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: In this article, we will learn how the Filter changes its default values during Parameter selection in SSRS Reports with Examples. Scenario: I have 4 parameters in my report: Start Date: Default is today’s Date End Date: Default is today’s Date + 60 Days Department: No Default value. Project: Dependent Filter on Department. Once the Department is selected, default all projects will be selected. Filter Working: Below listed is various test cases which shows how the Select ALL in Project Filter changes according to other 3 parameters. Default Filter: Start Date: Current Week Start Date End Date: Today’s Date+ 60 days Department: Select all manually Project: Disabled Results: All projects will be automatically displayed and selected based on dates and Department. Selecting wide range of Dates after Initial run (After Step 1): Start Date: less than Current Week Start Date or Current Week Start Date End Date: Greater than Today’s Date+ 60 days Department: Select one manually Project: Not all the Projects will be selectedResults: All projects between the date range will be displayed but the projects after today’s date + 60 will be un-selected. Selecting more Department (After Step 2): Start Date: Any End Date: Any Department: Select two department Project: Not all the Projects will be selected Result: All projects between the date range and department will be displayed but the projects in newly selected department will be un-selected. In this example, projects in Assurance Department will not be selected. Conclusion: Dependent filter (Project Filter) will be disabled initially. Dependent Filter will be displayed and open once all the other filters are selected. Dependent Filter will change based on the other Filters change. After initial run, any change in other filter will control the behavior of Dependent Filter. Selecting Wide Range initially and then reducing the Range will keep the Dependent Filter as Select All. E.g.: Decreasing the Date Range or Selecting less number of department selected initially. Selecting a range initially and then increasing the range will remove Select All in Dependent Filter (Only the initial range Project will be selected). This the default SSRS behavior. E.g. Increasing the Date Range or Selecting more departments selected initially. EXCEPTION: If you increase the Date Range and then Reduce the Department Selected, All Projects will be selected by default.

Introduction: In this blog, we try migrating Email Template records under Email Marketing module in ClickDimensions. ClickDimensions has many modules like Email Marketing, Analytics, Event Management etc. Under Email Marketing Module, we have entities like Email Template, Email Sends, Sent Emails, Unsubscribes and so on. While trying to migrate all the Email Templates from D365 v8.2 to v9 using TIBCO Cloud Integration, we encountered an issue. We could not find any field or entity that stored the HTML code which formed the body of the Email Template. The reason behind this issue is the HTML code is stored on the ClickDimensions side and not on ours. Therefore, in order to migrate Email Templates successfully, we need to use the Import/Export options in ClickDimensions Settings. Steps: 1. In your Source environment, go to Settings and click on ‘ClickDimensions Settings’. 2. Click on ‘Export’. 3. Select the entities you want to export and click on ‘Next’. 4. Click on ‘Export’. A .zip file will be downloaded. 5. Now, go to your Target Environment and click on Settings -> ‘ClickDimensions Settings’-> ‘Import’. 6. Click on the ‘Upload ZIP File’ button and select the file you just downloaded from the Source Environment. Click ‘Import’. Conclusion: The Email Templates will now be present in the Target System. This process did not change the GUID of the record, which is important to note as it may be required further on in the migration process. I hope this blog contributed to clearing things up when it comes to migrating Email Templates in ClickDimensions. I will be adding more blogs about migrating records in other entities of Click Dimensions soon. Stay tuned!

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: The feature is finally release, and it is power BI has announced general availability of persistent filters in the Power BI service. All Power BI reports will now automatically retain the filters, slicers, and other data view changes that you have made. You no longer need to spend your valuable time slicing and dicing your report and repeating the same steps each time you return to the service. With this feature, you will be able to pick up right where you left off last time and quickly get to your insights! Also, if you want to reset all the filter to the state when the report was publishing, this feature will allow you to do that. Enabling Persistent Filter: To see persistent filters in action, simply head to any Power BI report that you have view or edit access to. You will notice a new button on the top bar that says, “Reset to default”. By default, this is disabled. It essentially means that you are viewing the author’s published view of the report and have not made any changes. Once you have modified the report to the view that you like, will activate the Result to default button, and will allow you to go back to default state when it was published. Click on reset to default And you are done.  Report will be reset to the default published state. Disabling persistent Filter for report: Persistent filter is turned on by default for all reports. If you want to disable the feature for the report, then you need to use the latest power BI desktop version that is released on Feb 2018. You need to navigate to : Power BI Desktop > Options > Report Settings > Persistent Filter. This is amazing feature that allows users to interact with report as they want. Try it out and let us know if there is any issue in comments below.

