In today’s fast-moving business environment, access to timely and trusted data is no longer a nice-to-have—it’s a necessity. Yet, most organizations still face hurdles that slow down decision-making and create frustrating bottlenecks.
And to be clear, we’re not talking about futuristic “real-time intelligence” from IoT or streaming data pipelines—we’re talking about something far more fundamental: the ability to quickly get reliable insights from the databases businesses already rely on every single day.
The Challenges of Accessing Data
For many business users, getting the answers they need from data looks something like this:
• Waiting for standard reports that only scratch the surface of what they truly need.
• Relying on SQL expertise to query the database for anything beyond the basics.
• Depending on IT teams or analysts to interpret and deliver answers.
These delays not only eat up valuable time but also mean that insights often arrive too late to influence critical decisions.
A New Approach: Conversational Access to Data
Now imagine an alternative: instead of waiting days or weeks for reports, a business user simply asks a question in natural language—and gets a trusted, contextual answer instantly.
With Data Agents, business leaders, analysts, and even frontline teams can access the data they need without knowing SQL, without relying on IT, and without waiting for the next report cycle.
• No SQL required.
• No long waits.
• Empowering business teams like never before.
The result? Data-driven culture, decision-making becomes truly self-service.
Building Trusted Data Agents: Beyond Just Turning On a Feature
Of course, setting up a Data Agent isn’t just about flipping a switch. To deliver reliable, trusted answers at scale, organizations need to plan, design, deliver, and continuously improve the ecosystem around their Data Agent. Let’s break down what this entails.
1. Planning and Cost Considerations
Before diving in, organizations must define clear goals and use cases. What business problems should the Data Agent solve first? At the same time, thoughtful cost planning is essential. Beyond the technology itself, budgets must account for data preparation, infrastructure, governance, and ongoing support. A Data Agent can reduce downstream costs by freeing up analysts’ time, but it requires upfront investment in design and setup.
2. Data Preparation
A Data Agent is only as good as the data it has access to. That means cleaning, transforming, and organizing your datasets before connecting them. Removing duplicates, standardizing formats, and ensuring completeness are critical steps to avoid misleading or incomplete answers.
3. Metadata Enrichment
Context is what makes data usable. By enriching datasets with metadata—like descriptions, business glossary terms, you help the Data Agent interpret questions more accurately and provide answers in the right business context.
4. Modelling and Design
Well-structured data models ensure that relationships between different entities, organizational metrics are clearly defined. Without proper modelling, Data Agents risk providing fragmented or inaccurate insights. Designing semantic models allows for richer, more intuitive answers that align with how the business actually operates.
5. Defining Agent and Data Source Instructions
Think of this as training your Data Agent. Defining role, rules, and usage boundaries helps ensure the Agent interprets user intent correctly and queries the right data sources. This is crucial for context, consistency and relevance.
6. Security and Governance
Opening up data access doesn’t mean compromising on security. Role-based permissions, data masking, and compliance checks need to be in place so that users only see the data they’re authorized to access. Equally important is governance: setting standards for how data is cataloged, consumed, and maintained ensures long-term trust in the system.
7. Delivery and Deployment Strategy
Designing a deployment strategy for Data Agents goes beyond an internal rollout—it’s about choosing the simplest and secure way to expose them where they create the most value. That could mean embedding the Agent into Microsoft Teams for seamless daily use, integrating it into a website or customer portal, creating a dedicated self-service page, or exposing it as an API so organizations can plug natural language access to data into any system they choose. A phased rollout often works best: begin with a high-impact use case, demonstrate value, then expand gradually.
8. Maintenance and Monitoring
A Data Agent isn’t “set and forget.” To stay effective, it requires ongoing care—regularly validating responses, updating data models, refining instructions, and monitoring performance to ensure it continues delivering accurate and trusted insights as business needs evolve. Metrics such as query response times, adoption rates, and user satisfaction also need to be tracked to ensure the system is delivering as intended.
9. User Feedback and Continuous Improvement
Adoption hinges on trust, and trust is built over time. Encouraging feedback from business users helps identify gaps, misunderstood queries, or areas where answers could be improved. Iterating on those insights ensures the Data Agent evolves with the business.
10. Scalability and Improvement Loops
As the organization matures in its use of Data Agents, improvements should be baked into the cycle—expanding to new data domains, refining models. A mature Data Agent doesn’t just answer questions; it proactively surfaces new opportunities.
The Path to a Self-Service Data Culture
When organizations invest in building Data Agents the right way, the payoff is huge:
• Business users get instant, contextual insights.
• Analysts spend less time firefighting ad-hoc requests and more time on strategic analysis.
• Leaders can make smarter decisions, faster.
In short, you create a true self-service data culture—where insights are no longer bottlenecked but flow seamlessly to the people who need them.
How to Get Started
💡 Our recommendation: start small. Pick a focused use case where quick wins are possible, ensure the Data Agent is delivering trusted answers, and then refine the agent, scale across teams and functions to maximize value.
We’re helping organizations set up their own Data Agents in Fabric—making data more accessible, reliable, and actionable than ever.
If you want to empower your business with instant, trusted insights, let’s connect.