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Data analyst practice test number 1.

• Post published: July 28, 2022

This is an Excel Data Analyst exam, you will be challenged to solve various data analysis issues that Excel Data Analysts face in their everyday work!

You will be using functions such as:

And more…

You can view the answers in the Solution tab! 🙂

Exam level – Intermediate-Advanced

If you prefer to work on this exam using regular Excel – click here to download the Data Analyst Practice exam no. 1

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Business Analytics with Excel: Elementary to Advanced

Johns Hopkins University via Coursera Help

• Introduction to Excel: Basics and Best Practices
• The purpose of this course is to expose you to a variety of problems that can be solved using management science methods and modelled in Excel. In this course, we start from the basics of spreadsheet design and work our way up to broader mathematical optimization modelling. Many airlines, banks, and technology companies could not operate today as they do without the skills and techniques taught in this course. In this first module, we begin by introducing a relatively simple example of a mathematical model which we will use as our platform to build off of for more complicated applications later in the course. Many problems used in the video lectures come from the text Business Analytics: Data Analysis & Decision Making by Albright & Winston (Cengage Learning, 2014), ISBN 1285965523
• What-If Analysis in Excel
• We are now ready to introduce more complexity to our spreadsheet models. Since everyone comes from different Excel backgrounds, we will review some basic functions and features as well as more advanced techniques. This module covers more of the modelling process and includes some of the less-well known, but particularly helpful, Excel functions and tools that are available. Remember though that this course's objective is not to be a "how-to" of Excel. Instead, the focus and intent is to use these features to provide insights into real business problems.
• Decision Analysis through Regression and NPV
• In this module the modeling concept of estimating relationships between variables by curve fitting, or regression analysis, is used to solve realistic business problems. Different regression curves are introduced and a mathematical analysis of which curve is best to help defend the model is presented. This allows not only an understanding of the techniques of modelling but also the rational behind which model to use.
• Linear Programming
• In this module we introduce spreadsheet optimization, one of the most powerful and flexible methods of quantitative analysis. The specific type of optimization presented here is linear programming (LP) which is used in all types of organizations to solve a wide variety of problems. As you will see through the examples presented in this course, LP is used in problems of labor scheduling, inventory management, advertising, finance, transportation, staffing, and many others. The goal of this module is to introduce you to the basic elements of LP, the types of problems it can solve, and how to model an LP problem in excel.
• Transportation and Assignment Problems
• This module provides even more examples of problems that can be modeling using linear programming (LP), in particular Transportation and Assignment problems. The basic transportation problem is concerned with finding the best (usually the least cost) way to distribute the good from sources such as factories, to final destinations such as retail outlets. The assignment problem involves finding the best (usually the least cost) way to assign individuals or pieces of equipment to projects or jobs on a one-to-one basis. Using Solver, we will take advantage of the special structure of these LP problems to find the best solutions to complex business problems in an efficient way.
• Integer Programming and Nonlinear Programming
• This module presents yet another subset of important mathematical linear programming models that arise when some of the basic assumptions of an LP model are made more or less restrictive. For example, restricting the decision variables to be whole numbers leads to the process of Integer Programming. Restricting the decision variables to be either 0 or 1 leads to binary programming. Lastly, we will see how the skills in this course can be used to solve more complex problems that involve nonlinear models.

Joseph W. Cutrone, PhD

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• Business Analytics with Excel: Elementary to Advanced

A leader in a data-driven world requires knowledge of statistical methods and appropriate models to analyze data. This course focuses on analytical frameworks for decision-making using Excel, covering linear and integer optimization, decision analysis, risk modeling, and more.

• Emphasizes formulating problems and translating them into useful models
• Prepares managers to interpret the output of software and appreciate quantitative decision-making approaches
• Covers basic to advanced modeling techniques, starting from Excel basics

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Course Modules

This course comprises six modules that cover Excel basics, what-if analysis, decision analysis through regression and NPV, linear programming, transportation and assignment problems, and integer programming and nonlinear programming, offering a comprehensive journey from fundamental skills to advanced modeling techniques.

