The proliferation of generative AI has also led to a proliferation of techniques and tools to use AI. Microsoft’s ecosystem itself while emerging as a versatile platform for building AI agents has a variety of agent options to navigate.

From low-code declarative agents focused primarily on instructions and grounding in business or public data to fully customized multimodal solutions, anyone from developers to business users have multiple pathways to create agents tailored to specific workflows. There are choices, but how can users unfamiliar with the ecosystem make the most appropriate choices (or only half-jokingly, “Why not use AI to choose?”).

This post examines four primary tools:

1) Agent Builder in Copilot Chat

2) Copilot Studio

3) Teams Toolkit

4) Azure AI Foundry

It contrasts their capabilities, target audiences, and paradigms.

But first, let’s explore two distinct types of agents that will impact choices ahead.

Declarative Agents vs. Custom Engine Agents: Foundational Differences

At the core of Microsoft’s AI agent strategy lies a distinction between declarative agents and custom engine agents.

Declarative agents leverage Microsoft Copilot’s prebuilt infrastructure, including its orchestrator, language models, and security framework. These agents are configured through natural language instructions, predefined actions, and enterprise knowledge sources like SharePoint or Graph connectors (plus optional web grounding, code interpreter, and image generator).

They excel in scenarios requiring rapid deployment within Microsoft Copilot Chat, such as onboarding assistants or IT helpdesk agents. By inheriting Copilot’s compliance and access controls, declarative agents minimize development overhead while ensuring enterprise-grade governance.

In contrast, custom engine agents operate on developer-selected and even developer-trained language models, enabling organizations to integrate proprietary language models, advanced orchestration logic, or multimodal capabilities. These agents support the most advanced proactive workflows (e.g., automated inventory restocking) and even cross-platform deployments.

While offering greater flexibility, custom engine agents carry higher operational complexity to maintain security and regulatory alignment.

Agent Development Tools: Capabilities and Tradeoffs

Agent Builder in Copilot Chat

Integrated directly into Microsoft 365 Copilot Chat, this tool allows business users to create declarative agents through conversational prompts. Users describe an agent’s purpose (e.g., “Market research advisor using corporate financial statements”) and configure knowledge sources via a guided interface.

Highlights:
• Natural Language Configuration: Agents are defined through free-form descriptions of their role, tone, and data sources.
• Built-In Grounding: Supports SharePoint files, public websites, and Graph connectors without requiring API integrations.
• Metered Consumption OR included in Microsoft 365 Copilot per user license: Agents accessing tenant data (e.g., internal SharePoint sites) follow pay-as-you-go billing if desired, or can use Microsoft’s per user licensing as well.
• Zero-Code Accessibility: Enables business analysts or subject-matter experts to deploy agents in minutes.
• Native Microsoft 365 Integration: self-created agents appear alongside others in Copilot Chat, requiring only basic publishing and sharing steps (still subject to organizational governance and administration).
• Limited Customization: Currently lacks support for custom APIs, multi-step workflows, multimodal agents, or external channel deployments.
• Dependency on Copilot Infrastructure: Cannot incorporate specialized models (e.g., medical LLMs)

Target Audience:

Individuals or line-of-business teams needing task-specific assistants without IT dependency. Example includes a targeted FAQ agent built on knowledge in a team’s SharePoint site. (SharePoint itself has agents, which is outside the scope of this post but which are a special case of declarative agents)

Copilot Studio

Positioned as a low-code/pro-code hybrid platform, Copilot Studio expands declarative agent capabilities with advanced features like Power Automate integrations and multi-channel publishing.

Highlights:
• Visual Orchestration: Drag-and-drop interface for designing conversational flows and connecting to Graph and Power Platform connectors (including outside the Microsoft ecosystem such as Salesforce or ServiceNow).
• Environments .
• Power Platform Foundation: Agents built in Copilot Studio take advantage of Power Platform environments and solutions.
• Balanced Flexibility: Supports both declarative configurations and custom engine agents.
• Centralized Governance: Admins manage agent access via Microsoft Admin Center and Power Platform Admin Center, with audit logs and policies for governance in Purview.
• Licensing: Requires Copilot Studio subscriptions for development unless using a “lightweight” Microsoft 365 Copilot license and only publishing to that channel.

Target Audience:

“Makers” in IT or operations roles building department-wide or enterprise-wide assistants.

Teams Toolkit for Visual Studio Code

A developer-centric toolkit for building declarative or custom engine agents as apps.

