Co-Pilot & Microsoft Fabric for Power BI

Introduction

Microsoft's data platform solution for the modern era is called Fabric. Microsoft's three primary data analytics tools:  Power BI, Azure Data Factory, and Azure Synapse all covered under Fabric. Advanced artificial intelligence capabilities built on machine learning and natural language processing (NLP) are made available to Power BI customers through Copilot. In this article, we will deep dive into how co-pilot and Microsoft Fabric will transform the way we develop and work with Power BI.

Co-Pilot and Fabric with Power BI

The urgent requirement for businesses to turn their data into value is something that both Microsoft Fabric and Copilot aspire to address. Big Data continues to fall short of its initial promises even after years have passed. Every year, businesses generate more data, yet a recent IBM study found that 90% of this data is never successfully exploited for any kind of strategic purpose. So, more data does not mean more value or business insight. Data fragmentation and poor data quality are the key obstacles to releasing the value of data. These problems are what Microsoft hopes to address with Microsoft Fabric, a human-centric, end-to-end analytics product that brings together all of an organization's data and analytics in one place. Copilot has now been integrated into Power BI. Large multi-modal artificial intelligence models based on natural language processing have gained attention since the publication of ChatGPT. Beyond casuistry, Microsoft Fabric and Copilot share a trait in that they each want to transform the Power BI user interface.

●       Microsoft Fabric and Power BI

Microsoft Fabric is just Synapse and Power BI together. By combining the benefits of the Power BI SaaS platform with the various Synapse workload types, Microsoft Fabric creates an environment that is more cohesive, integrated, and easier to use for all of the associated profiles. However, Power BI Premium users will get access to new opportunities for data science, data engineering, etc. Power BI will continue to function as it does right now. Data analysts and Power BI developers are not required to begin using Synapse Data Warehouse if they do not want to. Microsoft wants to combine all of its data offerings into one, called Fabric, just like it did with Office 365:

Image 1: Microsoft Fabric (Source: Microsoft)

Let’s understand in detail how Microsoft Fabric will make life easier for Power BI developers.

1.     Data Ingestion

There are various methods by which we can connect to data sources in Fabric in order to consume data. For example, utilising Spark notebooks or pipelines, for instance. This may be unknown to the Power BI realm, though.

Image 2: Data Transformation in Power BI

 Instead, we can ingest the data using dataflows gen2, which will save it on OneLake in the proper format.

2.     Ad Hoc Query

 One or more dataflows successfully published and refreshed will show in the workspace along with a number of other artifacts. The SQL Endpoint artifact is one of them. We can begin creating on-demand SQL queries and saving them as views after you open them. As an alternative, we can also create visual queries which will enable us to familiarise ourselves with the data flow diagram view. Above all, however, is the fact that this interface shares many characteristics with Power BI Data Marts, making it a familiar environment for those familiar with Power BI:

   Image 3: Power BI - One Data Lake Hub

3.     Data Modelling

With the introduction of web modelling for Power BI, we can introduce new metrics and start establishing linkages between different tables right away in this interface. The default workspace where the default dataset is kept will automatically contain the data model. The new storage option Direct Lake is advantageous for the datasets created in this manner via the cloud interface. By having just one copy of data in OneLake, this storage style prevents data duplication and unnecessary data refreshes.

●       Co-Pilot and Power BI

 Copilot, a new artificial intelligence framework for Power BI is an offering from Microsoft. CoPilot is Power BI's expensive multimodal artificial intelligence model that is built on natural language processing. It might be compared to the ChatGPT of Power BI. Users will be able to ask inquiries about data, generate graphics, and DAX measures by providing a brief description of what they need thanks to the addition of Copilot to Power BI. For instance, it demonstrates how a brief statement of the user's preferences for the report:

"Add a table of the top 500 MNC IT Companies by total sales to my model”.

The DAX code required to generate measures and tables is generated automatically by the algorithm.

Copilot enables:

●       Power BI reports can be created and customized to provide insights.

●       Create and improve DAX computations.

●       Inquire about your data.

●       Publish narrative summaries.

●       Ease of Use

●       Faster Time to Market

 Key Features of the Power BI Copilot are as follows: 

●       Automated report generation

Copilot can create well-designed dashboards, data narratives, and interactive components automatically, saving time and effort compared to manually creating reports.

●       Conversational language interface

We can use everyday language to express data requests and inquiries, making it simpler to connect with your data and gain insights. 

●        Real-time analytics

Copilot's real-time analytics capabilities can be used by Power BI customers to view data and react swiftly to shifts and trends.

 Let’s look at the step-by-step process on how to use Copilot for Power BI:

Step 1: Open Power BI and go to the Copilot tab screen,

Step 2:  Type a query pertaining to the data for example to produce a financial report or pick from a list of suggestions that Copilot has automatically prepared for you.

Step 3: Copilot sorts through and analyses data to provide the information.

Step 4: Copilot compiles a visually stunning report, successfully converting complex data into easily comprehended, practical information.

Step 5: Investigate data even more by posing queries, writing summaries to present to stakeholders, and more.
 

There are also a few limitations to using the Copilot features with Power BI: 

●       Reliability for the recommendations

All programming languages that are available in public sources have been taught to Copilot, ensuring the quality of its proposals. The quantity of the training dataset that is accessible for that language, however, may have an impact on the quality of the suggestions. APL, Erlang, and other specialized programming languages' suggestions won't be as useful as those for more widely used ones like Python, Java, etc.

●       Privacy and security issues

There are worries that the model, which was trained on publicly accessible code, can unintentionally recommend code fragments that have security flaws or were intended to be private.

●       Dependence on comments and naming

The user is responsible for accuracy because the AI can provide suggestions that are more accurate when given specific comments and descriptive variable names.

●       Lack of original solutions and inability to creatively solve problems.

 Unlike a human developer, the tool is unable to do either. It can only make code suggestions based on the training data.

●       Inefficient codebase

The tool is not designed for going through and comprehending big codebases. It works best when recommending code for straightforward tasks.

Conclusion

The combination of Microsoft Copilot and Fabric with Power BI has the ability to completely alter the data modelling field. It blends sophisticated generative AI with data to speed up the discovery and sharing of insights by everyone. By enabling both data engineers and non-technical people to examine data using AI models, it is transforming Power BI into a human-centered analytics platform.

Author Bio:

 Sagar Lad is a Cloud Data Solution Architect with a leading organization and has deep expertise in designing and building Enterprise-grade Intelligent Azure Data and Analytics Solutions. He is a published author, content writer, Microsoft Certified Trainer, and C# Corner MVP.

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