Introduction to Excel: Basics and Best Practices

This module introduces Excel basics and best practices, from creating budgets to using formatting, formulas, and functions. Students will gain a solid foundation in Excel before moving on to more advanced analytical techniques.

What-If Analysis in Excel

Discover what-if analysis in Excel, including goal seeking, data tables, uncertainty, and more. Students will learn to apply these techniques to real-world business problems, such as determining break-even points and analyzing uncertainty.

Decision Analysis through Regression and NPV

Explore decision analysis through regression and net present value (NPV) calculations. Students will learn to model and evaluate decision alternatives, using regression to analyze trends and forecast future outcomes.

Linear Programming

Learn about linear programming, including campaign marketing, investment allocation, and transportation problems. Students will apply solver tools in Excel to optimize resource allocation and solve complex business challenges.

Transportation and Assignment Problems

This module covers transportation and assignment problems, including alternative Excel templates and real-world scenarios such as new hire assignments and MLB umpire assignments. Students will develop problem-solving skills for logistical challenges.

Integer Programming and Nonlinear Programming

Delve into integer programming and nonlinear programming using real-world examples like project selection, portfolio variance, and investment decisions. Students will learn to use solver tools to optimize decision-making in complex business scenarios.

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Business Analytics with Excel: Elementary to Advanced

This business analytics course focuses on the latter, introducing students to analytical frameworks for decision making via excel modeling. linear and integer optimization, decision analysis, and risk modeling are examples of these..

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Description

In this course, you will learn:

• Introduction to Excel: Basics and Best Practices
• What-If Analysis in Excel
• Decision Analysis through Regression and NPV
• Linear Programming
• Transportation and Assignment Problems
• Integer Programming and Nonlinear Programming

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Excel for Business Analysts

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Excel is popular with companies that use it for various operations such as financial reporting, business analysis, performance management and budgeting. We examine some of the basic formulae used in Excel (including ‘sum’, ‘count’, ‘average’, ‘counta’, ‘min’ and ‘max’) and provide practical examples of their use. The course delves into the difference between relative and absolute reference in cells and explains when and how to use them. We then demonstrate merging and show you how to look up data functions in Excel. 

This may appear daunting but we break each step down and you can work at your own pace. When analyzing data, you may need to combine information from multiple worksheets into one with the help of Excel functions like ‘vlookup’, ‘hlookup’, ‘xlookup’, ‘index’ and ‘match’ and we train you to use them quickly and effectively. We then investigate Excel’s ‘if’ function, which performs logical calculations to build powerful worksheets for your business data that let you compare results and values. This section includes some practical examples and implementation of ‘if’ functions in different data sets, which will enhance the value of your business reports and worksheets.

We then describe how to organize and prepare your data for business analysis. Choosing the wrong format and using inconsistent values in your data can harm your analysis so we teach you how to prepare data by ‘cleaning’ and formatting it with the help of Excel functions such as ‘concatenation’ and the ‘flash fill’ and ‘table’ commands. Mastery of these functions lets you prepare better business reports and this course provides practical knowledge of Excel to help you make sense of information to analyze data more deeply and accurately. It will help anyone working in a business environment.

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Data Analysis in Excel (A Comprehensive Guideline)

In this article, we will learn how to analyze data in Excel , including:

• Different Excel functions, such as VLOOKUP , INDEX-MATCH , SUMIFS , CONCAT , and LEN functions.
• Using Excel charts – learn how to create various chart types, customize them, and interpret the insights they offer, and how to apply conditional formatting effectively for data analysis purposes.
• Creating pivot tables, performing calculations, and generating insightful reports.
• Using Excel’s sorting and filtering capabilities.
• The What-If Analysis feature in Excel and explore different scenarios by changing input values and observing the resulting outputs.
• Implementing data validation techniques to maintain data accuracy.
• The benefits of using tables and the built-in Analyze Data feature in Excel , which provides insights and recommendations based on your data.
• Introducing the Analysis ToolPak add-in, which offers a wide range of statistical functions and tools, including descriptive analysis and ANOVA ( Analysis of Variance ).

