Say your Supply Chain Analyst wants to know whether she needs to reorder a specific part. The inventory data she needs is in the warehouse system, the demand forecast could be pulled from the MRP, and a picture of recent consumption is in an orders spreadsheet she already has.
There are three paradigms on how she might tackle this challenge:
But how do you make it real?
For AI agents to be truly productive, they require not only expertise, but intimate knowledge of your business. That means structured, accurate data sources. No computer will be able to interact with your business in natural language unless, somehow, somewhere there is a data model describing your business in natural language. This process is often referred to as grounding. That is exactly what Digital Data Analysts do.
This blog post will define what a Digital Data Analyst is and show how Fabric Data Agents and Power BI Semantic Models fill in this gap.
A Digital Data Analyst is an AI agent that knows how to answer specific quantitative questions about your business. Your people, or other AI agents, ask questions of this agent and get specific, correct, context-aware answers with back up provided and references cited.
In the Microsoft AI & BI stack, these take the form of Fabric Data Agents. These agents are metadata rich and know how to work with your company's specific data sources to answer questions. They can look across the data available to Fabric in OneLake. But the best answers come from the well-structured Power BI Semantic Models you have already created for your reports and dashboards. Fabric Data Agents can be immediately useful to answer those ad hoc questions that arise ("How many Snickers did we sell at store 259 last year?"), but they can also into deterministic processes or multi-agent workflows in Copilot Studio or Azure AI Foundry.
In under 30 minutes, I was able to set up a Fabric Data Agent to connect to our Harvest timesheet data, which we have raw in the form of a lakehouse. Here's an example of a question I asked and the queries it was able to generate.
I started at this screen in Fabric.
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Note: There was a bit of confusion about the correct user ID because the Data Agent originally was rounding the integer-type ID result. Once we sorted that out, it worked very well. I'm sure that would never have been a problem if I had built a well-structured Semantic Model!
How would something like this make your business more efficient? Through agentic workflows.
Your business processes invoices. There's a good chance you have already automated some number of the required steps below:
All of the steps have a clear automation story, except for steps 3-4, where you need to rely on the analysis of someone who knows.
This is where the Digital Data Analyst comes in. Your pricebook data could be combined with your vendor information in a well structured semantic model. It has all the business terms, and how to measure them, baked in. It understands the relationships between vendors, pricebooks, services, and dates. Using semantic models, the Digital Data Analyst would cross-reference data from the invoice with existing price books and agreements stored in the system.
This allows them to generate precise queries and insights like:
If the agreement information is in contract form, Agents could be taught to read and structure that information in a Fabric Lakehouse. After the model is built, most of that teaching could be done in natural language. And the entire workflow could be automated by a controlling "Invoice Processor Agent".
This level of intelligence drastically reduces manual labor, optimizes decision-making, and accelerates processing while ensuring accuracy.
So that's an agentic workflow. A next question would be, "How do I then monitor and manage these agents? And what will they cost?" But that is for future blog posts.
Here’s the fantastic news for businesses already invested in Microsoft Power BI reports. If you’ve created Power BI dashboards that capture your business’s vital data, you’re closer than you think to deploying AI-driven solutions like fabric data agents.
Microsoft Fabric turns your Power BI semantic models into an Enterprise-grade Secure Semantic Layer. By putting a few Fabric Data Agents on top, your workers can already become AI-makers with Copilot Studio. These tools will allow your people to more efficiently interpret your intricate data, automate meaningful comparisons, and ensure vital decisions are data-driven.
"Microsoft Fabric turns your Power BI semantic models into an Enterprise-grade Secure Semantic Layer"
Not convinced yet? Here are five compelling reasons every forward-thinking business should bring a digital data analyst onboard or integrate AI-driven analysts into their workflows.
Traditional methods of data analysis are slow and require a heavy cognitive load to process before insights can be derived. Digital Data Analysts expedite this process by combining powerful AI tools and semantic understanding, ensuring businesses get actionable insights in seconds—not days.
AI tools like those embedded in Microsoft Fabric and Copilot are becoming indispensable. A Digital Data Analyst bridges the gap between AI capabilities and your organization’s unique needs, ensuring the technology delivers maximum value.
One of the most significant advantages of a Digital Data Analyst is their ability to ensure better, data-driven decisions. By contextualizing data specific to your business operations, these analysts make predictive analytics, scenario planning, and strategic forecasting significantly more reliable. The business context and knowledge finally becomes codified in these models.
Automation and precision are two primary outputs of their work, cutting down manual processes, reducing human errors, and enabling scale. This translates into noticeable P&L savings. For instance, automating an agentic invoice workflow reduces the cost-per-invoice processed by eliminating manual verifications. You want to empower your people to maximize their own efficiency by working with agents.
To be frank, this is becoming so easy, that this won't create a competitive advantage for you. It could create a very real competitive disadvantage if you treat AI Agents as another fad likely to blow over. The most scarce resource in any company is your best people's time and attention. Maximize this resource this by augmenting them people with AI agents and let your competitor play catch up.
Imagine a CFO armed with precise cash flow predictions from a Digital Data Analyst (a Financial Data Analyst) or a marketing team using customer behavior trends from a Customer Sentiment Analyst to tailor campaigns before launching. With the right analytics and AI, these are not hypothetical situations but achievable outcomes at scale.
Investing in Microsoft Fabric’s capabilities, enhancing your workflows with LLMs, and leveraging Copilot can create an AI-powered organization that stays ahead of the curve. With actionable insights and automated processing guiding every decision, you’re not just running a business; you’re revolutionizing it.
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The future of business decision-making lies in data, and a Digital Data Analyst is your guide to unlocking its full potential. If your organization is ready to streamline operations, improve accuracy, and harness the power of AI-driven insights, now is the time.
We’re here to help you build a secure, semantic layer tailored to your business needs, empowering you to use AI tools like Microsoft Fabric and Copilot with confidence.
Contact us today and take the first step toward transforming your data into a competitive advantage.
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