Last year, Microsoft introduced Microsoft Fabric, a unified SaaS platform that combines under one umbrella the best of Microsoft Power BI, various Azure PaaS offerings, and a host of new experiences related to data science, real-time analytics, and more. Fabric is underpinned by CAPACITY–the paradoxically simple yet complex measurement of compute resources powering various Power BI Fabric workloads.

FABRIC: SaaS rebrand
The same SaaS ecosystem Microsoft debuted as Power BI in 2015

ALSO FABRIC: new capabilities
A series of new workloads enabled independently of Fabric’s Power BI experience

To facilitate a smooth transition from Power BI to Fabric (new capabilities), Microsoft ensured customers could access these new Fabric workloads as well as Copilot for Power BI on their existing Power BI Premium capacity P SKUs.

However, with the introduction of Azure-billed pay-as-you-go and annual reservation F SKUs for Microsoft Fabric, Microsoft recently announced the eventual retirement of the Power BI Premium per capacity SKUs that needs to consider an organization’s Enterprise Agreement (EA) timing.

Impact of Enterprise Agreement (EA) on Transition

The timing of the EA has a significant impact on the transition timeline for a few reasons. Since the EA is a multi-year contract with an annual anniversary:

• An anniversary date “true down” or waiting until the end of the entire EA contract are the only options for customers to remove product SKUs such as Power BI Premium per capacity P SKUs
• Customers with an existing EA agreement can continue to use their existing P SKU purchases until the end of their EA agreement
• Customers with an existing P SKU on their EA can continue to purchase new Premium capacity using that SKU through the end of their EA.

See the Microsoft licensing page for the exact verbiage: https://www.microsoft.com/licensing/news/power-bi-premium-sku-retirement

The EA timing is key from a cost perspective to ensure that customers don’t overspend during the transition period. As a result, most organizations would likely transition from P SKUs to F SKUs around their anniversary date or wait until end of EA.

Considering Hybrid P + F

Having P SKUs on an EA doesn’t mean customers won’t be able to purchase new Fabric F SKUs for extending their workloads or scaling. Many organizations provision multiple capacities for various reasons such as workload or line of business isolation, multi-geo, and more.

In one sample scenario, there could be advantages for a hybrid approach with a mix of P and F capacities where Fabric workloads such as compute-heavy but less-frequently used data engineering workloads may sit on an Azure F PayGo that’s scaled or paused flexibly along with Power BI workloads that continue to remain in “static” Premium P until the EA anniversary. And so on…

Closing Thoughts

The transition from Power BI Premium P SKU to Fabric F SKU seems like a small change but has significant impact on contracts and costs for any Power BI Premium customer with an EA. It’s therefore important to understand the timeline for this transition and how it’s impacted by the Enterprise Agreement timing. Around the time of next renewal, existing P SKU customers should work with their Microsoft account representative to transition to a suitable Fabric SKU to take advantage of new F-only features like workspace identities, more flexibility in SKU size, pay-as-you-go billing options, and the ability to contribute toward any Azure spend commitments (MACC).

The post Transition from Power BI Premium P SKU to Fabric F SKU: Impact of Enterprise Agreement (EA) Timeline appeared first on DataVeld.

Data visualization and design have been essential skills for anyone who wants to communicate insights from data effectively.

Creating engaging and informative visuals and reports often requires a lot of time and effort, however; and involves multiple iterations of data analysis, design choices, and feedback loops.

Is this lengthy process still relevant in the era of generative AI, where we can now automatically generate visuals and reports from data?

Even in its infancy, generative AI has upended the way we work. AI is now used for diverse purposes such as generating realistic images, synthesizing speech or music, writing articles, and summarizing text, images, or videos. Generative AI can also be applied to data visualization and report design, where it can help you create visuals and reports from your data in a fast and easy way.

One of the main benefits of using AI for data visualization design is that it can save time. Instead of spending hours or days on manually analyzing data, choosing the right visualizations, and designing reports, you can use generative AI to accelerate much of the work. AI can automatically analyze data, select the most relevant and interesting insights, generate appropriate visualizations, and write clear and concise summarizations. Designers can then focus on refining the higher-level aspects of communicating with data.