Let’s use the following dataset as a demonstration of analyzing data in Excel.

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How to Analyze Data in Excel

Method 1 – use excel functions to analyze data.

Case 1.1 – The VLOOKUP Function

The VLOOKUP function is a frequently used function for looking up any particular data from a dataset. In the following example, we want to know how many goals an individual (for instance, Alex ) has scored.

• The formula in cell F5 is

Here, Excel is looking for the value in cell E5 within the range B5:C14 and retrieving the corresponding value from the second column of that range.

Case 1.2 – INDEX and MATCH Functions

• The formula in this case is:

Formula Breakdown

MATCH(E5, B5:B14, 0) → The MATCH function searches for the value in cell E5 within the range B5:B14 . The 0 as the third argument indicates an exact match. Output: 1

Case 1.3 – The SUMIFS Function

The SUMIFS function gets the sum of a range of cells with a set of conditions.

• If you want to get the goals scored by the players from Group A and Group B separately, the formula you can use in cell G5 is:

The formula sums the values in the range $D$5:$D$14 but only includes values where the corresponding cells in the range $C$5:$C$14 match the value in cell F5 .

Case 1.4 – The CONCAT Function

Let’s join the first and last names of certain individuals here using the CONCAT function in Excel .

• The formula in cell D5 is:

The formula joins the values in cells B5 and C5 , with a space between them, resulting in a single combined text string.

Case 1.5 – The LEN Function

You can count the number of characters of a cell or an array using the LEN function .

The formula in cell E5 is:

Method 2 – Data Analysis Using Excel Charts

• Select the range F4:G6.
• Go to the Insert tab and select any column chart .

• Excel will create a column chart for you.

Method 3 – Apply Conditional Formatting to Analyze Data

• Select the dataset in the range C5:C14.
• Go to the Home tab and choose Conditional Formatting, then select a set of Data Bars .

• Excel will add data bars.

Method 4 – A Pivot Table

Let’s calculate the number of goals scored by Group 1 and Group 2 players using the Pivot Table .

• Select the dataset in range B4:B14.  
• Go to the Insert tab and select PivotTable .

• A box will appear. We have chosen a New Worksheet as the destination of the Pivot Table .

• Drag the fields in the areas ( Group in Rows and Goal in Values ) shown in the image.

• Excel calculates the sum of goals.

Method 5 – Sorting Data in Excel

Suppose you want to sort the dataset in a descending order ( Largest to Smallest ).

• Select the range C5:C14.
• Go to the Data tab and select the Sort Z to A icon for descending order.

• Select Expand the selection option from the warning window.

• Your data will be sorted.

Method 6 – Filtering Data in Excel

Suppose you want to see the performance of the players of Group A .

• Select range B4:D14 .  
• Go to the Data tab and activate the Filter  option.

• Filter your dataset from the drop-down icon in the column heading. We have selected Group A in the Group column.

• Excel will get the list of all Group A players and their performance.

Method 7 – Excel What-If Analysis Feature

What-If Analysis in Excel refers to a set of tools and techniques that allow you to explore different scenarios and observe the potential impact on the results of your formulas or models. Excel provides several features for performing what-if analysis, including:

• Data Tables: Data Tables allow you to create a table displaying multiple results based on input values. You can perform either one-variable or two-variable data tables to see how changing inputs affect the final results.
• Goal Seek: Goal Seek helps you determine the input value needed to achieve a specific result. You specify a target value, and Excel automatically adjusts the input value until it reaches the desired outcome.
• Scenario Manager: Scenario Manager enables you to create and compare different sets of input values for your model. You can define multiple scenarios with varying inputs and switch between them to see the impact on the calculated results.

We will show an example of the Goal Seek feature. Suppose you have 100 units of a product to be sold. You want to see the necessary unit price if you want to get a revenue of $200 .

The formula in C6 is:

This is very simple as we all know that the unit price will have to be $2 . However, the fun with this Goal Seek feature is that you do not have to manually put the unit price. Rather, Excel will find it for you.

• Go to the Data tab and select What-If Analysis , then select Goal Seek .

