Cloud - Case Study - iLink Digital

12th June 2025

by iLink Digital

Marketing Cloud Account Engagement – Marketing That Converts: What AI + MCAE Can Do

Introduction: The New Era of B2B Marketing

Let’s be honest: your marketing team isn’t doing anything wrong. Campaigns are going out, leads are coming in, and the automation flows are ticking along as planned. But here’s the problem—conversion rates are stagnant, sales are still chasing cold leads, and personalization feels more like guesswork than strategy.

In today’s hypercompetitive B2B ecosystem, that’s not just a missed opportunity—it’s a growth risk. Did you know that over 60% of marketing-qualified leads never convert into sales? Not because the message is wrong—but because timing, personalization, and buyer intent are misaligned.

Buyers are more informed, more sceptical, and less loyal than ever before. They’re expecting precision, relevance, and speed at every touchpoint. And while tools like Marketing Cloud Account Engagement (MCAE) have long supported automated workflows, they weren’t built for the real-time, intent-driven demands of today’s buyers.

That’s where AI comes in—not as a buzzword, but as a strategic multiplier.

By combining MCAE with AI, marketers can stop relying on static rules and start responding with intelligence—adapting journeys based on behaviour, scoring leads based on real buying signals, and delivering the kind of personalization that drives true conversion.

In this blog, we’ll explore how AI supercharges MCAE to create smarter, faster, and more effective marketing—from predictive lead scoring to dynamic content delivery—and why the future of B2B success belongs to those who act now.

At iLink Digital, we help B2B teams make that leap—activating AI within MCAE to align marketing and sales, accelerate conversions, and drive smarter growth.

What Is Marketing Cloud Account Engagement (MCAE)?

MCAE: The Modern Marketer’s Growth Engine

MCAE is Salesforce’s B2B marketing automation platform designed to empower revenue teams with a unified view of customer data and seamless collaboration across marketing, sales, and service. With native integrations to Salesforce CRM and Data Cloud, MCAE enables marketers to orchestrate personalized outreach, automate lead nurturing, and prioritize sales efforts—all on a single platform.

Key capabilities include:

Lead management and nurturing

Dynamic segmentation and audience targeting

Automated campaign workflows

Real-time analytics and reporting

Deep CRM and Data Cloud integration

AI + MCAE = Smarter, Faster, More Profitable Campaigns

In a B2B world defined by shrinking attention spans and rising buyer expectations, “automation” alone is no longer a competitive edge. What modern marketing teams need is intelligence that learns, adapts, and delivers results before your competitor even notices the opportunity.

That’s exactly what the convergence of AI and Marketing Cloud Account Engagement (MCAE) offers—a transformation from rule-based marketing to real-time engagement strategy.

Let’s explore how this synergy unlocks enterprise-scale growth across the funnel.

Predictive Lead Scoring That Prioritizes What (and Who) Matters

Manual lead scoring is the modern equivalent of cold calling—imprecise, outdated, and often misaligned with actual buying intent. AI-driven lead scoring in

With Einstein Behavior Scoring, MCAE analyzes historical engagement, intent signals, and behavioral data to surface the prospects most likely to convert. The model learns over time, surfacing high-intent leads before they hit your defined thresholds, allowing sales team focus on high-value opportunities and marketing to nurture leads with precision. It aligns marketing with sales in real-time and shortens the qualification cycle.

According to Salesforce, customers using Einstein for predictive scoring saw a 29% increase in pipeline accuracy and reduced lead response time by 38%.

Dynamic Journey Orchestration That Adjusts as Buyers Behave

Today’s buyers don’t move through a funnel—they zigzag. AI-enhanced MCAE turns static nurture tracks into adaptive customer journeys that respond instantly to behaviors like:

Clicking a high-value asset

Visiting a pricing page

Going dark after initial interest

Using Einstein Engagement Frequency and Recommendations, MCAE tailors content cadence and selection per user—ensuring you’re delivering the right message at the right moment with just enough frequency to avoid fatigue. It increases campaign precision without increasing operational complexity.

Firms using AI-personalized journeys report 3x higher engagement rates and 40% reduction in content wastage (Forrester, 2024).

Smart Segmentation That Evolves Beyond Demographics

Modern marketing leaders are moving past static demographic segmentation to embrace AI-powered, behavior-driven audience models. Today’s AI tools enable marketers to build dynamic cohorts that update in real time, reflecting shifts in customer intent, engagement, and firmographics—not just job titles or industries.

With AI-driven segmentation, organizations can identify and target high-value groups such as:

Accounts trending toward purchase, detected through predictive analytics and engagement scoring.

Decision-makers exhibiting high cross-channel engagement, signalling increased buying intent.

Dormant leads showing renewed interest, flagged by recent firmographic changes or digital activity

Self-Optimizing Campaigns That Don’t Wait for Post-Mortems

With Einstein Campaign Insights, marketers no longer have to wait until a campaign ends to optimize. AI highlights friction points, top performers, and underutilized assets as the campaign runs—allowing real-time refinements that drive exponential lift. This is especially crucial in high-stakes, long-cycle B2B sales—where a week’s delay in optimization could mean a lost deal.

Marketers using AI-powered insights iterate campaigns 5x faster, leading to compounding performance gains across quarters (Salesforce Pulse, 2024).

How to Implement AI + MCAE: A Strategic Roadmap

Most organizations don’t fail to adopt AI because of lack of tools—they fail because of lack of clarity on how to start. Here’s a phased, high-impact approach to implementing AI with MCAE:

Conclusion: MCAE + AI Is Not the Future—It’s the New Standard

B2B buyers today are more informed, more selective, and less patient than ever. They expect messages that speak to their needs, content that feels relevant, and experiences that adapt to their pace—not just automated workflows that follow a preset script.

