This post explains how Fabric, Power BI, and Dataverse relate to one another, what roles they play in Microsoft’s data architecture and why this matters for Excel users.

Fabric, OneLake and Microsoft’s data architecture

Microsoft Fabric is a unified data platform that brings together storage, analytics, governance, and AI under one umbrella. You can think of it as the foundation upon which modern Microsoft data tools are built.

At its core lies OneLake, a single, organization-wide data lake that serves as the “OneDrive for data.” It’s designed to eliminate data silos and ensure that every analytics tool, from Excel to Power BI to SQL, works with the same, trusted datasets.

Fabric unifies capabilities from technologies like Azure Data Factory, Synapse, and Power BI into one environment. For Excel users, this means that the workbooks you create, the data models you connect to, and the reports you share can all be part of a broader, governed ecosystem rather than isolated files. In other words, you can spend less time managing copies of data and more time analyzing it.

Power BI as a layer of Fabric

Power BI is no longer just a visualization tool. It’s an essential part of Fabric. The relationship between Power BI and Fabric is best described as semantic + platform:

• Fabric provides the infrastructure: storage (OneLake), compute, and governance.
• Power BI provides the semantic model: how data is organized, related, and presented.

In practice, Power BI runs on Fabric. When you create a Power BI dataset, it’s stored in OneLake. When you build a report, it can connect to the same Fabric-based data model that other tools (including Excel) use.

Example

A sales team might store raw transaction data in Fabric’s OneLake. Power BI builds a semantic model on top of that data, defining measures such as revenue, profit margin, and year-over-year growth. Excel users can then connect directly to that semantic model, creating PivotTables or custom reports without duplicating data or logic.

For Excel users, this means instead of relying on manually updated spreadsheets or one-off exports, you can work directly with governed, version-controlled data that’s consistent across the organization.

Dataverse and the Power Platform: The operational counterpart

If Fabric is the analytical backbone, Dataverse is the operational brain of the Power Platform.

Microsoft Dataverse stores structured, relational business data used by Power Apps, Power Automate, Power Pages, and Copilot Studio. Unlike Fabric, which is optimized for analytics and large-scale storage, Dataverse is optimized for transactional operations and business workflows.

While Fabric and Dataverse serve different purposes, Microsoft is steadily connecting them. For example, Dataverse data can be shared into Fabric via OneLake shortcuts, making it available for deeper analysis in Power BI or Excel.

Example

A company’s HR team might use a Power App built on Dataverse to track employee training. That same data can be shared to Fabric, where analysts use Excel or Power BI to measure completion rates, visualize trends, and correlate training with performance metrics.

For Excel users, this means that the data you analyze is directly tied to the systems running the business. No more CSV exports or outdated files. Your reports can be live reflections of real operational data.

How Excel fits into this landscape

Excel sits comfortably across both worlds:

• Excel can connect to Fabric datasets or Power BI semantic models for governed reporting.
• Excel can update or reference Dataverse data through Power Automate or the Dataverse connector.
• Tools like Power Query, Python, and Copilot in Excel can leverage both Fabric and Power Platform data sources to summarize, generate, or explain insights, all within the familiar Excel interface.

Example:

An analyst could open Excel, connect to a Fabric dataset of company financials, and use Copilot to summarize quarterly trends and identify outliers. Behind the scenes, that analysis might draw on data stored in OneLake, modeled in Power BI, and enriched through a Power Automate flow from Dataverse.

Comparing the core components

To put all of this into perspective, it helps to compare the key layers of the Microsoft data ecosystem and how Excel interacts with each. Understanding these roles clarifies where Excel fits and why it matters.

When you understand this stack, you can start building workflows that make Excel a strategic player in your data operations rather than just a spreadsheet tool.

Common workflows for Excel users

Understanding these systems conceptually is one thing, seeing them in action is another. The following examples show how Excel can act as a bridge between Fabric, Power BI, and Dataverse in real business workflows.

These use cases show how Excel users can extend their reach into automation, AI, and advanced analytics, without leaving Excel itself.