• You want the revenue ( To value ) to be $ 200 and get the unit price in cell C5 . So, the Set cell is C6 and the cell for By changing cell is C5 . Put those values in the dialog box and click OK .

• Excel will put the unit price in C5 . Put the Revenue in the currency format if you want.
• Modify the Units Sold value and repeat the process to see how it affects the result.

Read More: How to Perform Case Study Using Excel Data Analysis

Method 8 – Data Validation

Let’s get back to our previous example (from the VLOOKUP section). We want to select a player’s name from all the available options rather than manually typing their names.

• Select cell E5.
• Go to the Data tab and select the Data Validation  option.

• A Data Validation box will pop up. Choose List in the Allow field.
• Set the source to =$B$5:$B$14 .

• You can now select the names from the drop-down  icon.

• Once you select a name, you will get the number of goals the player scored.

Method 9 – Excel Table

• Select the dataset in range D5:D14.
• Press CTRL + T.

• Excel will create a table.

Let’s see how you can get the total goals scored by these players without using any Excel Function .

• Click on any cell of the table.
• Go to the Table Design tab (this tab will be seen only if you select a cell of the table first).
• Select Table Style Options and check the Total Row  box.

• Excel shows the total goals scored.

Read More: How to Analyse Qualitative Data from a Questionnaire in Excel

Method 10 – The Analyze Data Feature

• Add this feature to your ribbon. Put the cursor on the Home ribbon and right-click, then select Customize the Ribbon .

• Select New Group and set its position on the Home ribbon.
• Select All Commands and add Analyze Data to this newly created group.

• Go to the Home tab and select Analyze Data .

• Excel will recommend several options for data analysis.

Method 11 – Using the Analysis ToolPak Add-in

• Go to the File tab and select Options . The Excel Options box will open.
• Go to Add-ins and select Excel Add-ins in the Manage field, then click Go .

• Check the box for Analysis ToolPak  and click OK .

• Let’s do some analysis using this add-in.

Read More: How to Convert Qualitative Data to Quantitative Data in Excel

Descriptive Analysis with the ToolPak

• Select range C5:C14.
• Go to the Data tab and select Data Analysis (This will be available once you activate the Analysis ToolPak add-in).

• A Data Analysis box will pop up. Select the Descriptive Statistics option and click OK .

• Set the input range and the output range and click OK . Check Summary statistics .

• You will get the descriptive statistics of the selected input range in your Excel  workbook.

Read More: How to Make Histogram Using Analysis ToolPak

ANOVA Analysis in Excel with ToolPak

ANOVA stands for Analysis of Variance . It is a statistical method used to compare the means of two or more groups to determine if there are any significant differences between them.

• Go to the Data tab and select Data Analysis .
• Select ANOVA from the Data Analysis box and click on OK .

• Set the input and output ranges.

• Excel will perform the analysis for you.

Read More: How to Analyze Data in Excel Using Pivot Tables

Things to Remember

• Data Validation ensures accuracy.
• The INDEX-MATCH function is better than the VLOOKUP  function.
• You need to refresh the Pivot Table when you change your dataset.

Frequently Asked Questions

1. What are the advantages of using the Analyze Data feature in Excel over manual analysis techniques?

Advantages of using the Analyze Data feature in Excel over manual analysis techniques include saving time by automating tasks, an easy-to-use interface, lots of helpful tools and functions, the ability to customize, and working well with other Excel features.

2. What is the difference between descriptive and inferential statistics?

Descriptive statistics help describe data by summarizing it while inferential statistics help make predictions about a larger group based on a smaller sample.

3. What are the uses of ANOVA?

ANOVA is used to compare the averages of different groups, see how categorical variables affect outcomes, analyze experiments, and understand different sources of variation in data.