The opportunity isn’t in starting from scratch. It’s in amplifying the tools you already have. With AI integrated into Marketing Cloud Account Engagement, your team can move beyond routine automation and start making smarter, faster, and more impactful decisions across the entire buyer journey.

If your team is still relying on static scoring and one-size-fits-all journeys, it’s time to elevate.

iLink Digital helps you move beyond static marketing automation by embedding AI into your MCAE ecosystem—so your team can respond faster, convert smarter, and scale what works. Ready to transform automation into real intelligence? Let’s talk.

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11th June 2025

by Thangaraj Petchiappan

The Road to AI Readiness: Transform Your Infrastructure to Lead in the AI Era

AI is no longer a futuristic concept—it’s a business mandate. According to IDC, global spending on AI-centric systems is projected to exceed $300 billion by 2026, and a recent survey highlighted that nearly half (44%) of IT leaders prioritize creating AI-ready data infrastructure. However, most organizations still struggle with the foundational requirement: a modern, AI-ready infrastructure.

At iLink Digital, we help enterprises re-engineer their digital core to support secure, scalable, and high-performance AI deployments—bridging strategy with infrastructure for real-world results. Continue reading to learn more!

Why Organizations Need AI-Ready Infrastructure?

AI-ready infrastructure isn’t just about capacity—it’s about compatibility, agility, and trust. Here’s why it matters:

It ensures scalability and security across cloud, edge, and on-premise environments.
It provides interoperability to connect legacy systems with modern platforms.
It optimizes cost-performance for AI/ML workloads with flexible deployment options.
It supports real-time processing through edge AI while maintaining data compliance.
It enables the use of AI-optimized hardware like GPUs and TPUs for speed and scale.
It enforces integrated governance—a must in today’s regulatory landscape.

What Makes an Infrastructure Truly AI-Ready?

Modern AI infrastructure is a full-stack transformation. It must support not just models, but the data, workflows, and security that surround them.

Core Components:

Compute: GPUs, TPUs, and FPGAs to accelerate model training and inferencing.
Storage: Cloud-native, scalable, and high-throughput for structured and unstructured data.
Networking: High-speed, low-latency infrastructure for real-time data flow.
AI Platforms: Tools for MLOps, model monitoring, and governance.
Orchestration & Automation: Kubernetes, Terraform, and CI/CD pipelines for agility and speed.
Data Integration & Observability: Real-time monitoring, logging, and traceability across AI systems.

These components must be tightly integrated to ensure your AI workloads run efficiently, securely, and cost-effectively.

Foundational Strategies to Unlock AI’s Potential

Strategic Focus	Description
Bring AI to the Data	Edge computing reduces latency and ensures sensitive data stays secure at the source, supporting real-time inference.
Optimize Workload Placement	Hybrid and multicloud strategies enable organizations to run workloads in environments best suited for performance and ROI.
Unify Development Pipelines	Integration of AI and application development accelerates deployment cycles and strengthens collaboration across teams.
Ensure Transparent Governance	Infrastructure must support explainability, traceability, and regulatory compliance across the AI lifecycle.
Adopt Open Standards	Leveraging open-source tools and interoperable platforms fosters flexibility, prevents vendor lock-in, and accelerates innovation.
Scale MLOps Practices	Automating the deployment, monitoring, and retraining of models ensures consistent performance and minimizes drift.
Design for Responsible AI	Embedding fairness, bias mitigation, and model accountability into infrastructure builds trust and futureproofs operations.

Gartner estimates that through 2025, 70% of organizations will lack the infrastructure and governance necessary to support AI at scale.

The Business Benefits of an AI-Ready Infrastructure

AI is not just another workload—it’s a new computing paradigm. Organizations that invest in an AI-optimized infrastructure position themselves to move faster, scale smarter, and innovate more responsibly. Here are four foundational benefits:

Did you know? IT organizations are also custom-training existing models (37%), using cloud services for AI (32%), building their own learning models (32%), allowing employees to use commercially available AI models (29%), and training employees (33%).

Enhanced Scalability

AI workloads vary dramatically in size and intensity—ranging from lightweight inferencing at the edge to compute-heavy model training in the cloud. An AI-ready infrastructure supports this diversity by:

Dynamically scaling compute, storage, and networking resources based on real-time demand.

Supporting hybrid architectures that enable seamless resource movement between on-prem, cloud, and edge.

Offering elasticity that ensures workloads perform optimally without overprovisioning or downtime.

Improved Security and Governance

As AI systems consume and generate sensitive data, security and compliance become critical. An AI-ready infrastructure enhances protection and trust by:

Enforcing consistent governance across all environments—cloud, edge, and on-prem.

Enabling fine-grained access control, encryption at rest and in transit, and secure model deployment pipelines.

Supporting audit trails, policy-based automation, and tools for monitoring fairness, bias, and drift in AI models.

Cost Optimization

AI workloads can be resource-intensive—and without strategic planning, costs can quickly spiral. An optimized infrastructure enables:

Smart workload placement across environments to match performance requirements with cost-efficiency.

Tiered storage strategies and automated provisioning to manage high-volume datasets without excessive spend.

Continuous monitoring to identify idle resources, optimize utilization, and eliminate redundancy.

Accelerated Innovation

The right infrastructure doesn’t just support AI—it accelerates it. It creates an environment where data scientists, engineers, and developers can experiment, deploy, and iterate at speed. Benefits include:

Fast access to high-performance compute resources for rapid model training and tuning.

Integration with open-source AI frameworks and MLOps tools to streamline development.

Support for containerized, modular deployments that reduce cycle time and improve reproducibility.