Another benefit of using AI for data visualization design is that it can improve the quality and diversity of output in minimal time. Generative AI can analyze large amounts of data and has demonstrated passable examples of core data visualization and report design. It can largely generate visuals and reports that are accurate, consistent, and arguably appealing for regular business consumers. Generative AI can also offer you multiple options and variations for your visuals and reports, allowing you to explore different perspectives and styles. This can help you discover new insights and ideas from your data that you might have missed otherwise.

So is a lengthy human-driven iterative design process still necessary? Should organizations still promote and pay for weeks or even months of design development time when they can settle for passable output in minutes or hours?

Maybe.

Generative AI for data visualization and report design also comes with some challenges and limitations. One of the main challenges is to ensure the trustworthiness and reliability of the generated output. Generative AI is not perfect, and it can sometimes produce errors or biases in the analysis, visualization, or reporting of your data. For example, generative AI might misinterpret your data in a way that a domain expert would not, it could generate misleading or inappropriate visualizations, or it could write inaccurate or vague summarizations that are not valuable ways to communicate the data. As a result, it is important to always verify and validate the output of AI while using them for your work.

Another challenge of AI is to maintain the human element and creativity in your data visualization design. AI can help you automate some of the tedious and repetitive tasks in your workflow, but it cannot replace your human judgment and intuition. It doesn’t understand the nuances of Bill from Marketing or Barbara from Finance asking for iterative tweaks and refinement. Generative AI creates, but it doesn’t immediately capture purpose, tone, or emotion in a design the way a human designer does. You have to guide it.

Therefore, it is important to leverage the potential to use generative AI as a tool to support data visualization design and communication, but not act as a complete substitute for it. Maybe you will no longer be spending weeks or months on a single effort, but you also won’t have complete value ready in seconds.

The post Is a Lengthy Iterative Data Visualization Design Process Relevant in the Era of Generative AI? appeared first on DataVeld.

Data is generated by every click, swipe, and interaction we have online. It is collected by businesses, governments, and organizations to understand their customers, markets, and operations. It is analyzed both manually and automatically by tools, AI algorithms, and models to generate insights, predictions, and recommendations.

It’s easy to get caught up in all the noise. To make the most of data, we need data literacy. Data literacy is the ability to read, understand, create, and communicate with data. It is a skill that empowers us to ask the right questions, find the right answers, and make the right decisions with data.

Data literacy is essential for any organization that wants to optimize its data culture. Data culture is the way an organization values, uses, and shares data across its teams and functions. It is collectively the mindset and behaviors that enable an organization to become more data-driven and hopefully more innovative.

Assess Your Data Literacy Level

Before you can improve your data literacy level, you need to assess where you are now. There are different ways to measure your data literacy level, depending on your objectives and resources.

One way is to use a self-assessment tool or questionnaire that covers different aspects of data literacy, such as:

• Data awareness: How familiar are you with the types, sources, formats, and quality of data available in your organization?
• Data understanding: How well can you interpret, analyze, visualize, and summarize data using basic statistics and tools?
• Data creation: How proficient are you in creating new data sets or variables from existing or external data sources?
• Data communication: How effectively can you communicate your data insights and findings using appropriate tools, formats, languages, and narratives?
• Data ethics: How aware are you of the ethical principles and practices related to data collection, storage, usage, and sharing?

Another way is to use a performance-based assessment that tests your data literacy skills in a realistic scenario or task. For example:

• You are given a dataset related to your business domain and asked to perform some basic analysis on it using a tool of your choice.
• You are given a business problem or question related to your domain and asked to find relevant data sources and methods to answer it.
• You are given a data insight or finding related to your domain and asked to present it in a clear and compelling way using a common tool such as Microsoft Power BI.

You can also use a combination of both methods to get a more comprehensive picture of your data literacy level.