Why this matters

Many Copilot and AI-driven capabilities across Fabric the Power Platform rely on access to data in Fabric or Dataverse. Understanding how these systems interact allows Excel users to:

• Communicate effectively with IT and data teams about data sources and permissions.
• Design smarter workflows that avoid redundant data silos.
• Unlock Copilot capabilities that depend on connected, governed data.

By understanding how data moves through Fabric and the Power Platform, you’ll be well positioned to future-proof your Excel skills and boost your value as an analyst. Even if you don’t yet have the licenses or IT permissions to use every new workflow these tools enable, you’ll still stay aligned with modern trends in data architecture and AI-driven analytics.

Conclusion

Excel remains a critical front door to Microsoft’s data strategy. Its role is evolving from a standalone spreadsheet tool to a gateway into a connected data ecosystem powered by Fabric, Power BI, and Dataverse.

By understanding these relationships, Excel users can modernize their analysis, automate their reporting, and collaborate with IT and data teams on equal footing. In short: you don’t need to stop being an Excel expert. You just need to expand your world.

For more details, explore Microsoft’s documentation for Fabric, Power BI, and Power Platform.

If you’d like some help thinking through how all these pieces fit together and how to future-proof your data strategy, workflows, and talent you can book a free discovery call below:

The post How to understand Microsoft Fabric as an Excel user first appeared on Stringfest Analytics.

In this post, we’ll look at how to build an AI-powered Excel report using the AI Builder in Power Automate. To keep things simple, we’ll start small with a workflow that’s triggered manually and outputs a formatted summary of a well-known vehicle dataset. From there, you can expand the flow to refresh data automatically, send approvals, or even generate visuals, but it’s best to start with the basics.

Download the exercise file below and follow along to build your first AI-driven Excel report.

The very first thing we need to do is get this data into the Dataverse. That’s where AI Builder looks for data to analyze. I’d suggest using Dataflows for this step since it gives you a dynamic pipeline that automatically stays up to date as your data changes.

If you haven’t worked with Dataflows before, check out my post on how to set them up. Once your data is loaded, you can name the table autompg in Dataverse. That will make it easier to reference later when we build our AI-powered report.

Next you’ll fire up Power Automate at make.powerautomate.com and for the ease of simplicity start an Instant Cloud Flow. If you’re not familiar with these steps, I’d suggest checking out my LinkedIn Learning course on Power Automate:

Add a step after manually triggering a flow. From the AI Builder group at the top, choose Run a prompt (or search for it). This lets you use generative AI right inside Power Automate to summarize data, rewrite text, or generate insights using values from earlier steps. Learn more at learn.microsoft.com/ai-builder/overview or start a trial at learn.microsoft.com/ai-builder/ai-builder-trials.

Now, just like any other step in a Power Automate flow, you’ll need to define a few parameters. In this case, it’s the prompt to run. Choose Custom prompt. There are some built-in templates, but it’s best to provide as much context as possible so the AI understands your audience and intent.

Here’s an example prompt you can use in AI Builder to describe your dataset and the tone you want for the report. Paste it into the Prompt instructions box on the left.

As you build your prompt, start by giving it a clear, searchable name at the top. Then, make sure to connect it to live data from Dataverse so your flow stays dynamic.

To do this, click Add content at the bottom of the prompt box (or press the / key). Switch to Dataverse, locate the autompg table, and select the autompg attribute. This will pull in all records from the dataset and keep your AI Builder prompt connected to the latest data.

Before testing the prompt, make a few quick adjustments. Click the three dots (…) and select Settings. Increase the record retrieval limit to around 400 or more to ensure all rows in the dataset are included. Then turn on code interpreter, which allows the system to run calculations, generate charts, and analyze your data more effectively within the flow.

Go ahead and test the results. you should see something like the below.

It looks great, but freeform text output can be unpredictable. One way to control that is by lowering the temperature, but since it’s already at the minimum, we’ll need another approach. The solution is to add structure by mapping the output into JSON. This gives the model a clear format to follow, which helps produce consistent, reliable responses every time you run the flow.

To do this, change the Output format on the right side of the prompt builder from Text to JSON, then paste in the setup below. Be careful not to break the live connection to the autompg table in Dataverse.

