Case StudyDriving Operational Efficiency with Dynamics 365 Sales & Customer Service

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Case StudyDriving Social Impact Through a Salesforce-Powered Assistance Ecosystem

BlogThe Cloud Security Execution Gap: Why Dashboards Lie and What a Real Assessment Reveals

Most organizations overestimate their cloud security maturity due to hidden gaps in identity, governance, and configuration. This blog explains how a structured cloud security assessment helps uncover risks and build a defensible, business-aligned security posture.

Cloud adoption has outpaced most organizations’ ability to manage cloud security effectively. What once felt like a controlled IT environment is now a complex mix of cloud platforms, identities, endpoints, and third-party integrations—often spread across Azure, AWS, and Microsoft 365.

And yet, the question that matters most remains surprisingly difficult to answer:

“Are we actually secure, and can we prove it?”

This is the exact challenge explored in our recent webinar, From Gaps to Governance: A Real-World Cloud Security Assessment, led by George Rhodes (vCISO & Security Architect) and Matthew Murdock (Practice Director, Cybersecurity). Drawing from real client environments, they unpacked what organizations are truly facing today and how to move toward a defensible, board-ready security posture.

The Reality: Cloud Security Confidence Is Lower Than You Think

Despite increased investment in cybersecurity tools and cloud platforms, most organizations operate with a false sense of security, largely driven by unresolved cloud security gaps.

As highlighted by George and Matthew:

Only 14% of security leaders successfully balance data security and business objectives (Source: Gartner)
Global information security spending was expected to exceed $212 billion and continue double-digit growth through 2026, driven by cloud adoption and expanding attack surfaces. (Source: Gartner)
Gartner predicts that 99% of cloud security failures will be the customer’s fault, primarily due to misconfigurations and identity gaps. (Source: Information Week)

These trends highlight a deeper issue: a growing disconnect between security investments and actual outcomes.

The Execution Gap: Where Cloud Security Breaks Down

One of the most critical insights from the session is what Matthew Murdock described as the “execution gap.”

This gap exists between:

What organizations believe is secure
And what is actually validated and enforced

Many of these gaps stem from overlooked cloud security misconfigurations and inconsistent enforcement of identity and governance controls.

Identity: The Most Exploited Entry Point

George Rhodes emphasized that identity remains the primary attack surface, especially in cloud environments.

In real-world scenarios:

MFA is inconsistently enforced
Privileged access is excessive
Dormant accounts remain active

In one example shared during the webinar, a compromised executive account, protected only by a weak password, led to financial fraud. Attackers didn’t break in; they logged in.

Cloud Security Misconfigurations: The Silent Risk

As Matthew pointed out, cloud platforms don’t create risk; cloud security misconfigurations do.

Organizations often struggle with:

Publicly exposed resources
Over-permissioned access roles
Inconsistent policy enforcement
Fragmented monitoring

These misconfigurations are rarely visible in dashboards but are among the most exploited vulnerabilities in modern environments, contributing significantly to ongoing cloud security gaps.

Too Many Findings, Not Enough Clarity

A recurring theme from both speakers: Organizations don’t lack data; they lack prioritization.

Security teams are overwhelmed with alerts and recommendations, but still struggle to answer:
“What should we fix first?”

Resilience: Assumed, Not Proven

George highlighted a critical gap in many organizations’ strategies – recovery readiness.

Examples shared included:

Backups that were never tested
Identity systems excluded from recovery plans
Recovery timelines that didn’t match business realities

In one case, a ransomware simulation revealed recovery delays of several days due to incomplete planning.

Why Mid-market Organizations Are Increasingly Targeted

According to Matthew Murdock, mid-market firms are becoming prime targets because they combine:

Valuable data
Increasing cloud complexity
Limited security resources
Fragmented tools and processes

These factors make mid-market cloud security environments particularly vulnerable, where enterprise-level risks exist without enterprise-level controls. As a result, unresolved cloud security gaps and unaddressed cloud security misconfigurations become easier for attackers to exploit.

What a Defensible Security Posture Looks Like

As George Rhodes explained, a defensible posture isn’t about perfection; it’s about clarity and confidence.

It means being able to clearly articulate:

What risks exist
What has been addressed
What remains
And why it matters to the business

Real-world transformations highlighted in the webinar include:

Before	After
~73% MFA coverage Dormant accounts active Limited threat visibility Days to detect compromise	100% MFA enforcement Zero dormant accounts Full visibility into risky behavior Near real-time detection

Similarly, organizations improved from:

Publicly exposed resources → Zero exposure
Fragmented logging → Centralized visibility
Untested backups → Validated recovery simulations

The Shift: From Tools to Outcomes

A key message from both speakers:

Security is not about tools, it’s about outcomes.

As Matthew emphasized, organizations must:

Test recovery, not just deploy controls
Measure business impact, not just technical metrics
Translate security investments into risk reduction

And most importantly, assign clear ownership.

The Solution: A Real-World Cloud Security Assessment

To close the execution gap, organizations need more than dashboards; they need validated insight.

A structured cloud security assessment helps organizations identify hidden cloud security gaps, validate configurations, and proactively remediate cloud security misconfigurations before they lead to incidents.