How to Improve Your Data Literacy Level

• Learn the basics of data. Data literacy requires a foundation of basic concepts and terminology related to data, such as types of data, data quality, data formats, data structures and data manipulation. You can learn these concepts through online courses, books, videos, or podcasts.
• Practice your data skills. Data literacy also involves practical skills that allow you to work with data effectively, such as finding, collecting, cleaning, analyzing and visualizing data. You can practice these skills by using various tools and platforms that enable you to access and manipulate data easily. For example, you can use [Modern] Excel [with Power Query] for spreadsheet analysis, Power BI for data model and report creation, and Python for working with code in a notebook.
• Develop your data mindset. Data literacy also requires a mindset that embraces data as a valuable asset and a source of insight. You can develop your data mindset by cultivating curiosity, critical thinking and creativity with data. You can do this by asking questions about the data you encounter, challenging assumptions and biases, exploring different perspectives and scenarios, and finding new ways to communicate your findings.
• Foster a data culture. Data literacy also depends on the culture and environment that surround you. You can foster a data culture by engaging with other people who share your interest in data, such as colleagues, mentors or peers. You can do this by joining online communities, forums or groups that discuss data-related topics, attending webinars, workshops or events that showcase best practices or case studies with data, or participating in projects, competitions or challenges that involve working with real-world data.
• Make data-driven decisions. Data literacy also implies using data to inform your decisions and actions. You can make data-driven decisions by applying a systematic process that involves defining your problem or goal, gathering relevant data, analyzing the data for patterns and insights, presenting your results in a clear and compelling way, and evaluating the impact of your decision. You can use this process for any kind of decision-making scenario, whether personal or professional.

The post Boost your Data Culture by Improving Data Literacy appeared first on DataVeld.

Data literacy has become a vital skill for individuals at any organization that wants to leverage data for better decision making and innovation. Data is everywhere, and decisions are often based on data-driven insights.

Data Literacy’s Impact

As the ability to read, understand, analyze, and communicate with data; data literacy is a crucial component of success in a fast-moving, data-driven world. Not everyone has the same level of data literacy, however, and this can create challenges and gaps within an organization.

Addressing Gaps

One way to address this issue is to use tools that make data more accessible and intuitive for everyone, regardless of their background or expertise. One such tool is the Microsoft Power BI natural language Q&A visual, which allows users to ask questions about their data in plain language and get instant answers in the form of charts, tables, and maps.

The natural language Q&A visual is powered by a sophisticated natural language processing (NLP) engine that can understand the intent and context of the user’s query and translate it into a query language that the data model can understand. The visual also provides suggestions and feedback to help users refine their questions and get the best results. You can even provide sample questions, provide synonyms for common semantic terms, and train the model.

Personally, I’m still surprised that many people are not even aware of the Q&A visual in Power BI. It’s been available for a few years, but the advantages of Q&A for the wider audience of exploration-hungry consumers and acceleration for report creators has regretfully not been well-evangelized. People must be too busy writing DAX I suppose, but there’s AI-based acceleration with GPT for that now as well.

Benefits of the Q&A Visual for Data Literacy

The natural language Q&A visual has several benefits for enhancing data literacy across an organization:

Lowers Barriers for Exploration

First, it lowers the barrier to entry for data exploration and analysis, as users do not need to know how to write complex formulas or queries to get insights from their data. They can simply type their questions in natural language and get answers in seconds.

Fosters a Culture of Curiosity

Second, it fosters a culture of curiosity and learning, as users can ask any question they have about their data and discover new patterns, trends or outliers. They can subsequently perform common actions like drill down into details, filter or slice the data, or change the visualization type to get different perspectives on their data.

Improve Communication and Collaboration

Third, it improves communication and collaboration, as users can easily share their findings with others by saving the visual in a report, or by exporting the visual as an image, or even share live on an interactive PowerPoint slide. They can also use the visual as a conversation starter with their colleagues or stakeholders, as they can ask follow-up questions or explore different scenarios together.

Get Started

Overall, the Microsoft Power BI natural language Q&A visual is a powerful tool that can help users of all levels of data literacy to access, understand and communicate with their data in a simple and intuitive way.

Go to the Microsoft docs to learn more and get started with the natural language Q&A visual in Power BI: https://learn.microsoft.com/en-us/power-bi/visuals/power-bi-visualization-q-and-a

The post Heighten Data Literacy with the Power BI Natural Language Q&A Visual appeared first on DataVeld.