Go ahead and re-test the prompt. You’ll notice the output looks quite different this time. The JSON format might seem messy at first, but that’s actually a good thing. It breaks your results into clear, structured building blocks, making them much easier to parse and reuse in the next step of your flow.

For this next step, we’ll take the JSON output and display it in an adaptive card. An adaptive card is a dynamic, interactive message format that can be shown in Teams, Outlook, or other Microsoft 365 apps. It’s a great way to present structured data clearly.

Instead of showing a block of raw JSON, users see a formatted, readable summary with key insights and buttons for actions. This makes your flow outputs easier to share and understand. If you want to explore adaptive cards in more depth, check out this post:

That means the third step in our flow will be to post an adaptive card to Teams. Specify the channel and any other details about where it should go, then for the Adaptive card parameter, paste in the information below:

Make sure to map your dynamic content to the correct values in the adaptive card code. Keep the quotation marks! They define where your live data goes and removing them can break the card’s structure.

Finally, save and test your adaptive card. You should see something like this in Teams:

Each time you run the flow, the results might vary slightly since it’s using generative AI, but the overall structure will stay consistent. That consistency is a good sign: it means our JSON formatting is working, and we can now fine-tune the look and layout through the adaptive card code.

Now that you’ve built your first AI-powered Excel report, you’ve got a solid foundation to build on. From here, you could add an approval process that routes the report to a manager in Teams before sharing, or schedule the flow to refresh and update the data automatically in Dataverse.

And this flow is just the beginning. Imagine generating monthly summaries automatically, sending alerts when key metrics drop, or creating follow-up tasks based on AI-generated insights. The possibilities go far beyond reporting. This opens the door to a smarter, more connected analytics process across your organization.

What other use cases could your team benefit from? Book a discovery call and let’s explore how to bring AI-powered reporting and automation into your workflows:

The post How to build AI-powered Excel reports with Power Automate first appeared on Stringfest Analytics.

Still, there’s a structure forming. Over the past year, the Excel ecosystem has been reshaped around a much broader vision of AI and automation. Excel is no longer a canned set of gridlines… it’s a gateway to an entire AI-powered data platform. If you can start seeing how these parts connect, you’ll be way ahead of most analysts and organizations.

Let’s walk through what I see as the emerging stack for AI and Excel, as well as some of my resources to get you started.

Power BI and Dataverse

At the foundation of the stack sits Power BI and Dataverse. Think of this as the data governance and storage layer, the place where your organization’s data actually lives and is managed. Power BI remains the visualization front end, but Dataverse is the real star for those moving beyond spreadsheets. It provides structured, secure tables of data that can be shared across Excel, Power Apps, and Power Automate without the chaos of file versions and email attachments.

In practical terms, Dataverse acts as your organization’s “truth layer.” When you connect Excel to Dataverse, you’re no longer pulling from CSVs or manually refreshing reports. Instead, you’re working directly with live, centralized data. This means your Copilot queries and Python models in Excel are referencing the same trusted data that your BI dashboards and apps do.

For a deeper dive on this, I wrote about how Excel fits into the Power Platform and Dataverse ecosystem here:

You can also check out my LinkedIn Learning course on the basics of Power BI for Excel users who are new to the Power Platform. It’s a great starting point, and the skills you’ll learn include many of the other tools mentioned in this post, like Power Automate and Copilot Studio. Definitely a keeper!

Power Query/Dataflows

Next comes Power Query and Dataflows. Power Query has long been Excel’s built-in ETL (extract, transform, load) tool, allowing analysts to clean, reshape, and combine data before analysis. But with Dataflows, this logic can be pushed into the cloud. Instead of each workbook running its own refresh process, you can define transformations once and share them across the organization.

If Dataverse is your source of truth, Power Query and Dataflows are how you make that truth usable. They standardize messy spreadsheets, merge data from multiple systems, and prepare clean tables for analysis. And since Dataflows can feed directly into both Power BI and Excel, your analysts can stay in Excel while working with enterprise-grade pipelines.

I covered how to connect Excel to Dataverse via Dataflows in detail here:

For a deeper dive into the fundamentals of Power Query in Excel, take a look at my book Modern Data Analytics in Excel:

Excel

Now we arrive at the familiar territory: Excel itself. Except this isn’t the Excel of even five years ago. Today’s Excel contains multiple AI-powered features that together form the intelligence layer of the stack: Copilot, Python, and now Agent Mode.