The Synoptek Cloud Security Assessment, as outlined by George and Matthew, focuses on:

Identity security (Entra ID, MFA, access control)
Cloud governance (Azure, AWS configurations)
Endpoint security
Framework alignment (NIST, CIS)
Risk-based prioritization

What sets it apart:

Evidence-based validation (not just scores)
Non-intrusive approach
A prioritized remediation roadmap

What You Walk Away With

Organizations completing this assessment gain:

A clear understanding of their security maturity
Visibility into cloud security gaps across cloud and identity
A prioritized action plan
A defensible baseline for leadership and boards

And this clarity can be achieved in just 3–5 weeks.

Don’t Wait for a Breach to Expose the Gaps

As George Rhodes put it, organizations don’t need to wait for an audit or worse, a breach, to understand their risk.

The difference lies in:

Validating what’s actually working
Prioritizing based on real impact
Aligning security with business outcomes

Watch the Full Webinar & Take the Next Step

To hear directly from George Rhodes and Matthew Murdock and explore real-world examples in more depth:

Watch the on-demand webinar >

And if you’re ready to move from uncertainty to clarity:

Take our Cloud Security Assessment

In just a few weeks, you can:

Identify hidden risks
Prioritize effectively
Build a stronger, defensible security posture

Turn your security posture into a business advantage >

Security isn’t about eliminating every risk. It’s about being able to confidently explain your risk posture to leadership and act on what matters most. That’s the shift from gaps… to governance.

BlogAI Pilot to Production: Why Most Enterprise AI Efforts Stall

A strategic look at why enterprise AI initiatives stall at the pilot stage, and the barriers that block scalable, measurable business value.

AI has quickly become one of the largest line items in the modern enterprise technology budget. Goldman Sachs estimates $4 trillion to $8 trillion of total capital investment over the next five years. For leadership teams across industries, the mandate is clear: turn AI into a competitive advantage.

Despite heavy investment, a large share of enterprise AI initiatives fail to deliver measurable business value at scale. According to an article by Forbes, 95% of corporate AI initiatives show zero return.

Core systems may be in place, yet value remains fragmented across operations, data, and workforce productivity. Data readiness and governance gaps continue to limit trust in AI outputs, raising concerns around safe, scalable deployment and reinforcing the need for a robust agentic AI governance framework. These challenges are further amplified in environments without a clear AI transformation roadmap or a unified data layer.

This growing disconnect defines the AI Impact Gap and is becoming increasingly evident across enterprises transitioning from AI pilot to production.

The Hidden Cost of AI That Doesn’t Deliver

Imagine allocating millions toward AI transformation, deploying Copilot to help sales teams prioritize high-value deals, embedding AI agents in customer service to reduce response times, and automating finance workflows to minimize reconciliation errors.

Yet, months later, the reality feels very different from what was expected. In addition to wasted investment or delayed timelines, there is a gradual erosion of confidence in what AI is supposed to do for the business, especially when the underlying data ecosystem is not unified through platforms like Microsoft Fabric. This is where organizations begin asking a critical question: how to scale AI beyond pilots in a way that delivers sustained value?

The Early Warning Signs Leaders Shouldn’t Ignore

Before AI initiatives stall completely, there are usually signals that start subtly and then become difficult to overlook:

AI conversations remain focused on tools rather than outcomes
Different teams pursue disconnected use cases with limited coordination
Data quality concerns begin to influence trust in AI outputs
ERP and operational systems feel like constraints rather than enablers
Employees experiment with AI, but do not rely on it in daily workflows
Leadership struggles to connect AI efforts to financial impact, often due to a lack of formal AI readiness assessment.

Individually, these may seem manageable. Collectively, they point to a deeper issue where AI is still not embedded in how the business actually operates in a consistent, repeatable, and value-driving way.

The Real Reasons AI Pilots Fail in the Enterprise

When organizations examine why AI is not delivering, the answers are rarely simple. The root causes tend to span technology, data, operations, and culture, intersecting in ways that are easy to underestimate early on.

1. Fragmentation Across Systems

Most enterprises have built their technology landscape over time, with ERP systems, analytics platforms, collaboration tools, and cloud infrastructure layered together in different phases of modernization. Without a unifying data platform, AI initiatives launched within one area of the business often struggle to extend into another. Insights remain localized, decisions remain fragmented, and value creation becomes uneven across the organization.

2. Data That Isn’t Ready for the Demands of AI

Many organizations continue to operate with data that is distributed across multiple systems. Without a strong agentic AI governance framework, AI outputs become difficult to trust at scale. Leaders hesitate to act on insights that are not fully explainable or consistent, while teams spend more time validating outputs than applying them. Over time, this gradually weakens confidence in the data and the decisions that depend on it.

3. ERP Systems That Can’t Keep Up

ERP systems sit at the center of enterprise operations, but have evolved through years of customization and extension. Without AI-first ERP modernization, organizations face challenges in how deeply intelligence can be embedded into core business processes. As a result, integrating new capabilities becomes more complex, and scaling them across the enterprise demands far greater effort than originally anticipated.

