Introduction: In this blog we will explore some of the new features added to Power BI. Power BI has upgraded its Q&A Experience in its latest April release. It has simplified and simultaneously improved the natural language recognition process which is one of Power BI’s most powerful tools for Query Processing! Some of Q&A Explorers cool new Features: You can now add a simple image, shape or button which on being click can launch a Q&A Explorer! You just need to toggle the Q&A option on under Action for the particular image/shape/button. Adding a Q&A button can look something like this. On clicking on this newly created Q&A Explorer a dialog appears where the user can ask questions to generate dynamic visuals. To learn more about this feature you can view my previous blog on Natural Language Processing over here. You can add suggested questions which will show on the left side of the dialog when a user clicks on the Q&A button. When you click on Save and close these newly added Suggested Questions will get saved to this specific Q&A button. The Q&A Explorer can also return whole reports now when you search specific keywords. You can do this by going to a particular report and turning it’s Q&A Feature on in Page Information. Searching these keywords in the Q&A Explorer will return this particular report. Optionally, if you have page level filters then you can set Require single selection On for a particular filter. This filter will then be shown in the Dialog while searching for the queried report. Conclusion: These are some of the latest features added to Power BI’s arsenal. Q&A Explorer is an underused tool but if used correctly it can improve your interactive experience with your reports tremendously.

Introduction: Deployment of Power BI to Dynamics 365 for Finance and Operations is done by Embedded Power BI in Dynamics 365 for Finance and Operations. Configurations of Power BI in operations: Configure your LCS project within Dynamics 365 for Operations Navigate to System Administration –> System Parameters –>Go to Help Tab Here you will be asked to Connect to Life Cycle services. This operation is mandatory, it enables Dynamics 365 for Operations to established a trusted connection to LCS using your user credentials. Click on “Click here to connect to Lifecycle Services” On successful connection, you will be able to choose a set of LCS projects from the drop down menu. Select the LCS project Enable Power BI: Register Dynamics 365 for Operations deployment as an web app. 1. Login to you Power BI account 2. There are some fields we need to fill in: AppName (e.g. “D365PBI”) AppType (Server-side Web app) Redirect URI (this will be your instance URL with “oauth” at the end. E.g https://D3651611aos.cloudax.dynamics.com/oauth) Home Page URL (This will be your instance URL. E.g https://D3651611aos.cloudax.dynamics.com/) 3. Choose APIs to access 4. Then hit “Register App”. This will generate a Client ID and a Client Secret which we are going to input inside D365. 5. Keep this window open, we need to copy paste the keys into D365. Deploy Power BI Files: Navigate to System Administrator –>Deploy Power BI Files .Click on Deploy Power BI Files Here you will be asked to Authorize Power BI, Click on Authorize Power BI. Click on Deploy Power BI Files

Introduction: Azure Machine Learning Studio is an amazing tool that lets us create efficient ML experiments with simple drag and drop features. We can predict anything from Flight Predictions to Churn Analysis. But what if we want to represent this predicted data a more visually appealing format? Well it is possible to do this by representing your predictions on Power BI! Pre-Requisites: Basic Understanding of Azure Machine Learning Studio. Basic Understanding of Power BI. A Blob Container created on Azure Storage.   Steps: Create your Azure Machine Learning Experiment on Azure Machine Learning Studio. Convert your Training Experiment to a Predictive Experiment and Deploy it as a Web Service. We will create a Console application in Visual Studio and copy paste the code inside Batch Execution. For automation we can create automated data pipelines but for now we will just use a simple Console application. Remove the existing code from the Console Application and copy paste the Batch Execution code. Install the necessary Nuget Packages and also update the following parameters. – BaseURL will be the same. – Storage Account Name, Storage Account Key and Storage Container Name will be parameters that can be found in your Azure Blob Storage which was created. – Api Key can be found in the Web Experiment Page in Azure Machine Learning Studio. – The input path is the path where you have saved your input csvfile for Batch Execution. Your Input csv file should have all the features which you have used to train your experiment After you run your Console application a new output1results.csv file should get generated in your Blob Container. The output results should include the labels which your experiment generates in it’s output. It should include the Scored Labels and Scored Probabilities labels as well. Now you can get your data using Azure Blob Storage as your source in Power BI and use the columns in the output1result.csv file to generate your ML Predicted Reports. The Report can look something like this. I hope this blog helps you to combine Azure Machine Learning Studio and Power BI to create a powerful predictive solution.

Introduction: Narratives for Power BI is a product that automatically delivers dynamic narratives that explain the insights within your data. No more manually writing explanations and spending time interpreting data. Instead, the narratives, which are powered by advanced analytics, are perceptive and dynamic and explain what is most interesting and important in your data. Drill down deeper into your data and watch narratives update in real-time during the data discovery process Steps: Go to powerbi.narrativescience.com and enter your business email id.  A link for downloading the extension and installation instructions will be mailed to you A pibiviz file will be downloaded on downloading the extension. A pbiviz file is nothing but a custom visual which can be imported in Power BI Desktop. Import the file on Power BI Desktop Benefits: Automated Narratives generated that give more detailed insights about the report which may not even be obvious Real time update on interaction with data Many customization options to personalize your narrative Click on Narrative and select Dimensions and Values based on which Narratives will be generated. After selecting the fields you will have to select your narrative type. Discrete: For distinct data like that in Bar Charts Continuous: For continuous data like that in Line Charts Percent of Whole: For data by percentage like Pie Charts Scatterplot: For data based on scatterplot like Charts A narrative gets generated It also changes on real time interaction The type structure and verbosity can be customized in the Format Pane Type Can be Discrete, Continuous, Percent of Whole or Scatterplot. Structure can be either in Paragraph format or Bullet Points. Verbosity the level of information displayed. Low verbosity would show less detailed narrative with high level information while High verbosity would show a very detailed narrative. Medium verbosity would be a midway between both. I hope this blog encourages you to use this powerful extension to improve your reports by making it as detailed as possible with minimalistic efforts!  

The CALCULATE function in DAX is the magic key for many calculations we can do in PowerPivot. Below is the syntax: CALCULATE( <expression>, <filter1>, <filter2>… ) The expression that we put in the first parameter has to be evaluated to return the result (i.e. a value, not a table). For this reason, the expression is usually an aggregation function like SUM, MIN, MAX, COUNTROWS and so on. This expression is evaluated in a context that is modified by the filters. A key point is that these filters can both enlarge and restrict the current context of evaluation. Let’s try to understand what it means by considering a few examples. The following data model we have imported in PowerPivot named ‘Contract’ & ‘Project’ Scenario 1 Compare Contract & Project data model on YearMonth Column and take sum of multiple records of revenue column of Project data model into Contract data model Project data model has StartYM & StartRevenue Column as shown below And Contract data model has YM column, using Project data model StartYM, StartRevenue columns & Contract data model YM column, here we have derived StartR column with the help of Calculate() DAX function as shown below Formula is =calculate(sum(Project[StartRevenue]),filter(project,Project[StartYM]=Contract[YM])) Scenario 2 Calculate running total of ToDo column in ‘Contract-ToDo’ data model on basis of YearMonth column as shown below Formula is =calculate(sum(‘Contract-ToDo'[ToDo]),filter(‘Contract-ToDo’,’Contract-ToDo'[YearMonth]