Top 5 Challenges That Stall AI Growth

Even visionary enterprises can struggle to scale AI due to fundamental infrastructure issues. Here are the top barriers we see:

Hybrid Complexity
Managing disparate systems across cloud, edge, and on-prem can cause fragmentation and inefficiency.
Security Risks
AI systems need tighter protection—from data poisoning to adversarial attacks.
Uncertain ROI
AI infra investments can be steep. Without workload mapping and TCO clarity, ROI remains vague.
Lack of Lifecycle Management
ML models need ongoing training, testing, and versioning—often absent in traditional infra setups.
Skill Gaps & Adoption Barriers
Without internal AI champions and training programs, enterprise-wide adoption stagnates.

iLink Digital’s Approach to Infrastructure Transformation

Every enterprise is unique. That’s why our infrastructure modernization journey is tailored, outcome-driven, and scalable.

Whether you’re building from the ground up or modernizing incrementally, our AI-first approach helps future-proof your infrastructure—while aligning to compliance, performance, and cost goals.

Discovery & Gap Analysis

We benchmark your infrastructure, map your goals, and identify AI enablement gaps.

Hybrid & Multicloud Architecture Design

Leverage Microsoft Azure, VMware, and open standards to support AI workloads across environments.

AI-Optimized Infra Planning

We recommend compute, storage, and networking best suited for your use cases—be it generative AI, predictive analytics, or edge ML.

Application Modernization Services

We transform legacy applications into modern, cloud-native solutions using microservices and containerization.

AI Center of Excellence Setup

We help establish governance, develop internal talent, and standardize MLOps through internal CoEs.

Conclusion

AI is here, and it’s moving fast. Your infrastructure shouldn’t be what slows you down. At iLink Digital, we don’t just build systems—we build AI-ready foundations that let you scale smarter, move faster, and compete stronger. Let’s rethink your infrastructure for the AI era—together. Reach out to us today to begin your transformation.

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Thangaraj Petchiappan

Chief Technology & Innovation Officer at iLink Digital

Author

Thangaraj Petchiappan leads the company’s digital transformation initiatives for Fortune 500 clients. He focuses on enhancing infrastructure automation and integrating advanced bot solutions across various industries, including healthcare, oil & gas, manufacturing, telecom, retail, and NPO sectors. As the founder of the AI-Powered Cybersecurity iLab in Texas, he spearheads the development of innovative AI and ML solutions. Additionally, Thangaraj shares his expertise as a keynote speaker, cloud advocate, and coach, offering guidance on digital transformation and technology leadership.

White and Blue Modern Product Differentiation Presentation (2)

5th June 2025

by iLink Digital

From Bloated to Brilliant: Tackling Tech Debt in Your Salesforce Stack

Every Salesforce environment begins with small, well-intentioned adjustments — a validation rule here, an integration there, perhaps a custom object to support a growing sales process. But over time, these incremental changes accumulate, turning a once-efficient system into a complex, sluggish environment.

It’s a common challenge. In fact, studies show that over 70% of organizations struggle with CRM technical debt within two years of implementation. Frustrated users, incomplete dashboards, and prolonged update cycles become familiar obstacles — the quiet consequences of unchecked system growth.

At iLink Digital, we’ve seen this scenario unfold often. The good news is, a costly overhaul isn’t the answer. With a targeted, data-driven strategy, it’s possible to streamline operations, enhance performance, and future-proof your Salesforce environment without disrupting business continuity.

Continue reading to discover how you can bring clarity, speed, and resilience back to your CRM.

What Is Tech Debt in Salesforce?

In a Salesforce context, technical debt refers to outdated, redundant, or overly complex elements that have been built over time, often as quick fixes that have outlasted their usefulness.

Common Symptoms of Tech Debt:

Over-customized processes that confuse more than they help
Legacy or brittle integrations that break with every Salesforce update
Siloed data across business units and tools
A clunky interface that reduces adoption and productivity

Why it happens: As your business scales needs evolve. Teams build quick solutions without long-term thinking, leading to duplicated functionality and inefficiencies across your CRM.

The Hidden Cost of Bloat

Most Salesforce instances don’t break overnight—they slow down. And that slow creep is where the cost hides.

Over the years, as teams make quick fixes, layer on integrations, or customize without strategy, Salesforce becomes a maze. Instead of a single source of truth, it turns into a tangle of duplicate fields, confusing logic, and bloated code. The price? Your people—and your progress—pay for it.

🚫 Here’s what bloat does to your business:

Kills Productivity

Sales and support reps waste time navigating non-intuitive screens, hunting for the right records, or re-entering the same data in multiple places. Actions that should take seconds stretch into minutes—multiplied across every team member, every day.

Breaks Reporting and Analytics

When data is inconsistent or duplicated across objects, your dashboards become unreliable. Executives can’t trust KPIs. Marketing can’t measure campaign ROI. Decision-making slows, or worse—goes off course.

Drains IT Budgets

Bloated orgs require more maintenance, more testing, and more patchwork solutions. That means your IT team spends most of its time fixing or monitoring legacy systems rather than enabling innovation.

Stalls Innovation

Want to launch a new product, campaign, or feature? In a bloated system, every change requires navigating dependencies, unpicking outdated automation, or waiting weeks for regression testing. The risk of “breaking something else” makes teams hesitant to move fast.

Every outdated field, broken integration, and bloated workflow adds invisible drag to your Salesforce stack. Here’s how iLink helps you move from complexity to clarity.

The Path to “Brilliant”: A Modern, Scalable Salesforce

Eliminating technical debt doesn’t necessitate a complete system overhaul. At iLink Digital, we specialize in guiding organizations through a strategic, phased transformation of their Salesforce environments. Our approach focuses on delivering clarity, speed, and long-term scalability, ensuring minimal disruption to your operations.

Data and Integration Excellence

At iLink Digital, we recognize that data is the cornerstone of any successful Salesforce transformation. Our priority is to establish a clean, connected, and real-time data environment, as even the most well-designed CRM systems falter when built on poor data foundations.