4. A Focus on Activity Instead of Impact

While moving an AI pilot to production, many organizations track progress through activity-based indicators such as the number of deployments, pilots completed, or features rolled out across different functions. While these milestones are important from an execution standpoint, they do not always translate into measurable business outcomes such as revenue growth, cost efficiency, improved cycle times, or reduced operational risk. As a result, AI initiatives risk becoming activity-driven rather than impact-driven, which makes it difficult for leadership teams to confidently prioritize investments or scale initiatives with clarity.

5. AI That Sits Outside Real Workflows

Even when AI capabilities are available, their value is often limited by how and where they are introduced into the organization. In many cases, AI tools exist alongside daily workflows rather than within them, which means they are used intermittently rather than becoming part of how work is performed. Solving this is central to understanding how to scale AI beyond pilots and drive real operational change.

A Real-World Pattern: Where Things Start to Break Down

Consider a mid-sized enterprise that has implemented Dynamics 365 as its core ERP platform, alongside cloud infrastructure and productivity tools rolled out across the workforce. It has begun layering in AI capabilities as part of its Dynamics 365 AI modernization journey.

On paper, everything seems aligned. In practice:

Finance teams still rely on manual reconciliations despite automation tools
Operations teams question the accuracy of AI-driven forecasts
Data teams spend more time preparing data than enabling insights
Employees try AI features occasionally, but do not depend on them

A Conversation Worth Having Now

A clear divide is starting to emerge. On one side are organizations still experimenting with AI. On the other hand are those operationalizing it, supported by integrated ecosystems that combine ERP, AI, and data platforms.

The AI Impact Gap is not always visible in dashboards or quarterly reports. It shows up in hesitation, in stalled momentum, and in initiatives that never quite scale. Closing the AI Impact Gap demands clarity on how to scale AI beyond pilots, a strong AI readiness assessment, and alignment across systems, data, and people.

This is exactly the conversation we are bringing to our upcoming session with speakers from Synoptek and Microsoft.

Becoming a Frontier Firm with Microsoft AI

May 21, 2026 • 9:00 AM PT

If your organization is investing in AI but still navigating pilots, fragmented systems, or unclear outcomes, this session will explore what may be happening beneath the surface and why.

BlogWhy CIOs Must Architect for Experience: The Strategic Shift from SLA to XLA

To solve the challenge of fragmented IT silos, CIOs are adopting experience-led IT strategies. This framework replaces traditional SLAs with Experience Level Agreements (XLAs)—using AI-driven IT modernization to measure and optimize the Digital Employee Experience (DEX). The goal is a seamless, integrated architecture that reduces digital friction and drives high-velocity business results.

In today’s era of modern business, a CIO’s performance is no longer judged solely by the silence of the servers, but by the velocity and agility of the workforce. We have entered an era where “technical uptime” is the baseline expectation, yet “digital friction” remains a silent killer of enterprise value.

For the C-suite, the hard truth is this: Experience is not a “soft” HR metric or a front-end design flourish, it is a rigorous architectural requirement. This requires a fundamental SLA vs XLA shift. While SLAs (Service Level Agreements) tell you if the system is “up,” XLAs tell you if the system is “working” for the human beings using it.

Experience is not created by individual tools; it is forged in the way those tools interact. A managed experience provider understands that world-class user sentiment is the direct result of intentional, integrated IT design. To stay ahead, CIO IT strategy 2026 must pivot from being “Chief Infrastructure Officers” to “Chief Experience Architects,” treating integration and operational efficiency not as back-office tasks but as non-negotiable

The Myth of the Silver Bullet: Why Tools Alone Fail

Many organizations are attempting to modernize their technology, but they often fall into the trap of “tool sprawl.” They invest millions in best-in-class CRM platforms, cloud storage solutions, and high-end communication suites, yet their employees remain frustrated and disengaged.

The reason is simple: fragmented environments undermine outcomes.

When technology systems exist in functional silos, the data cannot flow, and the work becomes disjointed. This is what we call the “Experience Gap.” A user should not have to navigate five disparate systems or perform manual data entry across multiple platforms to get a single task done. An experience-led IT strategy recognizes that the connections between the tools are just as critical as the tools themselves. Without deep integration, you are not building a system that works well; you are building a digital obstacle course that slows down your best employees.

The Pillars of Experience-Driven Architecture

Architecting for experience requires a fundamental shift in how we plan IT environments. It moves the focus from “Component Health” to “Workflow Velocity.”

1. Integration as the Nervous System

For a CIO, this means prioritizing API-first architectures and making sure all systems can share data seamlessly. When IT infrastructure is well-integrated, AI-driven IT modernization can truly take root. Artificial intelligence cannot provide meaningful insights if it is blind to siloed data. By integrating your systems, you provide the “fuel” that AI-driven managed services need to automate complex tasks and personalize the DEX (Digital Employee Experience).

2. The SLA vs. XLA Shift: Measuring What Matters

The most critical pillar is moving from technical uptime to human outcomes.

What is an SLA? A Service Level Agreement measures technical outputs (e.g., “Is the email server up 99.9% of the time?”).
What is an XLA? An Experience Level Agreement measures the human outcome (e.g., “Can the employee find information and complete their task in under two minutes?”).

While SLAs tell you if the system is “on,” XLAs tell you if the system is “productive.” This shift eliminates the “Watermelon Effect”—where IT metrics look green (healthy) on the outside, but user frustration is red (high) on the inside.