One of the key advantages of Power BI is the ability to create powerful custom measures in your data models using the Data Analysis Expressions (DAX) language. DAX allows users to create complex calculations based on their data, but it can be time-consuming to write DAX from scratch–especially for new users.

To help with this, Power BI debuted and now offers a feature called Quick Measure Suggestions, which uses natural language and GPT to generate DAX code for common measure scenarios quickly.

There are several benefits to using Quick Measure Suggestions, including:

1. Time savings: Quick Measure Suggestions can save time by automating the creation of commonly used measures, eliminating the need to write DAX code from scratch.
2. Reduced errors: Quick Measure Suggestions can help reduce errors that may occur when writing DAX code manually. Since the DAX code is generated automatically, there is less chance of syntax errors or other mistakes.
3. Easy to use: Quick Measure Suggestions is easy to use, even for users who are not familiar with DAX. Natural language examples can help users quickly generate DAX code for their specific measure scenarios.
4. Consistent measures: Quick Measure Suggestions can help ensure that measures are consistent across the organization. Since Quick Measure Suggestions generates DAX code based on predefined measure scenarios, measures created by different users will have a similar structure.

With Quick Measure Suggestions, you describe the measure you want to create and simply click Generate to get DAX measure suggestions. Supported measure types include:

• aggregated columns
• count of rows
• aggregate per category
• mathematical operations
• selected value
• if condition
• text operations
• time intelligence
• relative time filtered value
• most/least common value
• top N filtered value
• top N values for a category
• information functions

While Quick Measure Suggestions are a way to accelerate your DAX, you’re still in charge. It is essential to validate the DAX suggestions to make sure they meet your needs, and test the results afterward to ensure that the resulting measure accurately reflects your intended analysis.

The post Learning Power BI? Try GPT-based natural language Quick Measure Suggestions for DAX appeared first on DataVeld.

It can be challenging for Mac users to work with Power BI Desktop because it is a Windows-only application for creating datasets and reports. In this tutorial, we’ll walk you through the steps to install Power BI Desktop on your Mac!

Step 1: Find an image of Power BI Desktop

The first step is to find an image of Power BI Desktop that you want to use for your MacBook. You can use any image you like, as long as it’s a high-quality image and in the correct size for your MacBook.

Step 2: Customize the MacBook skin

Once you have your image, you’ll need to customize the MacBook skin. You can use an online service like Skinit, which allows you to upload your image and customize it to fit your MacBook perfectly. Make sure to choose the correct MacBook model and size, as the skin needs to fit snugly and align with all the ports and buttons.

Step 3: Order the customized skin

After customizing your MacBook skin with the Power BI Desktop image, order it online. The website will ask for your shipping address, and you’ll need to pay for the skin using a credit card or PayPal account.

Step 4: Install Power BI Desktop

When your skin arrives, carefully remove it from the packaging and lay it flat on a clean surface. Make sure your MacBook is clean and free of dust and debris. Line up the skin with the MacBook, starting from the bottom and working your way up. Smooth out any air bubbles or wrinkles as you go, and press down firmly on the skin to make sure it adheres correctly.

Congratulations! You have successfully installed Power BI Desktop on your Mac. Now you can enjoy Power BI Desktop every time you use your MacBook!

This post was generated without common sense by GPT.

Dataset Authoring Preview

Okay, so perhaps that’s not the solution you’ve been looking for. The good news though is that the Microsoft Power BI product group recently introduced a preview of dataset authoring in the Power BI service. While still in its early stages, Microsoft is closing the last major gap in the goal to bring eventual feature parity between Desktop and the web. Explore the Power BI service at PowerBI.com for report authoring with both interactive and operational / paginated reports (new and evolving), dataset authoring for models (new), dataflows for ingesting and transforming data with Power Query, and more!

Read more in the Power BI blog: Edit your data model in the Power BI Service (Preview) | Microsoft Power BI Blog | Microsoft Power BI

The post How to Install Power BI Desktop on a Mac (without Parallels) appeared first on DataVeld.