Analyze Data in Excel: Knowledge Hub

• How to Install Data Analysis in Excel
• How to Use Data Analysis Toolpak in Excel
• How to Enter Data for Analysis in Excel
• How to Use Analyze Data in Excel
• [Fixed!] Data Analysis Not Showing in Excel
• How to Analyze Raw Data in Excel
• How to Analyze Large Data Sets in Excel
• How to Analyze Text Data in Excel
• How to Analyze Time Series Data in Excel
• How to Analyze Sales Data in Excel
• How to Analyze Likert Scale Data in Excel
• How to Analyze qPCR Data in Excel
• How to Analyze Quantitative Data in Excel
• How to Analyze Qualitative Data in Excel
• Organize Data in Excel: A Complete Guide
• Rearranging in Excel
• How to Add Tags in Excel?
• How to Summarize Data in Excel

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Introduction to modern analytics using Excel and Power BI

Learn how Microsoft Office 365/Microsoft Excel 2016+, combined with Microsoft Power Query, Data Model, Power BI Desktop, and Power BI service come together to provide Microsoft modern analytics capabilities.

Learning objectives

In this module, you will:

• Learn about modern analytics.
• Discover the benefits of the Microsoft modern analytics technology suite of tools. Including Office 365/Excel 2016+, Power Query, Data Model, Power BI Desktop, and Power BI service.
• Learn about the ecosystem of modern analytics and the roles of people in your organization who use the technologies.
• Install and update modern analytics applications.

Prerequisites

Office 365/Excel 2016+

• Introduction min
• Benefits of modern analytics min
• Ecosystem of modern analytics min
• Roles of modern analytics min
• Install and update modern analytics applications min
• Check your knowledge min
• Summary min

Year-Over-Year Growth Charts: A Comprehensive Guide

Published: August 26, 2024 - 6 min read

Are you struggling to effectively track and visualize your company’s performance over time? Year-over-year (YOY) growth charts are powerful tools for analyzing business trends and making data-driven decisions. This comprehensive guide will walk you through creating, understanding, and leveraging YOY growth charts in Excel.

Step-by-Step Guide: Creating YOY Growth Charts in Excel

Now that we understand the basics, let’s create a YOY growth chart in Excel using PivotTables and PivotCharts.

Preparing your data

• Open Excel and create a new worksheet.
• Date (in a format Excel recognizes as dates)
• Metric (e.g., Sales, Revenue, Units Sold)
• Enter your data, ensuring each row represents a single data point. For example:

Using PivotTables and PivotCharts

• Select your data range.

• Go to Insert > PivotTable.

• In the Create PivotTable dialog, ensure your data range is correct and choose where to place the PivotTable. Click OK.

• Drag ‘Date’ to the Rows area.
• Drag ‘Metric’ to the Filters area (if you have multiple metrics).
• Drag ‘Value’ to the Values area.

• Right-click on any date in the PivotTable and select Group  > Years  & Quarters .

• In the PivotTable, right-click on the ‘ Sum of Value ‘ cell and select ‘ Value Field Settings ‘.
• In the ‘Show Values As’ tab, select ‘% difference from’ and choose ‘(previous year)’ in the Base field. Click OK.
• Select your PivotTable and go to Insert  >  PivotChart .

• Choose a line chart type and click OK.

Formatting and customizing your chart

• Add a title : Click on the chart title and enter “Year-over-Year Sales Growth”.

• Format the Y-axis : Right-click on the Y-axis, select ‘ Format Axis ‘, and set the ‘Number’  format to ‘Percentage’.

• Adjust colors : Click on the data series and choose a color that matches your brand or preferences in the sidebar.

• Add data labels : Right-click on the data series, select ‘Add Data Labels’, and format them to show percentages.

• Remove gridlines : Select gridlines and press Delete for a cleaner look.

• Add a legend : If comparing multiple metrics, ensure the legend is visible and clearly labeled.

Your chart should now clearly show the YOY growth percentage for each quarter across different years.

Advanced YOY Growth Analysis Techniques

To gain deeper insights from your YOY growth data, consider these advanced techniques:

Comparing multiple metrics

• In your PivotTable, add another metric to the Values area.

• Adjust the Value Field Settings for each metric as needed.
• In your PivotChart, you’ll now see multiple lines representing different metrics.

This allows you to compare, for example, YOY growth in sales versus profit margins.