3. Operational Efficiency as a Core UX Metric

In an experience-led IT strategy, efficiency is not just about saving money; it is about making the system work better for the people using it.

Less Friction: When a system is efficient, users do not have to deal with repeated logins, applications respond quickly, and work flows smoothly.
Proactive Support: An efficient operational model uses Experience-driven IT management to identify and resolve performance bottlenecks before the users even notice a problem.

When a system is inefficient, the “noise” of IT; the constant stream of updates and minor downtimes leaks into the user’s day. True operational excellence makes technology “invisible,” allowing users to focus entirely on their work.

Moving from Fixing Problems to Designing a Good System

The traditional way of managing technology is reactive: wait for something to break and then fix it. A company that focuses on the user experience designs the system to work well from the start. They begin with the desired business outcome driven IT services and then design the infrastructure required to make it happen.

This is why many CIOs are working with a managed experience provider. These partners do not just manage the technology; they make sure it is aligned with overarching business goals. By using intelligence to modernize the technology, these providers can help a CIO eliminate legacy debt and move to a system that is intentionally designed for the users.

The Cost of Inaction: Fragmentation and Frustration

If companies do not design their technology systems to work well for their people, they will pay the price. In 2026, the cost of fragmented systems will no longer be hidden in the technology budget; it will be visible on the balance sheet. Organizations that fail to architect for experience will face:

Slow Digital Progress: An inability to launch new features because their systems are not flexible enough to support rapid change.
Losing Good Employees: High-value employees will get frustrated with the technology and leave the company.
Higher Costs: Fragmented systems require more manual work, redundant licenses, and complex security patches, which drives up the Total Cost of Ownership (TCO).

Conclusion: Lead with Experience, or Be Left Behind

The role of the CIO has fundamentally changed. It is no longer about keeping the technology running; it is about making sure the technology works well for the people. Designing technology systems to work for people is not a one-time project; it is a continuous strategic process that marks the frontier of leadership.

It requires a relentless focus on integration and operational efficiency, often achieved by working with a managed experience provider. By moving from managing silos to architecting ecosystems, CIOs can finally deliver the consistent, high-velocity experiences that define the winners in the digital economy.

In 2026, the mandate is clear: Lead with experience or be left behind.

The future of IT isn’t in the hardware; it’s in the experience you build upon it.

On-demand WebinarFrom Gaps to Governance: A Real-world Cloud Security Assessment

As organizations grow, prepare for audits, or undergo due diligence, security expectations rise quickly. Yet critical gaps—such as identity misconfigurations, privilege sprawl, inconsistent cloud governance, and weak endpoint enforcement—often remain hidden until they are exposed by an audit, a security incident, or investor scrutiny.

In this expert-led session, Synoptek cybersecurity leaders demonstrate how organizations can proactively identify and quantify security maturity gaps across identity, cloud, and endpoint environments—before regulators, auditors, or attackers do.

You’ll learn how a focused 3–5 week security assessment can establish a defensible security baseline, align to frameworks such as NIST, SOC 2, ISO 27001, and Zero Trust, and deliver a prioritized roadmap for reducing risk—without disrupting business operations.

Why This Matters Now

Security risk today is increasingly driven by identity compromise, cloud misconfigurations, and inconsistent enforcement of access controls. At the same time, organizations face growing pressure from regulators, auditors, boards, and private equity stakeholders to demonstrate a clear, measurable, and defensible security posture.

Many organizations rely on fragmented tools, dashboards, or security scores to assess their environment. However, without a structured, framework-aligned view of security maturity, critical gaps often remain undetected until they surface during an audit, breach, or due diligence process.

Understanding where you stand—and what to prioritize next—is essential to reducing risk, strengthening governance, and ensuring your security strategy can withstand both operational threats and external scrutiny.

Key Takeaways

Why regular security assessments are critical for audit and compliance readiness
How identity-driven attacks and cloud misconfigurations create hidden risk exposure
What a defensible, framework-aligned security baseline looks like in practice
How to prioritize remediation efforts based on risk, not just severity scores
Steps organizations can take now to reduce ransomware exposure and improve resilience

Our Approach

Unlike tool-based assessments or automated scans, this approach provides a framework-aligned, business-contextual view of security risk.

By evaluating identity, cloud control planes, and endpoint enforcement together, organizations gain a complete picture of their security posture—enabling leadership to make informed, defensible decisions and prioritize what matters most.

BlogManaged IT Services: How Outsourced Helpdesks Boost Productivity

For the modern professional, work is no longer a physical place; it is a digital experience. Whether your team is collaborating from a high-rise building, a home office, or a satellite branch in another city, their ability to deliver results depends entirely on the seamless performance of their technology. When that technology falters – a cloud sync error, a forgotten password, or a malfunctioning VPN, the result isn’t just a “technical glitch.” It is a full stop on business momentum.

While many organizations attempt to manage these disruptions with a small in-house team, the sheer volume of support required in a hybrid world often leads to burnout and delayed resolutions. Transitioning to a professional Managed IT Services Provider (MSP) isn’t just about “outsourcing your problems”; it’s about installing a 24/7 productivity engine that empowers your workforce to stay in their flow state.