Copilot

This is the most visible AI layer, allowing users to generate formulas, create charts, and summarize data through natural language. It’s the first step toward conversational analytics inside the spreadsheet. You can ask it to “summarize sales by region” or “highlight outliers in this column,” and it will produce working Excel formulas or visualizations for you.

But Copilot doesn’t replace your analytical thinking. It depends on your ability to ask the right questions and recognize when its answers don’t quite make sense.

To get started with Copilot in Excel, check out my course on LinkedIn Learning:

Python in Excel

Next comes Python in Excel, which bridges the gap between Excel users and the data science ecosystem. Python unlocks advanced analytics, machine learning, and visualization capabilities directly in the workbook. You can import packages like pandas, numpy, or matplotlib and perform operations that were once out of reach for Excel alone. This means you can run predictive models, clean data programmatically, or create custom visuals, all while maintaining Excel’s familiar interface.

For a quick, practical overview of 15 ready-to-use Python in Excel examples, check out my short course on Gumroad.

What’s especially exciting is that Copilot and Python now work together through the Advanced Analysis experience. Instead of writing Python code manually, you can ask Copilot to generate it for you. For instance, you might type “show me a histogram of revenue distribution by region” or “forecast next quarter’s sales with a linear model,” and Copilot will return executable Python code that runs right inside your workbook.

To see Advanced Analysis in action, check out this post:

It’s a major leap toward making Excel a full analytics development environment: one where formula-based logic, natural language prompts, and code-based analysis coexist seamlessly.

Agent Mode

Agent Mode represents a major shift from single prompts to full reasoning workflows. Copilot is built around a one-shot model: you ask a question, it answers. Agent Mode, by contrast, uses an iterative reasoning loop that plans, executes, validates, and retries until the output meets the user’s intent. Rather than just speeding up a task,

Agent Mode can manage an entire workflow under your supervision, much like delegating to a junior analyst. This means a tool that doesn’t just write formulas for you. It can build reports, validate totals, format outputs, and so much more.

Learn more in my guide on getting started with Agent Mode here:

Office Scripts and Power Automate

Once you’ve built intelligence into your Excel processes, you’ll want to execute them reliably. That’s where Office Scripts and Power Automate come in. Office Scripts lets you record and reuse repeatable actions in Excel on the web: cleaning data, formatting tables, or updating charts. When paired with Power Automate, those scripts become part of larger workflows that run automatically, even when you’re not in Excel.

This combination is how Excel begins to extend its reach across the wider Microsoft 365 ecosystem. A workbook can now refresh data, apply formatting, run calculations, and send updates entirely on its own. A Power Automate flow might open an Excel file stored in OneDrive, trigger a script to recalculate KPIs, and post the results as a formatted summary in Teams. Another flow might collect survey responses from Microsoft Forms, append them to a central Excel table, and update a dashboard every morning. The line between spreadsheets, communication tools, and business systems becomes almost invisible once these pieces are connected.

Power Automate with Office Scripts essentially turns Excel from a static reporting tool into an active participant in your organization’s workflows. It’s where business logic meets execution.

To learn more about these two tools, check out my LinkedIn Learning courses covering each:

Copilot Studio

At the top of the stack sits Copilot Studio, the tool that connects everything else. Copilot Studio lets you build and manage custom copilots and agents that can interact with Excel, Power Automate, and external systems through connectors and APIs. If Copilot is your assistant and Agent Mode is your analyst, Copilot Studio is your command center.

With Copilot Studio, you can design domain-specific copilots that draw from your organization’s own data sources and workflows. A finance Copilot can answer questions about budget performance by querying live Excel data from Dataverse. A project management Copilot can notify stakeholders when milestones are delayed by triggering a Power Automate flow. An HR Copilot might summarize headcount changes from an Excel table or pull analytics from Power BI. In each case, the Copilot is not a static chatbot: it’s an orchestrator that understands context, retrieves information, and can take action.

