Category: Business

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.

Achieving a complete, 360-degree view of member eligibility is often challenging due to fragmented data sources and reliance on manual, email-based workflows. Current processes frequently involve static Excel or CSV files exchanged with external vendors — a method that is time-consuming, error-prone, and lacking real-time oversight. 

Business analysts and eligibility teams face the absence of a unified interface to efficiently access, validate, and update member eligibility data. These disconnected processes lead to higher operational overhead, slower turnaround times, and an increased risk of inaccuracies. 

To address these challenges, we developed an Agentic AI-powered Eligibility Update Automation solution. Leveraging Azure OpenAI, Azure Logic Apps, Azure Cognitive Services, and Semantic Kernel, the multi-agent architecture automates email monitoring, data extraction, validation, classification, bulk updates, and service request synchronization. This enables accurate, real-time eligibility updates — eliminating manual intervention while improving efficiency and accuracy. 

Problem Statement 

In a typical customer service workflow for member eligibility management, processes are heavily manual and prone to delays. Internal eligibility teams compile member details and share them with external vendors via email using Excel or CSV files. 

Vendors review and update these files with eligibility decisions — such as approved or denied — addressing scenarios like account creation and onboarding. Once returned, the internal team manually or through bulk upload updates the core application, followed by updates to corresponding service requests in the ticketing system. 

This email-driven, multi-step workflow suffers from: 

• Lack of automation 

• Limited auditability 

• No real-time visibility 

The result? Longer turnaround times, higher operational costs, and an increased risk of data errors. 

SNP’s Solution Overview 

Our AI-driven, agent-based solution is designed to eliminate manual effort, streamline communication, and ensure accurate, scalable processing of eligibility updates. 

Key capabilities within the Agentic Framework include: 

• Email Monitoring – Automatically scans incoming vendor emails, extracts relevant attachments (Excel/CSV), and routes them for processing. 

• Data Extraction & Validation – Reads and validates member updates for completeness, accuracy, and consistency, flagging anomalies. 

• Scenario Classification – Uses AI models and business logic to categorize updates (e.g., Approvals, Denials, Account Creation, Onboarding). 

• Bulk Update Preparation – Transforms validated data into application-ready formats for seamless uploads into core systems. 

• Service Request Update – Synchronizes updates in the ticketing system to maintain operational alignment. 

• Exception Management – Isolates incomplete or erroneous records for manual review, safeguarding data integrity. 

A real-time Operations Dashboard provides insights into key process metrics — including processed email counts, updated member records, pending exceptions, SLA adherence, and turnaround times. 

System Architecture 

Master Agent
Serves as the central coordinator, maintaining context and orchestrating downstream agents. Built using Azure OpenAI and Semantic Kernel for intelligent task handling. 

Email Monitoring Agent
Continuously scans inboxes, extracts vendor attachments, and securely passes files to downstream processes. 

Validation & Classification Agent
Ensures data accuracy, validates completeness, and classifies records using AI models and business rules. 

Bulk Update Agent
Formats validated records for automated integration into core systems like Mydas. 

Service Request Update Agent
Updates related service requests in ticketing systems such as Remedy Force, ensuring synchronization. 

Exception Management Agent
Flags problematic records, creates review queues, and facilitates manual intervention. 

Deployment and User Experience 

Our solution integrates seamlessly with existing customer service environments through secure enterprise interfaces. Built entirely on Azure services, it delivers compliance, scalability, and strong data governance. 

Eligibility analysts benefit from: 

• An intuitive interface that eliminates manual email handling. 

• Automated workflows that free up time for higher-value tasks. 

• Real-time dashboards for complete process visibility. 

Key Business Benefits 

• Enhanced Operational Efficiency – Automation reduces processing time and manual workload. 

• Improved Data Accuracy – Structured validation prevents human errors. 

• Real-Time Transparency – Dashboards and audit trails support SLA compliance. 

• Scalable & Modular Design – Easily extendable to new workflows and systems. 

Reference Workflow 

Conclusion 

Transitioning from manual, email-driven eligibility workflows to an AI-driven, multi-agent architecture marks a significant step toward operational excellence. By combining intelligent automation, scenario-based classification, and seamless system integration, organizations can achieve faster turnaround times, improved data integrity, and enhanced process visibility. 

This solution empowers eligibility teams to focus on strategic, high-value initiatives while ensuring accurate, compliant, and timely updates — ultimately elevating service quality and business agility.