The Quantifiable Cost of Tech Frustration

Digital friction is a silent thief of time. Recent 2025 research from Gartner revealed that while 100% of organizations were pushing for digital growth, only 23% of digital workers were completely satisfied with their work applications—a significant drop from 30% just two years ago. This satisfaction gap is a primary driver of “digital friction,” which occurs when tools remain isolated and rigid rather than intuitive.

For a growing company, this downtime represents a massive drain on resources. By utilizing a managed IT company for helpdesk support, organizations can finally address the “70% Problem,” where most of the IT energy is spent on basic maintenance rather than strategic growth. This shift is becoming mission-critical as Gartner forecasts that worldwide IT spending will total $6.15 trillion in 2026, an increase of 10.8% from 2026. As companies race to optimize their service delivery models and integrate generative AI at scale, those trapped in a reactive maintenance cycle will find themselves financially and operationally left behind.

Why Partner with an IT Managed Services Provider (MSP)?

An IT managed services provider offers more than a call center; they provide a comprehensive support ecosystem that scales with your business needs.

1. 24/7/365 Resilience

The modern work week doesn’t end at 5 PM on a Friday. For teams across the globe, support must be immediate and constant. A professional MSP provides a “follow-the-sun” model, ensuring that an IT managed services provider is always available to resolve a crisis, whether it’s at midnight on a Tuesday or noon on a holiday.

2. Specialized Managed Application Services

Productivity today is tied to the specific performance of SaaS tools and enterprise applications. Specialized providers offer managed application services that go beyond basic hardware fixes. They ensure your CRM, ERP, and collaboration suites are configured for peak performance. As noted in our research on Operational Excellence with AI-Enabled Managed Services, the proper integration of AI within these applications is now the single biggest differentiator in operational speed.

3. Sustainable and Reduced TCO

Managing a modern helpdesk in-house requires expensive software licenses, continuous training on emerging threats, and high salaries for specialized talent. IT outsourcing services consolidate these costs.

The Power of Local Managed IT Services

While the technology may be in the cloud, the accountability should be local. Many organizations prioritize locally available managed IT services providers because they understand the specific regulatory and infrastructure landscape of their area. Whether you require support in Costa Mesa, Saint John, or Denver, having a partner with a local presence ensures a higher level of trust.

A managed IT company with a local footprint can offer “boots on the ground” for hardware deployments or complex site migrations that remote teams simply cannot handle. This hybrid support model is a proven factor in driving efficiency. For example, in this Case Study, a mental healthcare and welfare agency was able to stabilize its entire tech environment in less than 90 days. By shifting to a managed model, they advanced from a maturity level of zero to two, allowing staff to stop “working around” technical problems and focus entirely on care delivery, which saved each provider an estimated 160+ hours of productivity annually.

From “Fix-it” to “Proactive Prevention”

The most advanced managed IT services don’t wait for the phone to ring. They utilize AI-powered IT operations to detect failures before they happen.

Proactive Monitoring: Identifying a failing server or a security vulnerability before it impacts the user.
Automated Remediation: Using scripts to fix common issues like password resets or software updates, without human intervention.
Trend Intelligence: Analyzing ticket data to identify if a specific office (like Atlanta) is having recurring network issues, allowing for a permanent fix rather than a temporary patch.

Conclusion: Empowering the Future of Work

In a world where talent is hard to find and even harder to keep, the digital experience you provide your employees is a key part of your value proposition. Viewing IT support as a “back-office cost” is an outdated mindset that hinders growth.

By investing in managed IT services and expert IT Outsourcing Services, you are giving your team the one thing they need most to succeed: time. Whether it’s through local managed IT services that provide a personal touch or AI-powered IT operations that work silently in the background, the right partnership transforms your IT infrastructure from a burden into a catalyst for innovation. As we move toward 2026, the businesses that will dominate their markets are those that have eliminated the friction between their people and their potential.

BlogAzure Synapse Link for Dataverse: Why Your Data Isn’t Keeping Up

A practical guide to eliminating dashboard latency by replacing traditional ETL with near real-time analytics using Azure Synapse Link for Dataverse and Microsoft Fabric.

Imagine your sales team closes a high-value deal at 10 AM, but your leadership dashboard doesn’t reflect it until the next day. By the time insights arrive, the opportunity to react is already gone. This is the reality for many organizations still relying on traditional ETL pipelines.

Modern enterprises can’t afford such delays, especially when working with operational data. Azure Synapse Link for Dataverse eliminates that bottleneck by enabling near real-time data replication directly into analytics platforms, so you can run BI, reporting, and machine learning on fresh data without impacting your transactional systems.

It ensures that the same deal is visible in your analytics dashboards within minutes. Your leadership team can instantly track performance, your marketing team can adjust campaigns in real time, and your operations team can respond proactively.

This blog explores how Azure Synapse Link for Dataverse works, its key features and setup, and how it compares to Microsoft Fabric Link for enabling real-time, ETL-free analytics on Dataverse data.

What is Azure Synapse Link for Dataverse?