Our approach includes:

Comprehensive Data Audits: Utilizing tools like Salesforce Optimizer and our proprietary accelerators, we identify duplicates, unused fields, and conflicting objects to streamline your data architecture.

Integration Optimization: We replace fragile point-to-point connections with robust, middleware-powered hubs such as MuleSoft or Azure Integration Services, ensuring scalability and transparency.

Standardization: We align data models, naming conventions, and synchronization strategies across systems like ERP, EHR, marketing automation, and customer service tools to provide a unified customer view.

High-Impact, Low-Complexity Optimizations

Not every issue requires a massive fix. iLink Digital identifies quick wins with significant returns, modernizing user experiences, cleaning up customizations, and reducing administrative overhead.

Our strategies involve:

User Interface Redesign: Leveraging low-code/no-code tools like Flow Builder, Lightning App Builder, and Dynamic Forms, we create intuitive interfaces that enhance user productivity.

Legacy System Decommissioning: We retire obsolete Apex code, Visualforce pages, and redundant third-party applications, replacing them with native Salesforce features.

Maximizing Native Capabilities: By intelligently enabling built-in functionalities such as Einstein Activity Capture, Path, and Omni-Channel routing, we reduce reliance on external tools.

Agile, Iterative Delivery for Continuous Value

We advocate for a sprint-based, outcome-driven delivery model that demonstrates value early and often, fostering confidence and adoption throughout the organization.

Our process includes:

Prioritized Backlog Creation: We develop a backlog of improvements based on user impact, implementation effort, and alignment with business objectives.

Pilot Rollouts: Implementing changes in controlled environments allows us to test, validate, and refine solutions before broader deployment.

Impact Measurement: We track key metrics such as time savings, data accuracy improvements, and user satisfaction to quantify ROI at each stage.

Continuous wins don’t just clean your org—they boost stakeholder confidence, foster user engagement, and make transformation a shared success.

No Bloat: Design Principles to Prevent the Next Wave of Tech Debt

Getting your Salesforce house in order is one thing. Keeping it clean and future-ready is another. Here’s how you can embed anti-bloat thinking into your Salesforce strategy.

Principle	What It Means	Why It Matters
Build for Scale	Use modular design and flexible architecture	Grows with your business without rebuilds
Governance First	Define data standards, change processes, and roles	Prevents rogue customizations and chaos
Cross-Team Alignment	Regular syncs between business, IT, and users	Ensures solutions meet real needs
Documentation & Audits	Maintain org-wide documentation and health checks	Keeps teams aligned and systems clean
Invest in Learning	Train users on new features and updates	Promotes adoption and usage of native capabilities

Key Takeaways: The Brilliant Salesforce Checklist

A high-performing Salesforce org doesn’t just look good—it works better. Here’s your cheat sheet for making that a reality:

Conclusion

Unchecked technical debt doesn’t just slow you down—it compounds.
Every redundant object, outdated integration, or misaligned automation adds friction to your Salesforce stack, making it harder to scale, optimize, or innovate.

At iLink Digital, we specialize in reversing this trend. Through structured org assessments, integration remediation, and scalable architecture realignment, we help enterprises regain technical control—without disrupting mission-critical operations.

If your CRM is starting to feel more like a liability than a lever for growth, now’s the time to act.

Let’s stabilize, streamline, and future-proof your Salesforce—before the next update breaks what’s already fragile. Ready to uncover the hidden value in your Salesforce stack? Let’s talk.

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5th June 2025

by Thangaraj Petchiappan

Microsoft 365 Copilot for Cybersecurity: Top Use Cases and How It’s Transforming Security Operations

In today’s hyperconnected world, cybersecurity isn’t just a priority — it’s a business imperative. As threats grow smarter and more relentless, organizations need intelligent, agile defense strategies that go beyond traditional tools.

That’s where Microsoft 365 Copilot steps in. This AI-powered security assistant integrates seamlessly with tools like Microsoft Defender, Sentinel, and Intune, helping security teams detect, investigate, and respond to threats faster and smarter than ever.

And with iLink Digital, a trusted Microsoft technology partner, businesses gain the expertise and guidance to adopt and maximize this powerful capability.

Curious how it works? Keep reading — we’ll break it down

Understanding Microsoft 365 Copilot in Cybersecurity

Microsoft 365 Copilot leverages large language models and organizational data to deliver actionable security insights using natural language. Unlike standalone security tools, Copilot is tightly integrated with Microsoft’s security ecosystem, including Microsoft Defender XDR, Microsoft Sentinel, and Microsoft Intune.

Microsoft 365 Security Tool	Copilot Capabilities	Outcomes
Microsoft Defender XDR	Incident triage, threat correlation, and remediation guidance	Reduced investigation time, more effective threat mitigation
Microsoft Sentinel	KQL query generation, log analysis, anomaly detection	Accelerated threat hunting and response
Microsoft Intune	Endpoint configuration review, compliance posture insights	Better device management and policy enforcement

Real-World Cybersecurity Use Cases with Microsoft 365 Copilot

Incident Investigation and Response

Microsoft Security Copilot significantly accelerates incident investigation. Instead of spending hours filtering logs or writing KQL queries, security analysts can now summarize alerts, correlate signals, and receive remediation guidance through a natural language interface.

For example, in this official case study, a Tier 1 analyst used Copilot to investigate and respond to an active attack, showing how the AI assistant guided every step—from surfacing the threat to documenting the response.

Additionally, a Forrester study commissioned by Microsoft reported a 26% reduction in initial response time for organizations using Security Copilot.

Before Copilot	With Copilot
Manual log filtering and query writing	AI-generated summaries and recommendations
Extended investigation durations	Up to 30% faster incident resolution
Higher risk of oversight	Enhanced threat visibility

Threat Detection and Analysis

Copilot enhances threat detection by proactively identifying anomalies, decoding obfuscated scripts, and correlating events using Microsoft Sentinel and Defender.