Imagine being able to analyze your company’s revenue while you’re washing your hair. Or tracking your metrics while you’re secretly singing your favorite tunes. It’s now possible with new data analytics tool Shower BI.

Marketed toward analytics engineers who work remotely and need to regularly clean data, Shower BI is a refreshing new way entrant from a startup named [redacted pending result of ongoing litigation].

Some people may be skeptical about the idea of analyzing data in the shower, but the new startup is confident that once you try Shower BI, you’ll never go back to your old, non-waterproof data analysis tool. With Shower BI, every shower becomes a chance to gain gallons of insights, make decisions, and stay cleanly ahead of the competition.

For those of you who are worried about privacy, don’t be. Shower BI comes equipped with encryption and security features to ensure that your data stays safe–even if you accidentally drop it in the tub.

Speaking of which, the startup also plans a future premium tier, Shower BI Extreme. Benefits of the new Extreme version will include a robust and durable casing that is shockproof in addition to being waterproof.

The post Startup Releases Waterproof Data Analysis Tool: Shower BI appeared first on DataVeld.

Power BI Paginated Report Bear, a beloved figure among data analysts hungry for Excel exports and emailed PDFs, is not who he claims to be. In fact, he is not a real bear at all. Instead, I believe that he is a stuffed animal with someone’s hand controlling him from behind the scenes.

After years of intensive video review, I believe that I have definitive evidence showing that Paginated Bear is not who he claims.

Take a look for yourself. Note the human-arm-shaped protrusion to the rear of the “bear” during one of his interviews.

Also of note, if you watch his YouTube interviews, he speaks yet his mouth never moves. In fact, it appears he does not even have a mouth.

While circumstantial, the fact that Paginated Report Bear has disappeared from public view over the past few years while living off his YouTube earnings as well as the belief that he may have taken on a pseudonym (“Mr. Sparkles”) lends further credence to the theory.

While we may never know for certain, we’ll be on the lookout for corroborating evidence. Check back here regularly for updates.

The post Power BI Paginated Report Bear EXPOSED appeared first on DataVeld.

Desperate to win its first bake-off, a presenter for analytics startup Oven.AI recently turned up the heat during a live competition. Confused by the rules as a first time competitor for the bake-off, and following the instructions found in a GPT recipe, the presenter placed his laptop in an oven on stage.

“Today we’re going to show you a truly 360 degree view of your customer” the presenter told a captive audience as the bake-off began. Within a few minutes, a strange odor began to permeate the room.

“You’re on fire!” one member of the audience suddenly shouted. Ignorant of the warning, the presenter responded “I know! Thanks for your support!”

After ten minutes, smoke began to fill the room, and two minutes later, the auditorium sprinklers went off. Everyone was safely evacuated out of the auditorium.

After the fire department arrived, the presenter was given five more minutes outside to conclude the interrupted demo.

The cause of the fire has not been officially released pending further investigation.

The post Software Bake-Off Goes Awry After First Time Competitor Puts Laptop in Oven appeared first on DataVeld.

After a recent DNA test, a Power BI Paginated Report discovered it was created directly in the browser using web authoring instead of the traditional Report Builder desktop application. The report, which had been deployed among its sibling reports for months, was reportedly in a state of shock and disbelief upon discovering its true origins.

“I can’t believe this has happened to me,” the Paginated Report reportedly said to its fellow reports. “I thought I was painstakingly created by some developer over the course of hours using the same tool as all of you. Now I’ve learned that someone simply clicked around in their browser for a few seconds just before I was born.”

“I had to come to terms with the fact that my entire life, I had been living a lie,” it said. “But at the same time, I was excited to discover a whole new world of authoring capabilities, one that I had never known existed before.”

The news has sent shockwaves throughout the Power BI community, with many wondering how this could have happened. The surprising news comes only months after paginated reports also shifted from a Premium only feature to being available in Pro. Some are even calling for an investigation into how it became so easy to create basic paginated reports.

The post After DNA Test, Power BI Paginated Report Stunned to Discover It Was Web Authored appeared first on DataVeld.