The real potential of Copilot Studio lies in this orchestration. You’re doing more than just monitoring your data. You’re building systems that can reason across multiple layers of the Microsoft stack and perform tasks end to end.

For an example of how this works, see my tutorial:

What’s fascinating is that Copilot Studio uses many of the same components we’ve already discussed. Your Excel files can act as data sources, your Office Scripts can become agent actions, and your Power Automate flows can serve as orchestration layers. Excel remains the front door, but now the system behind it can reason, decide, and act.

Where it’s all heading

Right now, it’s fair to call this ecosystem messy. The boundaries between products aren’t fully clear, features are evolving fast, and documentation can lag behind the technology. But when you zoom out, the direction is unmistakable.

Excel is becoming the user interface to a much larger AI and automation ecosystem. Analysts will soon spend more time designing workflows, defining logic, and validating insights than manually crunching numbers. The winners will be those who can think across tools—connecting Power Query to Python, linking Office Scripts to Power Automate, and embedding their logic into custom Copilot experiences.

The tools are powerful, but the key is systems thinking. Your team needs analysts who understand how data flows from one layer to another, how automation can scale their work, and how to evaluate AI outputs critically. Without that mindset, you risk building disconnected tools that never deliver true value.

To see how this future might play out when it comes to Excel-based training and skills development, check out this post:

Conclusion: build your strategy now

The best thing you can do right now is get your analysts’ skills ducks in a row. Learn how these tools relate to one another. Start experimenting with small automations. Map out your data pipelines and workflows before the technology overwhelms you.

If your organization is trying to make sense of how to connect Excel, Power BI, and the Power Platform into a cohesive AI strategy, I’d love to help. You can book time with me here to talk through where you are, what you want to achieve, and how to structure a roadmap that turns this messy new world into a clear competitive advantage:

The post How to understand the modern Excel AI stack first appeared on Stringfest Analytics.

Once this connection is in place, you can start running prompts or reports on a schedule; for example, automatically generating a weekly variance report and posting it to Teams or emailing it. You could also build guardrails into your flows, such as approval steps or editable review stages, so your team can validate AI-generated insights before they’re published.

In this post we’ll connect a sales data table so that the data flows into Dataverse through Dataflows. I know that in the real world your sales data probably doesn’t live in Excel, or at least I hope it doesn’t. Excel often isn’t the best place for a single source of truth. But I also live in the real world, and I know that Excel has its advantages for powering those sources of truth. So here’s how to bridge that gap into a more developer and enterprise friendly way of doing things.

Save this workbook somewhere easy to find on your OneDrive or SharePoint. You’ll need it there for the next steps.

What is a Dataflow?

A Dataflow is a cloud-based way to extract, clean, and load data into the Power Platform. Power Apps will be our way in to set up these Dataflows, since that is where you can create and manage them to feed data into Dataverse. Dataflows use the same Power Query technology that Excel users already know, but instead of saving transformations inside a single workbook, they are stored in the cloud so multiple apps and services can reuse the same prepared dataset.

Once a Dataflow loads data into Dataverse, it becomes accessible across the Power Platform, including Power Apps for building interactive applications, Power Automate for scheduling and running flows, and Copilot Studio for creating generative AI experiences that draw on your Dataverse data.

A comparison table of using Power Query in Excel versus Dataflows in Power Apps follows.

Syncing the Excel data to Dataflows

To get started, go to make.powerapps.com and head to Tables. These data sources are actually Dataverse tables. You’ll notice that many of them are already there by default. You didn’t create them, and you can safely ignore them for now. Dataverse includes a set of standard tables out of the box like Account, Contact, and Appointment because they support core business functions used by many Power Apps solutions. For this walkthrough, we’ll focus on adding our own table by going to the top menu, selecting Import, and then choosing Import data with Dataflows.

At the top, select Import, then choose Import data with Dataflows. This is where we’ll set up our live connection between Excel and Dataverse. While there are several simple ways to import static data from Excel (for example, by uploading a CSV), Dataflows are what make the connection dynamic.

Great work! Now select Choose Data Source, then pick “Excel workbook.”

At this point, your workbook needs to be stored on OneDrive or SharePoint. Go ahead and sign in with your account, then browse to and select the workbook you want to connect.