In Microsoft’s own documentation, Copilot analyzes suspicious scripts or command lines in Defender XDR, identifies MITRE ATT&CK tactics, and assists analysts in classifying malicious behavior.

Further insights from Datalink Networks show how Copilot reverse-engineers encoded scripts and offers remediation suggestions, enabling faster threat containment and improved accuracy in triage.

Security Posture Management

Microsoft 365 Copilot also supports proactive security posture management by surfacing policy gaps and offering targeted guidance via Intune and Microsoft Secure Score.

According to Microsoft’s Intune documentation, Copilot helps administrators identify non-compliant devices, analyze policy misalignments, and automate configuration updates.

This allows CISOs to quickly answer key questions like:

Which devices are non-compliant?
Are our MFA policies enforced organization-wide?
Where do the highest-risk access points exist?

Security Gap	Copilot Recommendation	Outcomes
MFA not enforced on 30% of users	Enable Conditional Access via Intune	98% policy compliance in 3 days
Outdated OS patches	Surface & prioritize updates	Automated rollout initiated
Legacy protocol in use	Recommend policy deprecation	Reduced Secure Score risk rating

Policy Configuration and Compliance

Copilot streamlines compliance by enabling AI-assisted policy configuration and automated audit reporting—especially critical for highly regulated sectors.

In the Contoso compliance case study, Microsoft outlines how Copilot helped define, implement, and monitor communication compliance policies. This saved time, reduced manual overhead, and improved audit readiness.

Additionally, Copilot assists in aligning with frameworks like ISO 27001 and NIST, helping teams generate auditor-ready summaries and stay ahead of emerging regulatory changes.

iLink Digital’s Role in Empowering Cybersecurity with Copilot

Implementing Microsoft 365 Copilot is not just about turning on a feature—it’s about transforming how security teams work.

iLink Digital specializes in helping organizations realize the full potential of Copilot through a structured, hands-on approach:

We offer tailored workshops like “Full-Day Workshop: Unlock Productivity with Microsoft 365 Copilot,” which immerse stakeholders in real-world scenarios across security, compliance, and risk.

We leverage Microsoft best practices and the Prosci change management methodology to ensure seamless onboarding, internal alignment, and high adoption rates.

Our consulting services cover everything from data readiness assessments and Copilot prompt engineering to customized policy configuration and access governance.

This model ensures not only successful deployment but also ongoing optimization of Copilot as your threat landscape evolves.

Best Practices for Implementing Copilot in Cybersecurity

Successful Copilot integration hinges on more than technical setup. It requires strategic foresight and continuous alignment between people, processes, and platforms. Here are best practices we recommend:

Engineer smart prompts

The quality of Copilot’s output depends on how you engage it. We help teams craft use-case-specific prompts for threat hunting, compliance, and incident response.

Train iteratively

Security teams should undergo phased training—starting with query optimization, then advancing to scenario-based Copilot use.

Standardize Copilot governance

Establish policies around what queries are allowed, who can access which capabilities, and how outputs are validated.

Align with Microsoft telemetry

The more data Copilot has access to via Defender, Sentinel, and Intune, the more effective it becomes.

Enterprise-Grade Trust and Compliance

Copilot upholds Microsoft’s trusted security model, ensuring your sensitive data is protected at every step:

Trust Feature	Description
Data Sovereignty	All data remains within your Microsoft 365 tenant.
No Data Leakage	Microsoft does not use your company’s data to train its AI models.
Encryption	All data is encrypted at rest and in transit.
Permissions-Aware	Copilot respects all existing access controls—you only see what you’re allowed to.
Compliance Aligned	Supports frameworks like GDPR, HIPAA, ISO 27001, and FedRAMP.

This makes Copilot not only powerful—but enterprise-ready.

Conclusion: Future-Proof Your Security Operations

Security teams today are expected to do more with less. Microsoft 365 Copilot isn’t just an assistant—it’s an amplifier. It brings precision, speed, and intelligence into every layer of the security operation.

With iLink Digital as your implementation partner, you’re not just deploying another tool. You’re redefining how your organization manages cyber risk—proactively, intelligently, and at scale.

Ready to rethink security with AI? Reach out to us today!

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Thangaraj Petchiappan

Chief Technology & Innovation Officer at iLink Digital

Author

3rd June 2025

by iLink Digital

Microsoft Fabric’s Direct Lake – Findings About Power BI’s New Storage Mode

Fast, interactive data access is essential for Business Intelligence (BI) solutions. Two key factors impact end-user performance: data availability within the BI system and the rendering speed of the reporting frontend. Microsoft Fabric introduces Direct Lake, an innovative storage mode designed to address these challenges.

Direct Lake draws data from OneLake, Fabric’s underlying logical data lake. Data is loaded into memory directly from Delta Parquet files, eliminating the need to duplicate data into Power BI’s proprietary cache or query a warehouse SQL endpoint.

The diagram below illustrates the distinctions among various storage modes available in Power BI.

Direct Lake Features

In Direct Lake mode, because data is sourced and loaded directly from files in the data lake, the need to refresh the entire dataset cache is eliminated, enabling near-real-time analysis like DirectQuery (DQ) mode, but without the latency or data duplication associated with Import. Traditional Import refresh often encounters errors and requires substantial time to complete, resulting in notable operational gains through avoidance of such issues. Alternatively, the memory cache is dynamically updated based on column usage, following a “preloading” process that directs it to the latest data lake files.