Introduction: In this blog, I will outline how to migrate Email Attachments and update the status if an email. In my previous blogs, I have shown how to migrate the body of an Email and its Activity Parties from one CRM to another using Scribe. Email Attachments: Below, is the map used to migrate Email Attachments. As you can see, it is pretty straightforward, barring a few things to keep in mind while mapping. 1) Email Attachments are stored in the ‘activitymimeattachment’ entity. 2) I did not map the ‘attachmentid’ field as it produced an error as well as there is probably no reason one would need the GUID of the attachment. Not mapping attachmentid will create a new GUID for the attachments being migrated. 3) Most data regarding the Attachments migrated along with the first map migrating ‘Email’ activity. 4) That is why, in this map, we just migrate the subject, filename and body fields along with ‘objectid’ and ‘objecttypecode’. 5) The ‘objecttypecode’ tells which entity the attachment belongs to and its GUID. Once you run the map successfully, you will see the attachments displayed in the email. This includes image attachments as well. Target: Email Status Update: As for most Activity entities, while migrating, we migrate with an ‘Open’ status. This is done to ensure the record does not become read-only which would not allow us to migrate the corresponding Activity Parties and Attachments. This could lead to an inconsistency in data in Source and Target. Once the Activity Parties and Attachments have been migrated to the record, we can now update the Status of the Email to what it is in the Source environment. This is a basic but fundamental step to ensure no data inconsistency. Sample State Code & Status Code Values: In this map, all we have to map are the ‘Status Code’ and ‘State Code’ as it is in the Source Environment. This will update the status of the email. In the screenshot below, you can see that the Status has been updated to ‘Sent’. Conclusion: This completes the process of creating TIBCO Cloud Integration Maps for Email Migration from CRM to another. I hope this and my two preceding blogs provide a sufficient outline for the process of Email Migration.

Introduction: In this blog you will see how you can use power BI as existing dataset to create the report in Power BI desktop. Service Live Connection: You can establish connection to a shared dataset in the Power BI service, and create many different reports from the same dataset. This means you can create your perfect data model in Power BI Desktop, publish it to the Power BI service, then you and others can create multiple different reports (in separate. pbix files) from that same, common data model. This feature is called Power BI service Live connection. To create Shared data set you need to, create a dataset and report and publish it in a workspace which is common to all. Select the workspace that is shared and where the report needs to be deployed. Report will start to Puclish to workspace. You will get below confirmation, when the report is sucecssfully published. Establish a Power BI service live connection to the published dataset If you’re not signed in to Power BI, you’ll be prompted to do so. Once logged in, you’re presented with a window that shows which workspaces you’re a member of, and you can select which workspace contains the dataset to which you want to establish a Power BI service live connection. Click on load and the dataset will be loaded and you can create the reports and publish it. Below are some known limitations to this as well: Read-only members of a workspace cannot connect to datasets from Power BI Desktop. Only users who are part of the same Power BI service workspace can connect to a published dataset using the Power BI service live connection. Users can (and often do) belong to more than one workspace. Try it out and put you question below if there is anything.

Introduction: Microsoft released a PDF cheat sheet of which machine learning algorithms can be used on Azure Machine Learning Studio. This Microsoft Azure Machine Learning Algorithm Cheat Sheet helps you choose the right machine learning algorithm for your predictive analytics solutions from the Microsoft Azure Machine Learning library of algorithms. The algorithms have been grouped in 5 different groups. These groups are: Regression: For predicting values. For Example when predicting a stocks price. Anomaly detection: For finding unusual data points. For example, any highly unusual credit card spending patterns which deviates from the normal credit card spending patterns. Clustering: The data points have no labels associated with them. Instead, the goal of an unsupervised learning algorithm is to organize the data in some way or to describe its structure. For example, discovering companies with similar marketing strategies. Two-class classification: When there are only two choices, it’s called two-class or binomial classification. For example distinguishing between a Cat or Dog. Multi-class classification: For predicting three or more categories. For Example predicting the winner of a Race. To read the cheat sheet, read the path and algorithm labels on the chart as “For <path label>, use <algorithm>.” For example, “For speed, use two class logistic regression.” Sometimes more than one branch applies. In this case it is better to create scored models with both the algorithm and compare both of their accuracy to decide which algorithm is the better fit. Even a beginner can easily use the cheat sheet provided to select which algorithm is apt for creating their predictive solution. There are some generalizations and oversimplifications, but it points you in a safe direction. It also means that there are lots of algorithms not listed here but these many algorithms are more than enough to give you a good head start in the ML world.

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: In latest Power BI update, Microsoft introduced new feature as Multi-select data elements on visuals. In Power BI Desktop, we can highlight a data point on visuals by simply clicking on the data point in the visual. Means, if we have an important bar chart, and we want other visuals on the report page to highlight data based on our selection, we can click the data element in one visual and see results reflected in other visuals on the page. This is basic. Till now we are only able to filter the data on single-select highlighting. See below screen capture: New feature: But by using the new feature of the Power BI we can multi-select data element on visuals, we can now select more than one data point in Power BI Desktop report page, and highlight the results across the visuals on the page. To multi-select data points in visuals, simply use CTRL+Click to select multiple data points. See below screen capture for multiple data points select on visual(multi-select):