Now things should start to look more familiar if you’ve used Power Query before. You’ll first choose which parts of the workbook to connect to. In this case, I’m connecting only to the table I need, currently named “Table1” (I really should have named it better!). Fortunately, we’ll get a chance to give it a clearer name once it’s loaded into Dataverse.

Now we are fully in Power Query, and you can see the label in the upper left corner confirming it. If you wanted to build data transformations, this is where you could do it. For now, I will skip that step and select Next to finalize the Dataflow.

Now we have a few options for how to finalize this connection into Dataverse. I am going to have it create a completely new table.

Here we can name the table, set data types, define a primary key, rename columns, and make other adjustments under the Column mapping dropdown. Each column is assumed to have a single type, just like in Power Query.

For now, I will assume Dataflows and Dataverse handle this setup correctly. If something needs to be changed later, that is fine. This process is reproducible, so we can always go back and update it.

Last, we’ll be asked how we want the Dataflow to refresh. We can set it to run on a schedule or trigger it manually. I am going to set it to refresh manually for now. Since there is a way to trigger a refresh directly within Power Automate, I’ll handle automation later as part of a broader Power Automate flow.

Great work. Now let’s test it with a manual refresh. Go back to your Excel workbook and add a test row. Then return to Power Apps and open Dataflows. You should see your active Dataflow listed, and it may take a moment to finish publishing. From there, select the three dots next to your flow. You can give it a name and choose Refresh to trigger the update.

Amazing. The new record is there. We’ve successfully set up a Dataflow that brings data from an Excel workbook into Dataverse.

Conclusion

In this post, we connected an Excel workbook to a Dataflow in Power Apps and saw how this simple step brings your spreadsheet data into the Power Platform ecosystem. Once the data is stored in Dataverse, it’s no longer limited to one workbook. It can power automations, apps, and even AI-driven summaries across your organization.

In a future post, we’ll build on this foundation by creating a Power Automate flow that pulls your Dataverse data and uses a prompt to generate a dynamic weekly summary. We’ll even have it post directly to Teams for an automated, AI-powered status update, set up an approval process for review and more. Stay tuned!

The post How to connect your Excel workbook to the Dataverse via Dataflows first appeared on Stringfest Analytics.

This post explains how to think about Agent Mode, how it compares with Copilot, and why both still matter. It also looks at where tools like Copilot Studio and Agent Flows fit into the broader Microsoft ecosystem for Excel users.

Copilot vs. Agent Mode

At first glance, Copilot and Agent Mode sound like two versions of the same thing. Both involve AI that interacts directly with Excel. In reality, they have very different design goals.

Copilot is a helper. It is designed for the small, piecemeal tasks that analysts perform every day. You might ask Copilot to clean up a dataset, write a complex formula, summarize a range, or create a quick chart. It provides targeted help within the context of the workbook you already have open.

Agent Mode is a builder. Instead of working cell by cell, it can take a broad instruction and generate a complete workbook. You might tell it to build a quarterly sales dashboard or create a forecasting model for next year. It can create sheets, link formulas, and even write explanations. It is far more autonomous and structured around end-to-end creation.

A simple comparison helps clarify the difference:

This distinction matters because it changes how you work with Excel. Copilot sits beside you while you work. Agent Mode takes your prompt, runs with it, and delivers a finished product.

Excel Copilot: When piecemeal still wins

It might sound like Agent Mode is the clear winner. After all, if it can build an entire model for you, why not use it all the time?

The reason comes down to how analysts actually work. Most of us are not starting from a blank sheet. We are maintaining workbooks that already exist. They might be forecasting models, KPI dashboards, or monthly reports that have evolved over years. They are usually mission-critical, connected to multiple data sources, and fragile in places.

In that context, incremental help is often safer and more realistic than full automation. You want a tool that can step in, understand what is there, and fix small issues without breaking the logic. Copilot handles this better right now. It can explain formulas, generate snippets, or reformat data without taking over the file.

Agent Mode, on the other hand, behaves like a blank-slate designer. It is better at starting fresh than at understanding what is already built. From what I have seen so far, it struggles when the goal is to repair or optimize an existing model “in flight.” It tries to interpret your workbook, but the context often gets lost.