Analyzing seasonality and trends

• Create a line chart showing absolute values alongside YOY growth percentages.
• Look for recurring patterns in specific months or quarters to identify seasonal trends.
• Right-click on your data series
• Select ‘ Add Trendline ‘

• Choose the appropriate trend type (linear for steady growth, polynomial for more complex patterns)

  Understanding Year-Over-Year Growth Charts

Year-over-year growth charts are visual representations of how a specific metric changes from one year to the next. These charts help businesses track performance, identify trends, and make informed decisions based on historical data.

What is a YOY growth chart?

A YOY growth chart compares data points from the same period across different years. For example, it might show sales figures for Q1 across multiple years, allowing you to see how Q1 performance has changed over time.

Importance of tracking YOY growth

Tracking YOY growth is crucial for several reasons:

• Performance evaluation: It provides a clear picture of how your business is progressing over time.
• Trend identification: YOY charts help spot long-term trends that might be obscured by short-term fluctuations.
• Goal setting: Historical YOY data informs realistic future growth targets.
• Seasonal adjustments: By comparing the same periods year-over-year, you can account for seasonal variations in your business.

Common use cases for YOY analysis

YOY growth charts are versatile tools used across various industries and departments:

• Sales teams use them to track revenue growth and identify successful products or regions.
• Marketing departments analyze the effectiveness of campaigns over time.
• Finance teams use YOY charts for budgeting and forecasting.
• HR departments track employee retention and recruitment metrics.
• Operations teams monitor efficiency improvements and cost reductions.

Choosing the Right Chart Type for YOY Growth Visualization

Selecting the appropriate chart type is crucial for effective data visualization. Let’s explore the most common options for YOY growth charts.

Line charts

Line charts are excellent for showing trends over time and are particularly useful for YOY growth visualization.

• Clearly show trends and patterns over multiple years
• Easy to compare multiple metrics on the same chart
• Work well with continuous data
• Can become cluttered with too many data series
• May not be ideal for showing exact values

Bar charts are versatile and can be used effectively for YOY comparisons, especially when dealing with discrete categories.

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• Easy to read and compare specific values
• Work well for comparing a small number of years or categories
• Can be arranged vertically or horizontally
• Can become overwhelming with too many categories or years
• May not show subtle trends as clearly as line charts

Dual-axis charts

Dual-axis charts combine two chart types (usually a line and a bar) to display related metrics with different scales.

• Allow comparison of metrics with different magnitudes
• Can show both absolute values and growth rates on the same chart
• Provide a comprehensive view of performance
• Can be confusing if not designed carefully
• Require careful scale selection to avoid misrepresentation

Choosing the best chart type

Consider these factors when selecting a chart type:

• Number of data points: Line charts work well for many data points, while bar charts are better for fewer comparisons.
• Type of comparison: Use bar charts for side-by-side comparisons and line charts for trend analysis.
• Audience: Choose simpler charts for general audiences and more complex visualizations for data-savvy stakeholders.
• Story you want to tell: Select a chart type that best highlights the insights you want to convey.

Year-Over-Year Growth Charts in Excel

Mastering year-over-year growth charts in Excel is a valuable skill for any data-driven business professional. By understanding the principles behind YOY analysis, choosing the right visualization techniques, and following best practices, you can turn raw data into actionable insights that guide your company’s strategy and growth.

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How to build customized Power BI dashboards with user insights data in External ID

Sharon Rutto

August 27th, 2024 0 1

The user insights feature is generally available (GA) in Microsoft Entra External ID external tenants. It is accessible via Microsoft Graph APIs, which are currently in beta, or through prebuilt dashboards in the Microsoft Entra admin center. User insights dashboards provide organizations with valuable insights into user behavior and patterns within customer-facing applications. You can query and analyze user metrics such as total user count, monthly active users (MAU), daily active users (DAU), newly added users, authentications, and multifactor authentication (MFA) usage.

