Microsoft Fabric: Use Copilot to Generate Data Model Synonyms

One of my older posts explains how to enable Copilot on Fabric and how to use Copilot to generate Power BI reports. In this post, I aim to explain yet another use case for Copilot that can help us to make a better and more useful semantic model in Power BI using synonyms. In an old post published in May 2016, I explained how to use Power BI synonyms to take our Power BI Q&A experience to another level. In that post, I explained how we could use synonyms to translate data model objects in different languages so the end-user could ask questions in their native language and get the results in Power BI. That was such a cool use case for synonyms, I suppose, wasn’t it? Fast track to December 2023, I believe the Q&A is still one of the coolest Power BI features that stands out when demoing the solutions to the customers; therefore, it makes absolute sense to use synonyms to improve the Q&A‘s efficiency and accuracy. This blog post explores the possibility of using Copilot to define synonyms in Power BI Desktop.

Prerequisites

As explained here, we first need to enable Copilot on Fabric Service. Please note that the technique explained in this post requires either Power BI Premium Capacity or at least F64 Fabric capacity and won’t work on PPU, Embedded capacities, Fabric capacities smaller than F64 or Fabric Trial (FT) capacities.

We also need to have the latest version of Power BI Desktop installed on our machine. With that, let’s begin.

Using Power BI Copilot to generate synonyms

While defining synonyms for the semantic model objects significantly helps with the Q&A experience, it is still a cumbersome process if done manually. So, if we meet the prerequisites, we can summon Copilot to the rescue. Follow these steps after opening a Power BI file in Power BI Desktop:

Ensure you’re signed into Fabric service with your account
Click the Insert tab
Select the Q&A visual
On the Q&A visual, click the Q&A Setup button shown with a gear icon
On the Q&A Setup window, you must see a message offering to “Improve Q&A with synonyms from Copilot” on top of the window; click the Add synonyms button

The following image shows the preceding steps:

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In Nov 2023, Microsoft announced Microsoft Fabric’s general availability and Public Preview of Copilot in Microsoft Fabric. In a previous post, I explained what Copilot means to Power BI developers, which is valid for other Fabric developers such as data engineers and data scientists as Copilot for Fabric helps with those experiences as well. But the main focus of this blog post is to discuss the requirements, how to enable Copilot, and how to use it from a Power BI development point of view. So, this blog will not discuss other aspects of Copilot in Microsoft Fabric. With that, let’s begin.

Requirements

Right off the bat, Copilot is only available on Power BI Premium capacities or their equivalent Fabric capacities. So, NO it is NOT available on Power BI Pro or Premium Per User or Power BI Embedded Analytics. So the Power BI items you want to use Copilot on must be in a Workspace assigned to a Power BI Premium P1 or Microsoft Fabric F64 capacities or higher.

You also need to have a Contributor role on the premium workspace.

To use Copilot, your Microsoft Fabric Administrator must enable it from the Fabric Admin Portal. This setting is not available in all regions yet, but Microsoft is gradually rolling it out to more regions.

Useful links:

This page explains Azure AI services‘ regional availability.
This page describes how to find your Fabric home region.

Enabling Copilot on Fabric Admin Portal

As mentioned before, your Fabric Administrator must enable Copilot features within the Admin Portal. Follow these steps to enable Copilot on your tenant after logging into Microsoft Fabric:

Click Settings (the gear icon on the top right of the page)
Click Admin portal
Ensure that the Tenant setting tab is selected
Scroll all the way down to the Copilot and Azure OpenAI Service (preview) section

Note

You can also use the search box and search for OpenAI to find the Copilot and Azure OpenAI Service (preview) section.

Enable the Users can use a preview of Copilot and other features powered by Azure OpenAI
Click the Apply button
Enable the Data sent to Azure OpenAI can be processed outside your tenant’s geographic region, compliance boundary, or national cloud instance
Click the Apply button again

That is it. You enabled the Copilot capabilities on your tenant.

The following image shows the preceding steps:

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In this blog post, I will explain some of the key concepts, personas, and terminologies related to Microsoft Fabric, a SaaS analytics platform for the era of AI. If you are not familiar with the basic concepts of SaaS analytics platforms and how Microsoft Fabric fits in, I recommend you read my previous blog post, where I explain them in detail.

Microsoft Fabric is an experience-based platform, meaning users can interact with it depending on their roles and personas. For example, a data engineer can use the Data Engineering experience to perform large-scale data transformation through the lakehouse. A data scientist can use Data Science experience to develop AI models on a single foundation without data movement. A business analyst can use the Power BI experience to create and consume interactive reports and dashboards. And a data steward can use the Data Activator experience to govern and secure data across the organisation.
The Data Activator experience is in private preview and is not available for public use yet!

Microsoft Fabric Terminologies

To understand how Microsoft Fabric works, it is crucial to know some of the terminologies that are used in the platform. Some of them are existing terms that are also used in Power BI or Azure services, while some of them are new and specific to Microsoft Fabric. Here are some of the key terms that you should know:

Tenant: A tenant is a dedicated instance of Microsoft Fabric that is provisioned for an organisation or a department within an organisation. A tenant has its own set of users, groups, permissions, capacities, workspaces, items, and experiences. A Fabric tenant is associated with an Azure Active Directory (AAD) tenant, which is a directory service that the organisations own when they sign up for a Microsoft cloud service such as Azure, Microsoft 365, Power BI, etc. AAD provides identity and access management for cloud applications. A tenant in Microsoft Fabric can only be accessed by users who belong to the same AAD tenant.
Capacity: Capacity is a term that refers to the amount of resources available to support a computing service. In the context of SaaS applications, capacity refers to the ability of the system to handle a certain amount of load or demand based on the required resources and infrastructure such as compute power (CPU, RAM, etc.), storage, network bandwidth and whatnot. As explained in my previous post, Microsoft Fabric is a SaaS platform. So, from a Microsoft Fabric perspective, capacities are sets of resources that are allocated to a tenant to run analytics workloads. The capacities sit in a tenant, and the available resources can be shared by multiple workspaces or dedicated to a single workspace for better performance and isolation. Microsoft Fabric capacities are available in various F SKUs that offer different levels of resources and features. For more information about capacities and SKUs, see Microsoft Fabric Capacity and SKUs.
Workspace: A workspace is a logical container that holds a collection of items and artefacts. A workspace can have one or more owners who can manage its settings and permissions and one or more members who can access its items. A workspace can also be assigned to a capacity to run its analytics workloads. In Microsoft Fabric, workspaces are based on Power BI workspaces.

The above terms also apply to Power BI, so they have been used within the community for a long time. The hierarchy starts with an organisation acquiring their potential Tenants, and then the purchased Capacities are available to tenants and the Workspaces that are assigned to capacities.

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Microsoft Fabric is a new and unified analytics platform in the cloud that integrates various data and analytics services, such as Azure Data Factory, Azure Synapse Analytics, and Power BI, into a single product that covers everything from data movement to data science, real-time analytics, and business intelligence. Microsoft Fabric is built upon the well-known Power BI platform, which provides industry-leading visualization and AI-driven analytics that enable business analysts and users to gain insights from data.

Basic concepts

On May 23rd 2023, Microsoft announced a new product called Microsoft Fabric at the Microsoft Build conference. Microsoft Fabric is a SaaS Analytics Platform that covers end-to-end business requirements. As mentioned earlier, it is built upon the Power BI platform and extends the capabilities of Azure Synapse Analytics to all analytics workloads. This means that Microfot Fabric is an enterprise-grade analytics platform. But wait, let’s see what the SaaS Analytics Platform means.

What is an analytics platform?

An analytics platform is a comprehensive software solution designed to facilitate data analysis to enable organisations to derive meaningful insights from their data. It typically combines various tools, technologies, and frameworks to streamline the entire analytics lifecycle, from data ingestion and processing to visualisation and reporting. Here are some key characteristics you would expect to find in an analytics platform:

Data Integration: The platform should support integrating data from multiple sources, such as databases, data warehouses, APIs, and streaming platforms. It should provide capabilities for data ingestion, extraction, transformation, and loading (ETL) to ensure a smooth flow of data into the analytics ecosystem.
Data Storage and Management: An analytics platform needs to have a robust and scalable data storage infrastructure. This could include data lakes, data warehouses, or a combination of both. It should also support data governance practices, including data quality management, metadata management, and data security.
Data Processing and Transformation: The platform should offer tools and frameworks for processing and transforming raw data into a usable format. This may involve data cleaning, denormalisation, enrichment, aggregation, or advanced analytics on large data volumes, including streaming IOT (Internet of Things) data. Handling large volumes of data efficiently is crucial for performance and scalability.
Analytics and Visualisation: A core aspect of an analytics platform is its ability to perform advanced analytics on the data. This includes providing a wide range of analytical capabilities, such as descriptive, diagnostic, predictive, and prescriptive analytics with ML (Machine Learning) and AI (Artificial Intelligence) algorithms. Additionally, the platform should offer interactive visualisation tools to present insights in a clear and intuitive manner, enabling users to explore data and generate reports easily.
Scalability and Performance: Analytics platforms need to be scalable to handle increasing volumes of data and user demands. They should have the ability to scale horizontally or vertically. High-performance processing engines and optimised algorithms are essential to ensure efficient data processing and analysis.
Collaboration and Sharing: An analytics platform should facilitate collaboration among data analysts, data scientists, and business users. It should provide features for sharing data assets, analytics models, and insights across teams. Collaboration features may include data annotations, commenting, sharing dashboards, and collaborative workflows.
Data Security and Governance: As data privacy and compliance become increasingly important, an analytics platform must have robust security measures in place. This includes access controls, encryption, auditing, and compliance with relevant regulations such as GDPR or HIPAA. Data governance features, such as data lineage, data cataloging, and policy enforcement, are also crucial for maintaining data integrity and compliance.
Flexibility and Extensibility: An ideal analytics platform should be flexible and extensible to accommodate evolving business needs and technological advancements. It should support integration with third-party tools, frameworks, and libraries to leverage additional functionality.
Ease of Use: Usability plays a significant role in an analytics platform’s adoption and effectiveness. It should have an intuitive user interface and provide user-friendly tools for data exploration, analysis, and visualisation. Self-service capabilities empower business users to access and analyse data without heavy reliance on IT or data specialists.
These characteristics collectively enable organisations to harness the power of data and make data-driven decisions. An effective analytics platform helps unlock insights, identify patterns, discover trends, and drive innovation across various domains and industries.

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Tag: AI

Microsoft Fabric: Use Copilot to Generate Data Model Synonyms

Prerequisites

Using Power BI Copilot to generate synonyms

Microsoft Fabric: Generating Reports with Copilot

Requirements

Enabling Copilot on Fabric Admin Portal

Microsoft Fabric: A SaaS Analytics Platform for the Era of AI

Basic concepts

What is an analytics platform?

Prerequisites

Using Power BI Copilot to generate synonyms

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Requirements

Enabling Copilot on Fabric Admin Portal

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Microsoft Fabric Terminologies

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Basic concepts

What is an analytics platform?

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