Analysts know this feeling well. Sometimes it is easier to start over than to fix what is broken. That is exactly how Agent Mode currently operates. It builds something new rather than carefully weaving into the logic you already have.

The bigger picture: Copilot Studio and Agent Flows

There is also a broader shift happening in how analysts work. Excel is no longer the single destination for analysis. It is part of a much larger ecosystem.

Data now flows through Power BI, SharePoint, OneDrive, Dataverse, and external sources like SQL or Azure. Analysts collaborate in Teams or push reports through Power Automate. In that world, Agent Mode’s single-application focus stands out. It can do amazing things inside Excel but does not yet extend far beyond it.

That is why Copilot Studio and Agent Flows are such important developments. They bring the same agentic logic to the entire Microsoft 365 environment. Copilot Studio allows you to design and deploy your own custom agents that can move between apps. You can connect Excel to Outlook, Teams, or Power BI without writing a line of code.

Agent Flows take that one step further. They combine the logic of Power Automate with the intelligence of AI. Instead of following rigid “if this, then that” rules, an agent can interpret the situation and decide what to do next. It is automation that learns context rather than just repeating instructions.

Seeing the Layers

A helpful way to visualize this evolution is to think in terms of layers:

Each layer builds on the previous one. Copilot helps you perform tasks within Excel. Agent Mode automates the creation of a full workbook. Copilot Studio and Agent Flows orchestrate those agents across the broader Microsoft stack.

Practical takeaways

If you are curious about where to begin, start with Copilot in Excel. It remains the foundation for understanding how AI works inside your spreadsheets. You can take my LinkedIn Learning course on Copilot in Excel for free. The course focuses on practical, real-world examples that help you build confidence before exploring the more advanced agentic tools.

Take the course here →

Once you are comfortable with Copilot, try Agent Mode. Test how it builds reports from scratch and see where it fits into your process. Use it not as a replacement but as a design partner that can show what is possible.

If your team is trying to make sense of all this, whether it’s how to integrate these tools into your existing workflow or how to train analysts for the next generation of AI-powered Excel, I am building training sessions and advisory resources around exactly that.

You can get in touch here to discuss what your organization is exploring, or connect with me on LinkedIn for new articles, sessions, and hands-on tutorials. I am still learning myself where the biggest opportunities for organizations lie, and your feedback helps shape that journey.

The post How to choose between Copilot, Agent Mode, and Copilot Studio in Excel first appeared on Stringfest Analytics.

In this post I hope to provide an overview of what Dataverse is, and explore its benefits and limitations compared to Excel.

What is Dataverse?

Microsoft Dataverse is a robust cloud-based data management platform designed to meet sophisticated and dynamic data requirements. It provides a scalable, secure, and comprehensive solution for relational data management, integrating advanced features such as business rule enforcement, data validation, and automated execution of workflows and processes crucial for enterprise-level applications.

Dataverse is intended to serve as a central repository, facilitating connections across multiple applications within the Microsoft ecosystem, including Dynamics 365, Power Apps, and Power BI. This integration capability is critical as it enables seamless data sharing and real-time interactions across different platforms, enhancing the coherence and interactivity of data environments.

Furthermore, Dataverse ensures high levels of data security and compliance with built-in features like role-based access controls, audit trails, and data encryption. These features are essential for managing sensitive business data and adhering to various regulatory standards, providing a level of security and governance well-suited to organizational needs.

In the context of the Power Platform, Dataverse acts as a foundational component. For example, Power Apps utilizes Dataverse as the primary data source for its applications, leveraging its scalable and secure environment to enable advanced functionalities like lookup fields and role-based security. Power Automate interacts with Dataverse to streamline workflows by creating, updating, or retrieving records, thus simplifying data synchronization tasks. Similarly, Power BI can tap into Dataverse for analytics, using its tables as a robust foundation for dynamic visualizations and reports. Even chatbots developed with Power Virtual Agents benefit from Dataverse by using it as a structured knowledge base, which enhances their functionality.

Comparing Dataverse and Excel

While both Excel and Dataverse are used for data storage and analysis, their capabilities differ significantly. Excel is well-suited for lightweight tasks, quick calculations, and ad-hoc data manipulation. Dataverse, on the other hand, shines in scenarios requiring relational data management, scalability, and collaboration.