Custom dashboards with Microsoft Graph API

The out-of-the box dashboards in the Microsoft Entra admin center provide easy-to-digest graphs and charts but have limited customization options. Microsoft Graph APIs enable you to build powerful, customized dashboards with data tailored to your specific needs and preferences. This has some advantages:

• Flexibility: You can integrate with other data sources to present your data in a way that aligns more with your business objectives.
• Enhanced visualization: You can have richer and more interactive visual representations of your data.
• Complex query handling: You can apply advanced filters, aggregations, and calculations to your user insights data and get more granular and accurate results.

User insights APIs are summarized into daily and monthly data, offering varied insights tailored to different needs:

• Daily APIs: Monitor daily fluctuations in user activity; perfect for recognizing immediate changes, tracking sudden spikes in requests and authentications, or evaluating the impact of daily marketing campaigns and product updates.
• Monthly APIs: Gain a broader understanding of user behavior trends and retention across extended intervals, beneficial for gauging the success of long-term strategies and initiatives.

Example dashboards you can build

• Seasonal trends: Visualizing seasonal trends can help in strategic planning and forecasting. For example, heatmaps showing user activity, sign-ups, or authentications by day of the week or month, can help identify high-traffic periods. This dashboard can help identify patterns and trends in user activity, enabling more effective resource allocation.
• Anomaly detection: This dashboard can show the number and frequency of unusual or suspicious events in your applications, such as failed sign-ins, sign-ups from unknown locations, or spikes in user activity. You can use this dashboard to monitor application security, troubleshoot issues, and respond to incidents.

Build your own user insights dashboard

Let’s explore how to build a customized Power BI dashboard using user insights Microsoft Graph APIs.

Prerequisites

• External tenant – To access user insights data, you must have an external tenant. If you already have an Azure subscription, use this quickstart to create an external tenant. If not, you can sign up for a 30-day free trial here .
• Registered app(s) – User insights collects and aggregates data from your customer-facing applications. Ensure you have at least one registered app with sign-in and/or sign-up activity.
• Power BI – For the purposes of the example in this blog post, we will use Power BI to visualize the data. You can use Power BI desktop or Power BI service . Alternatively, you can choose any other analytical tool you prefer.

Setting up External ID

• Confirm you’re in your external tenant – In Microsoft admin center , go to Identity > Overview > Manage tenants . Confirm your external tenant is the current tenant you’re in.

Register an app for authorization – To securely access Microsoft Graph APIs, you need to register an app that will be used to generate access tokens for authorization. Go to Identity > Applications > App registrations to create one as outlined here .

Configure API permissions for Microsoft Graph – Add the necessary API permission Insights-UserMetric.Read.All to the registered app from step 2. Follow the instructions provided here . Keep in mind that the access token you generate will only be valid for one hour. To manage this, you can create a function in Power Query to check for token expiration and automatically refresh it.

Creating a Power BI report

Once you have successfully set up your tenant, you can now create a Power BI report using custom connectors to fetch user insights data. Here’s how you can connect Power BI to Microsoft Graph and build your report .

Transforming and visualizing data

Power BI comes with Power Query Editor that can help you clean and shape your data. You can remove unnecessary columns, handle missing values, and apply transformations such as merging, grouping, filtering, and many more.

• Transform and model your data – Once you have pulled all the data you need from the user insights APIs, transform and model your data to suit your needs. Go to Home > Transform data to use Power Query Editor.

• Visualize your data – Build engaging reports and dashboards once your data is ready for use. Power BI offers a range of visual elements to help you represent your data effectively. The example below shows a summary of user activity with daily and monthly growth trends.

Let’s recap

In this blog post, we explored how to build customized Power BI dashboards using user insights data in Microsoft Entra External ID. We went through accessing user insights data via Microsoft Graph APIs, setting up an external tenant and registered app, and using Power BI to connect to Microsoft Graph and build a report. We covered how to transform and visualize data in Power BI, enabling you to create insightful dashboards that can improve decision-making and security monitoring in customer-facing applications.

To test out other features in the Microsoft Entra portfolio, visit our developer center . Sign up for email updates on the Identity developer blog for more insights and to keep up with the latest on all things Identity, and follow us on YouTube for video overviews, tutorials, and deep dives.

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This document covers the most significant duties performed but does not exclude other occasional work assignments not mentioned.