Azure Synapse Link for Dataverse enables near real-time replication of Dataverse data into Azure Data Lake Storage Gen2 and Azure Synapse Analytics, eliminating the need to build traditional ETL pipelines. It enables advanced analytics, business intelligence, and machine learning scenarios directly on your operational data.

Data is stored in the open Common Data Model format, ensuring semantic consistency across apps and deployments. Using Delta Lake / Parquet as the storage format, your data is always ready for query via Synapse SQL Serverless, Dedicated Pools, Spark, or Power BI.

Azure Synapse Link overview — continuous export from Dataverse to Azure Data Lake

Image reference

End-to-end data flow: Dataverse → Delta Lake → Analytics / Power BI

Key Features

Near Real-Time Sync: Changes in Dataverse are reflected in your data lake within minutes using efficient change tracking.
Zero Performance Impact: Replication runs asynchronously, so your transactional Dataverse system is never affected.
Automatic Schema Management: Schema changes in Dataverse are automatically propagated to your Delta Lake tables.
Delta Lake Format: Data is stored in open, industry-standard Delta/Parquet format for maximum interoperability.
Multi-Tool Integration: Works natively with Synapse SQL, Apache Spark, and Power BI.
Enterprise-Scale: Designed for large datasets with disaster recovery capabilities and high availability SLAs.

Real-World Scenario

Imagine a global retailer running Dynamics 365 Sales. Their sales team creates thousands of opportunities and orders every day. The analytics team needs daily (or even hourly) reports in Power BI without impacting CRM performance.

Without Synapse Link

Manual Dataverse exports to datalake or fragile ETL jobs running overnight
Reports are always 12–24 hours behind
ETL failures cause missing data in dashboards

With Synapse Link

Changes in Dataverse (Opportunity, Order tables) flow to ADLS in minutes
Power BI connects to Synapse SQL Serverless for always-fresh reports
No ETL pipelines to build or maintain, just point and query

Prerequisites

Active Azure subscription with an ADLS Gen2 account
Azure Synapse Workspace in the same region as your storage account
Synapse Administrator role within Synapse Studio
Storage Blob Data Contributor / Owner roles on the storage account
Dataverse System Administrator role; tables must have Track Changes enabled

10-Step Configuration

Create a Resource Group in Azure Portal

Create a Storage Account with Hierarchical Namespace (ADLS Gen2) enabled

Create an Azure Synapse Workspace linked to that storage account

Assign IAM role: Storage Blob Data Contributor to the Synapse workspace

Open Power Platform Admin Center → Azure Synapse Link

Select + New Link → Connect to your Azure Synapse workspace

Choose tables for Dataverse export to data lake

Monitor initial sync status on the Tables tab

Validate Delta / Parquet files in ADLS via Storage Explorer

Query your data with Synapse SQL Serverless or Power BI

What is Link to Microsoft Fabric?

Link to Microsoft Fabric takes data integration a step further, connecting Dataverse directly to Microsoft OneLake via shortcuts, eliminating the need for any storage account or Synapse workspace configuration.

The Link to Microsoft Fabric feature built into Power Apps makes all your Dynamics 365 and Power Apps data available in Microsoft OneLake, the built-in data lake for Microsoft Fabric. Data stays in Dataverse, with shortcuts created directly into OneLake so authorised users can work with it across all Fabric workloads.

Key Features

No Data Duplication: Data stays in Dataverse while Microsoft Fabric accesses it via secure shortcuts, eliminating the need for copies or data movement.
Near Real-time Access: Changes in Dataverse are instantly available in Microsoft OneLake, enabling up-to-date analytics without ETL delays.
Shortcut-based Architecture: OneLake shortcuts provide seamless, governed access to Dataverse data without physically storing it again.
Unified Analytics Experience: Access data across all Fabric workloads, Lakehouse, SQL endpoint, and Power BI DirectLake, from a single platform.
Native Dynamics 365 Integration: Works seamlessly with Microsoft Dynamics 365, including Finance & Operations apps, ensuring full business visibility.
Simple, Wizard-driven Setup: Configure the link directly from Power Apps using a guided experience; no infrastructure or pipeline setup required.
Optimized for Self-service BI: Enables business users to build real-time dashboards and reports quickly without relying on complex data engineering workflows.

Real-World Scenario

Imagine your customer support team logs a critical complaint at 2 PM in Microsoft Dynamics 365. The issue is about a product defect affecting multiple customers. With traditional systems, your support managers and leadership might only see this trend the next day, after dozens more complaints pile up.

Without Fabric Link

Customer cases are analyzed using scheduled ETL jobs
Reports in Power BI are delayed by hours or even a day
Teams react late to spikes in complaints
Root cause analysis happens after customer impact escalates

With Fabric Link

The moment a case is created in Dataverse, it becomes available in Microsoft OneLake via shortcuts
No data movement or duplication—Fabric reads data directly
Power BI DirectLake dashboards reflect new cases almost instantly
Support leaders see a spike in complaints within minutes

Prerequisites

System Administrator role in the Power Platform environment
Power BI workspace administrator access
Power BI Premium or Fabric capacity (same Azure geo as Dataverse)
Permission to create Fabric lakehouses and artifacts
Fabric Tenant Settings: Fabric items & workspace creation enabled
OneLake settings: External app data access enabled
Permissions to manage connections via Settings → Gateways

5-Step Configuration

The wizard-driven setup makes connecting to Fabric straightforward:

1. Open Power Apps → Tables → Analyze → Link to Microsoft Fabric.

Sign in to make.powerapps.com, select your environment, go to Tables, then choose Analyze → Link to Microsoft Fabric on the command bar.