Key features of Direct Lake mode include:

Available atop both lakehouse and warehouse workloads.
SQL Endpoint and XMLA Endpoint enable querying and editing, facilitating read-write capabilities from external tools like SQL Server Management Studio or Tabular Editor.
Automatic fallback to DirectQuery occurs when SKU limits are exceeded or unsupported features are utilized (configurable settings apply).
Changes to data are instantly reflected.
Elimination of translations from DAX or MDX to other query languages or pushdown query executions on disparate database systems.
Compatibility requires Database Compatibility Object level 160 or later.

Storage modes differences (Direct Lake Vs Import Vs DirectQuery)

One frequently asked question regarding Direct Lake is whether it serves as a replacement for either Import or DirectQuery?

The short answer is no; actually, Direct Lake is offered in Fabric as a complementary option to Import and DirectQuery. Understanding the distinct strengths of each storage mode helps illustrate how they complement one another. Notably, Direct Lake isn’t simply a hybrid of the other two modes; instead, it presents a singular, innovative way to access data from the data lake.

In terms of speed, Direct Lake surpasses DirectQuery and trails behind Import mode, though the difference between Direct Lake and Import may be minimal, contingent upon the specifics of the semantic model.

Comparatively speaking, Direct Lake exceeds DirectQuery in terms of velocity, though potentially falling behind Import mode, contingent upon the semantic model employed, wherein discrepancies between Direct Lake and Import may prove inconsequential.

Regarding similarities with Import mode, certain considerations must be considered, including analogous memory utilization patterns observed in both storage paradigms. Conversely, Direct Lake imposes restrictions on leveraging computed columns and tables, coupled with constraints affecting MDX-driven hierarchical representations in Excel PivotTables, ultimately influencing user interaction.

Direct Lake sources its data from the Fabric Warehouse, triggering a fallback to DirectQuery when necessary.

Direct Lake’s internals and how they affect performance

Direct Lake loads data from Delta Parquet files, leveraging its advantages to achieve excellent compression, requiring strong data engineering skills and knowledge of Fabric’s internal mechanisms.

Let’s begin with Parquet, an open-format file based on columnar storage, which stores data for each column together. This design enables better compression and smaller file sizes compared to row-based formats like CSV. The Delta Lake storage framework, which powers Microsoft Fabric (as well as Databricks, the originator of the standard), is built on Parquet files according to the Delta Lake open-source specification.

Delta Lake provides seamless, unified data access in Fabric. To optimize performance, default settings such as compaction and small file size management are enabled.

Building on these open-source standards, Microsoft developed the proprietary V-Order write-time optimization to make Parquet files more quickly scannable while maintaining open-source compatibility. This approach borrows patterns from SSAS and Power BI’s VertiPaq engine, which is why these files are sometimes referred to as “VertiParquet” files.

V-Order applies specialized sorting, row group distribution, dictionary encoding, and compression techniques. Together, these optimizations reduce network, disk, and CPU resource usage when reading the files. This improves performance and lowers both storage costs (due to compression) and compute costs (from reduced CPU usage). However, this advantage comes with a tradeoff: V-Order writes are typically about 15% slower than standard writes.

For this reason, V-Order should be used for producing the gold layer intended for Power BI consumption but not for writing bronze or silver layers. Additionally, Fabric’s mirroring feature includes a landing zone that automatically converts ingested data to VertiParquet.

Read performance from columnar storage depends on retrieving only the necessary information for each query. Another optimization strategy involves leveraging the frequency and recency of data usage. Instead of preloading all Delta files into memory, Direct Lake assigns a “temperature” to columns to decide whether they should be paged in or out of memory. This type of on-demand loading was already a feature of large semantic models in Power BI Premium.

Columns that have been recently queried by end users are kept in memory for faster access during subsequent queries. Conversely, columns with low temperatures—those not accessed for a while—will need to be loaded into memory when queried. When a Direct Lake semantic model is refreshed, pointers to the latest Delta files are updated, and the memory cache is cleared.

Fabric provides several programmatic options for selectively priming the in-memory cache in advance of user interactions, ensuring optimal performance.

Direct Lake Guardrails and Max memory

Fabric licensing impacts Direct Lake availability based on constraints on the underlying Parquet file, as well as memory and row volume maximums for the in-memory cache, per the table below:

Microsoft DREAM Demos available for Partners by Technical Stack

Fabric /PowerBI SKUs	Parquet files per tables	Row Groups per Table	Row per Table (Millions)	Max Model size on disk/OneLake (GB)	Max Memory (GB)
F2	1000	1000	300	10	3
F4	1000	1000	300	10	3
F8	1000	1000	300	10	3
F16	1000	1000	300	20	5
F32	1000	1000	300	40	10
F64/FT1/P1	5000	5000	1500	Unlimited	25
F128/P2	5000	5000	3000	unlimited	50
F256/P3	5000	5000	6000	Unlimited	100
F512/P4	10000	10000	Unlimited	12000	200
F1024/P5	10000	10000	24000	Unlimited	400
F2048	10000	10000	24000	Unlimited	400

Learn More

Since Direct Lake supports Delta tables being read directly from OneLake, a fallback to DirectQuery (DQ) will occur if the semantic model is built on top of SQL views in a warehouse. This happens because views are not materialized in OneLake.

When fallback to DQ mode is triggered, queries are directed to the source warehouse or lakehouse SQL endpoint instead of relying on the in-memory cache. While this approach is typically slower, it is still faster than querying warehouses outside of Fabric. Fallback occurs if any table in the query exceeds the limits outlined above. These limits are evaluated independently, so exceeding just one will initiate the fallback process.

Developers can control whether fallback is enabled for a dataset. Disabling DQ fallback is helpful for testing and can slightly improve performance, as the engine does not calculate a fallback query plan.

The number of Parquet files per table is a straightforward metric—it refers to the number of files associated with a specific table in the OneLake storage. For example, with an F64 capacity, if a table has more than 5,000 Parquet files, fallback will occur.