Highlights:
• Code-First Development: Create agents using SDKs and include adaptive cards, message extensions, and proactive triggers.
• Model Agnosticism: Integrate any LLM.
• Full Control Over AI Stack: Developers dictate orchestration logic, model choices, actions.
• Enterprise Scalability
• Learning Curve

Target Audience:

Software engineers creating complex assistants for scenarios like clinical trial management or supply chain optimization.

Azure AI Foundry

Microsoft’s cloud AI platform provides foundational services for building the most advanced agents from scratch.

Highlights:
• Multimodal Capabilities: Combine vision, speech, and language models in agents.
• Hybrid Orchestration: Deploy agents across multiple environments.
• Custom Fine-Tuning: Train domain-specific models using proprietary data.
• Architectural Freedom: Supports any agent design pattern.
• Global Infrastructure: Custom data residency and other needs in or across Azure’s regions for the most compliant deployments.
• Development Resources: Requires dedicated engineers for development and DevOps teams for ongoing maintenance.
• Consumption pricing: Metered billing

Target Audience:

Enterprises requiring bespoke agents for deep, scalable scenarios, custom models, or advanced real-time use cases such as fraud detection or audio analysis and subsequent real-time action.

Aligning Tool Choice with Organizational Needs

The Microsoft ecosystem accommodates diverse agent development needs, but tool selection hinges on three factors: technical expertise, workflow complexity, and solution requirements.

Business units prioritizing speed-to-value will favor Agent Builder or Copilot Studio, while engineering teams tackling unique challenges (e.g., real-time translation agents) require Teams Toolkit or Azure.

Critically, declarative agents reduce time to market but do not permit as much innovation or model selection as a tradeoff, whereas custom engine agents offer the most bespoke options and customization potential. As Microsoft continues blending these paradigms (which is abstracted from the users but evident to admins who find themselves jumping between the M365 Admin Center, Power Platform Admin Center, and Purview), organizations can architect agent portfolios that balance development and audience agility with their strategic AI investments.

The post AI Agent Development on the Microsoft Platform: A Survey of Tools appeared first on DataVeld.

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.

Tableau has Python packages for working with its Hyper API, both for native operations (tableauhyperapi) or through pandas (pantab). With the recently announced public preview of Microsoft Fabric, you can also easily work with data sourced from Tableau Hyper files in a Fabric Lakehouse.

This post explores using the Notebook feature in Microsoft Fabric to convert a Hyper file that’s been loaded into a lakehouse as a file and convert it to parquet using pantab. From there, you can load as a Delta Lake table for use with other Fabric experiences such as Power BI.

To get started, verify that your Microsoft Fabric workspace is assigned to a Fabric, Power BI Premium, or Trial capacity in the Premium section of Workspace settings.

Select the Library management section and add the pantab library from PyPI. This is the Python package that Tableau pushlishes to work with Hyper in pandas. You do not need to explicitly add the pandas library itself for this example.

After the pantab library finishes loading, create a new or open an existing Lakehouse in the Fabric workspace.

Optionally create a new subfolder under the Files section of the lakehouse, then Upload a Hyper file to any location in Files. For this example, I created subfolders called Hyper, where I store source files, and Converted, where I store parquet files that I’ve converted.

After the Hyper file uploads, create a new or open an existing Notebook.

In the notebook, connect to the appropriate Lakehouse and verify you can see your Hyper file. Add the following script in PySpark and modify the variables to match your own Lakehouse paths, file name, table name, etc. In my example, I use the default lakehouse, reference my Hyper source subfolder under Files, reference my Converted subfolder under Files for my destination, load a Hyper file called GeocodingData.hyper, and load the Country table from the Hyper file.

import pantab

lakehouse_path = '/lakehouse/default/'
lakehouse_source_path = lakehouse_path + 'Files/Hyper/'
lakehouse_source_file = 'GeocodingData.hyper'
lakehouse_converted_path = lakehouse_path + 'Files/Converted/'
table_name = 'Country'

df = pantab.frame_from_hyper(lakehouse_source_path + lakehouse_source_file, table=table_name)print(df)

df.to_parquet(lakehouse_converted_path + table_name + '.parquet')  

Initially, in the screenshot above, I’ve commented out the conversion to parquet and only view the pandas dataframe using the pantab.frame_from_hyper function and view the results to verify I’ve connected to the file without any issue.

After verifying that you’re able to get data from Hyper, uncomment out the final line to convert the dataframe to parquet.

Refresh the lakehouse view and verify you are able to see the parquet file in your destination location.

After creating the parquet, open the lakehouse, select the ellipsis […] next to the parquet file, and select Load to Tables to load the parquet as a Delta table.

From there, you can utilize the Delta table in other Fabric experiences such as Power BI datasets.

The post How to Convert Tableau Hyper for Use in a Microsoft Fabric Lakehouse 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.