2. Run the validation wizard.

The wizard checks prerequisites and Fabric subscription settings. If capacity isn’t available in your region, you’ll be guided to provision one.

3. Select a Fabric workspace and authentication method.

Choose your workspace and pick from Workspace Identity, Service Principal, or Organizational Account authentication.

4. Link tables automatically.

All Dataverse tables with Track Changes enabled are linked. Finance & Operations tables can be added later via Manage Tables.

5. Select Create.

Fabric Lakehouse opens automatically. The system creates a Fabric Lakehouse, SQL endpoint, Power BI dataset, and shortcuts. The lakehouse opens in a new browser tab when ready.

Dataverse direct link to Microsoft OneLake/Fabric ecosystem

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Synapse Link vs. Fabric Link: Which to Choose?

Choosing between Azure Synapse Link and Link to Microsoft Fabric depends on your data architecture, analytics needs, and preferred ecosystem. Both options eliminate ETL and enable near real-time access to Dataverse data, but they differ in setup complexity, storage approach, and analytics capabilities. Here’s a quick Synapse Link vs Fabric Link comparison to help you decide:

Feature	Synapse Link	Link to Fabric
Storage	Azure Data Lake Gen2	Microsoft OneLake
Format	Delta / Parquet	Delta Parquet (native)
No ETL pipelines	✓	✓
Query Engine	Synapse SQL / Spark	Fabric SQL / Spark / Power BI
Setup Complexity	Moderate; requires ADLS + Synapse workspace	Simple, wizard-driven with no storage needed
Data Copies	Data is replicated to ADLS	No copies, data stays in Dataverse
Best For	Enterprise analytics, ML	Quick BI, Fabric ecosystem

7 Common Mistakes When Implementing Azure Synapse Link for Dataverse

While Azure Synapse Link for Dataverse and Microsoft Fabric Link simplify near real-time analytics, many teams run into avoidable issues during setup and adoption. Understanding these common mistakes can help you get the most out of your implementation.

1. Treating Synapse Link Like a Traditional ETL Pipeline

One of the biggest misconceptions is assuming Azure Synapse Link for Dataverse works like ETL.

There’s no transformation layer built in
Data is replicated as-is into your data lake
Transformations must be handled downstream (e.g., Synapse SQL or Spark)

What to do instead: Design a separate transformation layer for business logic rather than expecting Synapse Link to replace ETL entirely.

2. Ignoring Data Modeling and Schema Design

Teams often rely on raw Dataverse tables without optimizing for analytics.

Highly normalized schemas can slow down reporting
Relationships may not be BI-friendly out of the box

What to do instead: Create curated views or star schemas in Synapse to improve Power BI performance and usability.

3. Not Enabling Track Changes on Required Tables

A common setup issue is forgetting to enable Track Changes in Dataverse.

Tables without change tracking won’t sync
Leads to incomplete or inconsistent datasets

What to do instead: Validate that all required tables for your Dataverse export to data lake scenario have change tracking enabled before setup.

4. Underestimating Storage and Query Costs

Although Azure Synapse Link for Dataverse removes ETL overhead, it doesn’t eliminate costs.

Data replication increases storage usage in ADLS
Poorly optimized queries in Synapse Serverless can increase costs

What to do instead: Implement partitioning, optimize queries, and monitor usage to control costs effectively.

5. Choosing the Wrong Architecture (Synapse Link vs Fabric Link)

Many teams jump into Synapse Link without evaluating whether Microsoft Fabric Link is a better fit.

Synapse Link introduces infrastructure complexity
Fabric Link offers a simpler, no-copy architecture

What to do instead: Evaluate your use case carefully

Use Synapse Link for advanced analytics and data engineering
Use Fabric Link for faster, BI-focused implementations

6. Expecting True Real-Time Instead of Near Real-Time

Synapse Link provides near real-time, not instant streaming.

Data latency can still be a few minutes
Misaligned expectations can confuse stakeholders

What to do instead: Set clear expectations with business teams about refresh intervals and latency.

7. Skipping Governance and Access Controls

Because data is replicated into a data lake, governance becomes critical.

Sensitive data may be exposed if not properly secured
Lack of role-based access can create compliance risks

What to do instead: Apply proper IAM roles, data masking, and governance policies in your Azure environment.

Wrapping Up

Most teams don’t struggle with data; they struggle with timing. By the time dashboards update, the moment to act has often passed. Azure Synapse Link for Dataverse and Link to Microsoft Fabric represent a modern, no-ETL approach to enterprise analytics. Whether you need heavy-duty Synapse SQL querying for data science workloads or a fast wizard-driven setup for Power BI reports via Fabric, Microsoft provides a native integration path that keeps your data fresh, secure, and ready to use.

If your decisions still depend on yesterday’s numbers, it’s worth rethinking your setup. Start small; explore Synapse Link vs Fabric Link and bring your Power BI reports closer to real time. The faster your data flows, the faster your business can respond.