As previously mentioned, Parquet is a columnar storage format, where each column is stored separately. Parquet files also include row groups, which are partitions of rows within a column, enabling compression algorithms to enhance performance. Larger row groups provide better compression opportunities. However, in Direct Lake mode, higher row group counts within a table can degrade performance and increase the likelihood of fallback to DirectQuery. In the F64 capacity example, the limit for row groups per table is also 5,000.

Another fallback limitation is the total number of rows in a table. For an F64 capacity, Direct Lake mode supports a maximum of 1.5 billion rows per table. If a query involves a table with more rows, that query will default to DirectQuery mode.

These three limitations can be monitored and managed using a Python script in a notebook. This allows you to examine your lakehouse tables and quickly determine if you need to optimize your Parquet files or adjust your capacity to ensure optimal performance in Direct Lake mode.

Maintaining lakehouse tables is key to staying within these limits. Using commands like OPTIMIZE and VACUUM can reduce the number of files and compact them while preserving V-Order optimization. These adjustments can be applied at an individual level or in bulk using tools like Apache Spark, SQL, or Python.

Lastly, Guardrails define resource limits for Direct Lake mode. If these limits are exceeded, fallback to DirectQuery mode is required to process DAX queries. For example, Max Memory defines the upper limit for how much data can be paged into memory.

Direct Lake models can include only tables and views from a single lakehouse or data warehouse.
Currently, Direct Lake tables cannot be mixed with other table types, such as Import, DirectQuery, or Dual, within the same model. In other words, composite models are not yet supported.
DateTime relationships are not supported in Direct Lake models.
Calculated columns and calculated tables are also not supported at this time. Instead, you must generate these in the gold layer that feeds into your semantic model.
Some data types may not be supported.
Direct Lake tables do not support complex Delta Table column types. Binary and GUID semantic types are also unsupported. You must convert these data types into strings or other compatible formats.
For table relationships, the key columns must have matching data types, and primary key columns must contain unique values. DAX queries will fail if duplicate primary key values are detected. This issue should be addressed during the data engineering process that creates your gold layer.
String column values are limited to a maximum length of 32,764 Unicode characters.
Embedded scenarios that rely on embedded entities are not yet supported.
Tables based on T-SQL views cannot be queried in Direct Lake mode. DAX queries using these model tables will revert to DirectQuery mode.
Validation is limited in Direct Lake models. User selections are assumed to be correct, and there is no validation of cardinality or cross-filter selections for relationships or selected date columns in a date table.
The “Direct Lake” tab in the Refresh History only lists failures related to Direct Lake refreshes. Successful refreshes are not currently displayed.
Creating a Direct Lake semantic model in a workspace located in a different region from the data source workspace is not supported. As a workaround, create a lakehouse in the workspace of the other region and use shortcuts to the tables before building the semantic model.
If a Direct Lake semantic model table connects to a table in the SQL Analytics endpoint that enforces row-level security (RLS), any queries involving that model table will default to DirectQuery mode.
The default Direct Lake semantic model does not support Service Principals. To enable this, Service Principal authentication must be configured for Fabric REST APIs in the tenant settings, and the Service Principal must have at least Contributor permissions for the Direct Lake semantic model workspace.
Direct Lake does not support Service Principal profiles for authentication.
The automatic date/time intelligence feature in Power BI Desktop is not supported. However, you can designate your own date table as a date table.
Publishing to the web from Power BI is supported only when using a fixed identity for the Direct Lake semantic model.
Your Fabric SKU determines the maximum available memory for each Direct Lake semantic model. If this limit is exceeded, queries to the semantic model may experience slower performance due to excessive paging in and out of model data.

Direct Lake – Issues and Limitations

Given that this feature is new and still in development, certain limitations and issues may arise. According to the Microsoft website, the following limitations have been identified:

Since Fabric is evolving rapidly, this list is likely to change over time, and several alternatives exist to address some of these limitations. For more complex requirements, composite models remain valuable and can still leverage Fabric’s lakehouse and warehouse as high-performance data sources.

Conclusion

While Direct Lake may have certain advantages over DirectQuery mode, particularly in terms of speed, Direct Lake offers several benefits including increased flexibility and efficiency compared to Import mode. Specifically, Direct Lake differs from DirectQuery mode, Here are some potential drawbacks, including lower latency and limited scalability Direct Lake. Despite these limitations, Direct Lake continues to evolve and adaptability support, developing new applications, and technologies, and responding to emerging trends and innovations in the field of data science.

If you’d like to know if this new storage mode suits your needs or you’re having trouble setting up a Direct Lake semantic model, feel free to contact us to discuss potential approaches tailored to your data volume requirements, refresh rates, and source configurations.

References:

Learn about Direct Lake in Power BI and Microsoft Fabric – Microsoft Fabric | Microsoft Learn

Use composite models in Power BI Desktop – Power BI | Microsoft Learn

How to configure Parquet format in the data pipeline of Data Factory in Microsoft Fabric – Microsoft Fabric | Microsoft Learn

https://learn.microsoft.com/en-us/power-bi/enterprise/directlake-overview#fallback

https://www.sqlbi.com/blog/marco/2024/04/06/direct-lake-vs-import-mode-in-power-bi/

28th May 2025

by iLink Digital

The Future Is Industry-First: How Salesforce Is Reshaping CRM Strategy

Introduction

Is your organization spending millions on a CRM to streamline sales and boost customer relationships? But months later, you’re stuck in endless customization loops, frustrated by a platform that just doesn’t quite fit your unique business needs. Sound familiar? You’re not alone.

This is the reality for many enterprises relying on generic CRM platforms. Executives report long customization cycles, prolonged implementation cycles, data silos, compliance gaps, and a lack of alignment with industry-specific workflows have led to poor ROI and put a toll on overall digital transformation efforts.