How Dataverse Integrates with Excel

Excel users can interact with Dataverse in several practical ways. Data can be imported into Dataverse tables or exported back to Excel for analysis. Using the Dataverse Excel connector, users can connect directly to Dataverse tables, enabling real-time updates and edits without leaving Excel. Additionally, Power Query allows users to load Dataverse data into Excel for advanced transformation and analysis, bridging the gap between the tools. For those building custom apps, Dataverse provides an intermediary platform that enhances the complexity and reliability of data management, surpassing Excel’s limitations.

To access these features, users need to ensure their environment includes the necessary permissions and licenses. Many Microsoft Power Platform environments include starter tables and prebuilt templates to accelerate adoption.

Advantages of Dataverse for Excel Users

Dataverse offers several distinct advantages to Excel for many use cases:

• Scalability: It handles large datasets more efficiently, accommodating growing business needs without hitting the file size limits Excel often faces.
• Relational Data Management: Dataverse allows users to define relationships between tables, enabling complex queries and multi-table reports that Excel struggles to handle in large datasets.
• Security: Provides robust role-based and field-level access controls to protect sensitive information, giving organizations tighter control over who can access and modify data.
• Automation: Seamlessly integrates with Power Automate to build advanced workflows, reducing the need for manual effort and boosting efficiency.
• Centralized Data: Acts as a single source of truth, eliminating version control issues across teams and departments, which is often a challenge when sharing Excel files.
• Collaboration: Enables real-time multi-user collaboration, making it easier for teams to work together on the same datasets without worrying about conflicting versions.

Disadvantages of Dataverse Compared to Excel

Despite its many strengths, Dataverse has some limitations when compared to Excel:

• Learning Curve: Dataverse’s relational data structure, business rules, and specific terminology may initially overwhelm users who are accustomed to Excel’s simpler, flat data structure.
• Cost: Some of Dataverse’s more advanced features, such as additional storage or premium integrations, may require extra licensing, which can be a barrier for smaller businesses or individual users.
• Dependence on Internet: As a cloud-based platform, Dataverse requires an internet connection to function, which limits its usability in offline scenarios.
• Flexibility: Excel’s grid interface and ad-hoc nature make it more flexible and intuitive for quick, one-off analysis or informal data exploration, whereas Dataverse excels in structured, large-scale, and persistent data environments.
• Complexity: For users not accustomed to database management or relational data models, Dataverse may appear more complex and require more setup time compared to the simple, spreadsheet-based approach in Excel.

Summary Table: Key Use Cases

So, which tool should you choose? As any analyst loves to say: it depends! Your preference may vary depending on the specific use case, though there are always exceptions to the rule.

Conclusion

For Excel users, Dataverse offers new opportunities for managing and analyzing data. While Excel is a go-to tool for quick data manipulation and analysis, Dataverse provides a scalable, secure, and integrated solution for more complex scenarios. Getting started is relatively simple with the right licenses and environment setup, and its integration with Excel allows users to leverage their existing skills to create more robust workflows. By combining the strengths of both tools, users can boost productivity and fully tap into the potential of the Power Platform.

However, there are a few drawbacks to consider. Dataverse can be more complex and require more setup compared to Excel, especially for users without experience in database management. Additionally, it may not be as flexible or intuitive for quick, ad-hoc analysis as Excel, which is often preferred for its simplicity and speed when working with smaller datasets or conducting informal analysis. Despite these challenges, Dataverse offers clear advantages in scalability and security for more advanced use cases..

What questions do you have about Dataverse? Have you used it, and how do you compare it to Excel? Or do you have any reservations? I’d love to hear your thoughts in the comments! I’ll also share the replay from the London Excel Meetup, where Brandon Patterson and Oakley Turvey presented “Data Steps – From Spreadsheet to Dataverse.” It was an engaging data adventure that explored the history of data, cloud vs. on-premise data storage, Excel, Dataverse, and more, including the pros and cons of each.

For a useful demo on how to manage your data in Dataverse with Power Query, check out this tutorial from Microsoft Learn.

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