business logic and workflow capabilities to transform your manual business processes to digital, automated processes.

Power Apps also provides an extensible platform that lets pro-developers: programmatically interact with data and metadata, apply business logic, create custom connectors, and integrate with external data.

Using Power Apps, you can create three types of apps: canvas, model-driven, and portal.

To create an app, you start with make.powerapps.com.

• Power Apps Studio is the app designer used for building canvas apps. The app designer makes creating apps feel more like building a slide deck in Microsoft PowerPoint. More information: Generate an app from data.
• App designer for model-driven apps lets you define the sitemap and add components to build a model-driven app.
• Power Apps portals Studio is a WYSIWYG (what you see is what you get) design tool to add and configure webpages, components, forms, and lists.

Prerequisites for Power Apps Development

·       A Microsoft 365 Business Premium Account.

My Power Apps Development Process (Canvas App)

1.     I signed in to Power Apps.

2.     I clicked on the Create > Canvas app from blank.

3.     After specifying my app name as “AI for Birds” > I selected “Phone” to be the Power Apps Format > and clicked “Create”.

4.     I checked “Don’t show me this again” from the pop up > Skip.

5. From the dropdown menu, I selected my Country as “United States” > Get Started.

6. From the blank canvas, I added some new screens and UI elements with proper screen navigations.

Steps to Connect Custom Vision with Power Apps

Power App uses Custom Vision API to detect Bird species with the help of the images. I connected Custom Vision API with Power Apps.

Here are the steps I followed:

1.     First, I clicked the File menu.

2.     Then I clicked on Collections on the left navigation bar.

3.     To establish the connection, I clicked on a New connection option from the top navigation bar.

4.     On the new connections list screen, I clicked the “+” icon & put my prediction key and site URL.

5.     Once the connection was established between the Custom Vision and the Power Apps, I was able to implement the same into the Power Apps.

(Note: The prediction key and the site URL are accessible from the Custom Vision AI website, wherein I created an image classifier.)

Implementing the Custom Vision into Power Apps:

After connecting the Custom Vision to Power Apps, here are the steps that I followed:

• In the image container (in my case, it was “UploadedImage2“), I created a Collection that stores the results of custom vision prediction.
• To store results in the gallery, the following syntax      was used:

On click Syntax: ClearCollect (<Name of your Collection to store the predicted results>, CustomVision.ClassifyImageV2(“<Your Project ID>”, “<YourProject name which can be obtained from the Custom Vision website>”, <Your Image Container>).predictions);

Publishing My Power App:

·       To publish the Power App, I clicked on File > Save > Publish.

How to Consume Power Apps?

Desktop:

1. The ‘AI for Birds’ Power Apps can be downloaded from this link – AI For Birds Power Apps.
2. Download the zip file and extract it, open the Power Apps Studio – https://make.powerapps.com/
3. Sign up with your Microsoft Office 365 account in Power Apps.
4. Click Create > “Canvas app from blank”.

5.   After specifying the app name > Select “Phone” to be the format > Create.

6.   After Clicking Create, it opens the Power App Studio in a new tab. It shows the steps to start building an app from a blank canvas. Just click Skip.

7.   Click on File > Open >Browse (Browse File). Browse the extracted file in Power Apps Studio and upload it.

8.   After adding the extracted file, click “Don’t Save”  and now you are ready to use “Power App Studio”.

9.   To use the Prebuilt custom Vision on Power Apps click “Ask for access”. An email window will open where you can ask the developer of the Custom Vision to grant access to a particular tenant. (Note: There might be a cost associated with the Custom Vision service.)

10.   Once access is granted from the developer of the app, you can use the Custom Vision API on your Power Apps.

11.   After modifying the App, you can save/publish it and view it on your phone.

How to download Power Apps on your Mobile Devices (Android/iOS):

The Power Apps application is available through the Apple App Store and the Google Play Store.

• Download the Power Apps from here. (For Android | For iOS)
• Sign in with your credentials.
• Use the App on your mobile phone.

In this blog, we have seen how easy it is to create power Apps and use it with Custom vision API.

I hope that this blog helps you see how to use custom vision API, Power BI and Power Apps to create a real world application like ‘aiforbirds’.

Using this app, you can easily find the answer to the question, “What type of bird is that?”

Explore bird statuses and trends with maps, species information, and some fun facts. Go to: http://aiforbirds.com/ for the webapp and “AI for Birds” in the App store for the phone app.

Thank you for your time. Good luck!

Sources:

1. https://www.customvision.ai/
2. https://docs.microsoft.com/en-us/azure/cognitive-services/Custom-Vision-Service/overview
3. https://azure.microsoft.com/en-us/services/cognitive-services/custom-vision-service/
4. https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier
5. https://www.tutorialspoint.com/power_bi/index.htm
6. https://en.wikipedia.org/wiki/Microsoft_Power_BI/