It’s no surprise that organizations are rethinking their CRM strategy. We’ve entered the era of the industry-first CRM, driven by regulatory pressure, rising customer expectations, and increasing operational complexity. Salesforce has been at the forefront of this transformation, building vertical-specific solutions and purpose-built solutions for over a decade to address the unique complexities across industries, setting a new gold standard in CRM evolution.

The Shift from Generic to Industry-First CRM

Why One-Size-Fits-All CRM Fails?

Traditional CRMs are often bloated with generic features and require heavy reconfiguration to fit into industry standards. These platforms lack prebuilt models tailored for specific verticals and often require extensive customization—raising costs, complexity, and maintenance demands. This generic CRM often leaves sales, service, and marketing teams frustrated while using such intricate dashboards, hindering their efficiency (especially in a regulated environment). Complex dashboards, multiple tool switching, and manual compliance tracking hinder team productivity.

According to Forrester, nearly 60% of enterprises using traditional CRM platforms spend more time customizing them than leveraging their actual capabilities. This complexity adds unnecessary overhead and prolongs deployment.

The Rise of Industry Clouds

According to Gartner, “By 2027, more than 70% of enterprises will be using industry cloud platforms to accelerate business initiatives.”

Salesforce has pioneered a new class of CRM, industry clouds that blend the power of SaaS, PaaS, and IaaS with features tailored for sectors like healthcare, manufacturing, retail, and financial services. These clouds are pre-configured with industry-specific workflows, AI, compliance logic, and KPIs. These vertical clouds offer faster time-to-value, lower implementation risk, and higher long-term ROI.

The complex regulatory demands and strict compliance requirements in the healthcare and financial industry are pushing the industries towards industry clouds for their agility and scalability, driving innovation and resilience.

According to IBM, organizations implementing Salesforce Industry Clouds are reporting higher ROI and improved business impact compared to traditional CRM deployments.

How Salesforce Is Leading the Industry-First CRM Revolution?

Salesforce has developed a comprehensive suite of 12+ industry clouds across sectors such as healthcare, financial services, retail, manufacturing, media, and education—each infused with deep vertical expertise through prebuilt vertical solutions, unified data architecture, and embedded intelligence that addresses sector-specific pain points with precision and scalability.

Key Innovations Powering Salesforce’s CRM Strategy

Deep Vertical Expertise

From healthcare to financial services to manufacturing, Salesforce delivers purpose-built solutions for real-world needs.

Prebuilt Compliance and Workflows

Templates and accelerators reduce go-live time and ensure adherence to industry standards.

Salesforce Data Cloud

Mulesoft integration with CRM unifies customer data across platforms and touchpoints for real-time analytics, enabling personalization at scale, providing a 360° view of customers or patients, ensuring seamless connectivity across platforms.

Built-in AI + Analytics

With Einstein AI, Tableau, and Flow, organizations can automate decisions, gain predictive insights, and drive more intelligent workflows, empowering executives to make better decisions without having to assemble custom data pipelines.

Industry-Specific CRM in Action

Healthcare & Life Sciences

The healthcare & life sciences industry often grapples with high-stakes challenges, such as fragmented systems, compliance complexity (HIPAA, FDA), and a critical need for patient-first care. Salesforce Health Cloud unifies clinical data, claims, HER, patient preferences, and scheduling into a single unified platform, empowering providers to deliver patient-centric care. Automated workflows and HIPAA-compliant processes reduce administrative burdens, while care coordination tools enhance collaboration across teams.

Manufacturing: Turning Data into Actionable Insights

For manufacturers, agility and efficiency are non-negotiable. Salesforce CRM gives manufacturers real-time visibility into sales pipelines, inventory, and customer preferences. Manufacturing always demands tight data integration (orders, inventory, production schedules) and AI-driven forecasting. Manufacturing Cloud provides real-time inventory tracking, predictive maintenance, and IoT integration, empowering manufacturers to respond faster to supply chain disruptions and optimize production cycles.
According to the Forrester Study, “A composite customer achieved 354% ROI over three years, including 5% revenue growth and saving 490 hours per year through AI-driven automation in outreach and reporting.”

Retail & Consumer Goods: Personalization at Scale

75% of customers expect a seamless & personalized experience across channels. Salesforce CRM enables retailers to deliver targeted campaigns, automate omnichannel journeys, and boost customer loyalty. From personalized product recommendations to real-time service support, Salesforce Commerce & Consumer Cloud helps connect every touchpoint that turns shoppers into lifelong brand advocates, boosting both conversion rates and long-term customer value along the way

Salesforce Financial Services Cloud

The BFSI sector often struggled with huge regulatory burdens, fragmented systems, and evolving customer demands. Financial Services Cloud (FSC) platform is built with compliance and client trust at its core. This unified platform empowers advisors and agents with a comprehensive, real-time client view while also automating compliance tracking and audit processes, significantly reducing operational risk. Institutions leveraging FSC have reported a 36% increase in customer satisfaction and a 27% reduction in operational costs, according to Salesforce.

Conclusion

The industry-first approach to your CRM is not just a fleeting trend; it’s a strategic imperative for organizations seeking agility, compliance, and exceptional customer experiences. By leveraging Salesforce’s industry-specific clouds and prebuilt compliance frameworks, businesses can minimize costly customizations, accelerate time-to-value, and future-proof their operations while allowing their teams to focus on strategic initiatives rather than time-consuming configurations.

As a platinum Salesforce partner with deep domain expertise across industries, we implement Salesforce clouds as they were intended: aligned to your domain, workflows, and business outcomes, not just legacy practices. From initial setup to post-launch, we ensure your Salesforce investment drives measurable value through clarity, simplicity, and strategic alignment.

If you’re ready to move beyond the status quo and lead your industry, iLink Digital brings the frameworks, talent, and experience to deliver transformation right the first time.

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