About the Author

Parth Shah

Technical Manager

Parth Shah is a Technical Manager at Synoptek with strong expertise in Business Intelligence (BI) and data platform technologies, bringing extensive experience in end-to-end project delivery. He plays a key role in designing and implementing scalable data solutions, with deep specialization in data warehousing and Microsoft SQL Server.

His experience spans the full data lifecycle, including data ingestion, storage, transformation, and consumption. He has strong expertise in integrating data from Microsoft Dynamics 365 Finance & Supply Chain Management, building modern data warehouses using Azure Synapse Analytics and Microsoft Fabric Link, developing semantic models, and enabling advanced analytics through interactive dashboards and reporting solutions.

BlogThe Role of AI and Automation in Delivering Managed Experiences

Moving to a Managed Experience Provider (MxP^TM) model delivers more than just technical uptime; it drives a 2x improvement in digital velocity and up to a 50% reduction in TCO. By replacing reactive “if-then” scripts with agentic AI and predictive IT operations, organizations can automate business outcomes rather than just tasks. This experience-led IT strategy ensures technology remains an invisible accelerator, allowing internal teams to shift from maintenance to strategic innovation.

For years, the promise of “automation” in IT was largely restricted to scripts that performed repetitive tasks, backups, patch deployments, and basic alerts. While helpful, this traditional approach was still reactive. It required a human to define the problem before the machine could execute the fix. However, as we enter Q2 of 2026, the complexity of the modern digital estate has outpaced human-only management.

The emergence of the Managed Experience Provider (MxP™) marks a definitive shift in this narrative. By integrating AI-powered IT operations at the core of service delivery, an MxP goes beyond just automating tasks; it automates outcomes. This is the engine behind “Invisible IT”; a state where technology functions so seamlessly that the user never has to think about the infrastructure supporting it.

From “If-Then” to “Agentic AI”: The MxP Evolution

Traditional automation follows “if-then” logic. AI-driven managed services, however, utilize Agentic AI; systems capable of making independent, intent-based decisions to maintain the health of an environment.

By the end of 2025, Forrester noted that firms shifted from AI experimentation to a business imperative, with Agentic AI representing the next frontier in sophisticated automation. In an MxP model, this means the system doesn’t just alert a technician when a database slows down; it analyzes the traffic pattern, identifies the root cause, and autonomously reallocates resources to resolve the lag before it impacts the user’s Experience Level Agreements (XLAs).

Predictive Resilience: The End of the Reactive Ticket

The most visible sign of a successful Managed Experience Provider is the decline of the support ticket. When predictive IT operations are active, the system is constantly scanning for “pre-incident” signals.

According to Gartner, the AIOps market is projected to grow significantly as large enterprises, which will account for over 52% of the market share by 2026, and seek to reduce system downtime through AI-led solutions. At Synoptek, this is realized through the Synoptek aiXops™ Platform, which combines decades of ITIL best practices with machine learning to provide:

Self-Healing Systems: Automatically fixing common configuration drifts or software glitches.
Unified Visibility: Correlating data across the cloud, applications, and security to find hidden bottlenecks.
Proactive Modernization: Identifying legacy components that are likely to fail or cause digital friction in the next quarter.

Quantifying the Impact: Velocity and Value

Why does the “experience” matter more than the “service”? Because experience correlates directly to the bottom line. Organizations leveraging an MxP framework are achieving a 2x improvement in digital velocity, the speed at which they can move an idea from concept to production.

By removing the manual “toil” of IT management, AI and automation deliver a dual benefit:

Reduced TCO: Automation reduces the human labor required for “level 1” support, contributing to up to a 50% reduction in total cost of ownership.
Innovation Surplus: Freed from maintenance, internal IT teams can focus on strategic projects that drive the 1700% increase in revenue seen by some experience-led organizations.

The Human Element: Training for the AI Era

A common misconception is that AI-powered IT operations replace the need for human expertise. They elevate it. In an MxP environment, the “Managed” part of the experience is delivered by experts who use AI as a high-fidelity tool.

As we’ve noted in an article on Operational Excellence with AI-Enabled Managed Services, the single biggest differentiator in 2026 is how well a provider can integrate AI into the flow of work. This requires a culture of continuous learning and a focus on experience-led IT strategy rather than just technical certifications.

Conclusion: Designing the Future Experience

The role of AI and automation in the MxP model is not just to work faster, but to work smarter. It is about moving the focus from the “server in the rack” to the “user at the desk.”

By partnering with a Managed Experience Provider (MxP), organizations gain access to a platform-driven approach where predictive IT operations and AI-driven managed services create a resilient, self-optimizing environment. As the digital landscape continues to grow in complexity, this level of automation is no longer a luxury; it is the only way to ensure that your technology accelerates your business rather than holding it back. The future of IT is here, and it is automated, intelligent, and above all, experience-focused.

Watch the video below to see how Synoptek is redefining transformation by delivering double the digital velocity at half the TCO. Learn how we shift the focus from traditional SLAs to Experience Level Agreements (XLAs) that prioritize your actual business objectives and user productivity.

Watch: The MxP Difference | How Synoptek Delivers Half the TCO, Double the Digital Velocity