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Microsoft Fabric is redefining analytics — here’s how to make it work for you.  Artificial intelligence has dominated boardroom agendas for two years running. Yet beneath the headlines and hype, a more sobering reality is emerging: according to BCG’s 2025 research , only about 5% of organizations are deriving measurable value from AI investments.  The problem isn’t ambition — it’s activation.  Most companies still wrestle with fragmented data systems, inconsistent reporting, and disconnected analytics that stall decision-making. For mid-market teams balancing growth and efficiency, that fragmentation can feel like trying to plan a moon landing with half the controls missing.  Enter Microsoft Fabric , an end-to-end platform designed to unify data engineering, analytics and AI under one roof. But adopting Fabric isn’t just a tech upgrade. It’s a strategic shift in how mid-market organizations prepare, plan and perform.  At Interloop, we see Fabric as the bridge between today’s complexity and tomorrow’s clarity. Here’s how leaders can navigate this new frontier — and why unification is the real unlock for AI success in 2026.  1. Before You Build, Benchmark  Most mid-market organizations already have pockets of excellence: Power BI dashboards here, a cloud data warehouse there, maybe a few AI pilots sprinkled in. What’s missing is the connective tissue — a unified foundation that keeps insight flowing instead of getting trapped in silos.  Start by mapping your current landscape.  Where does your data live? How many tools handle ingestion, transformation and reporting? How often do those systems sync and how often do they argue?  Fabric thrives on clarity. By consolidating ingestion, storage and analytics into OneLake , the platform eliminates costly duplication and latency. But readiness isn’t only about systems. It’s about culture, sponsorship and alignment.  Leaders should evaluate three dimensions before lift-off:  Data:  Are inputs consistent, governed and reliable?  Analytics:  Do teams share a single version of the truth?  Activation:  Can insights translate to measurable action?  Organizations that achieve alignment across all three see productivity gains of up to 25% annually. The rest spend that time reconciling conflicting numbers in spreadsheets.  🔗  Need a high-level primer?   Microsoft Fabric Overview     2. Plan, Prove and Propel  The journey to Fabric shouldn’t begin with a wholesale migration. It should start with a controlled plan  that proves value fast.  Choose one high-impact domain: finance forecasting, operations or customer analytics. Move that workload into Fabric and measure what changes. Most organizations find reporting latency drops, collaboration improves and costs become more predictable thanks to Fabric’s consumption-based model.  Next comes foundation building.  This is where OneLake’s unification shines — every dataset, model and pipeline lives in one environment. It’s the difference between managing multiple dashboards and navigating from a single mission control.  Automation also plays a key role. Fabric can orchestrate data movement from intake to insight with minimal manual effort. As one Interloop client put it, “What used to take a day of hand-offs now takes a minute.”   🔗  From File Drop to Business Action in 60 Seconds  shows that speed in action.  Govern Lightly — Not Loosely  Governance is often where momentum dies. Too much red tape and innovation stalls; too little and chaos reigns. The goal is balance — guardrails, not roadblocks.   Establish clear ownership: who builds, who publishes, who audits. Use Fabric’s built-in lineage and permissions tools to track data movement without creating bottlenecks. Governance should enable trust, not fear.  🔗 For a deeper dive into empowering safe self-service, see How Data Agents Unlock Trusted Self-Service at Scale .  Create a Center of Excellence  Even the best technology falters without people leading adoption. A Center of Excellence (CoE)  ensures continuity, training and accountability.  According to McKinsey, transformation initiatives with executive sponsorship are 3x more likely to succeed . The CoE formalizes that sponsorship, turning early wins into repeatable processes across departments.    3. From Unified Data to Unified Decisions  Once your data foundation is steady, the real payoff begins — turning insight into action.  Fabric brings real-time analytics to the mid-market. Dashboards refresh in seconds, not hours. Teams can monitor sales trends, manufacturing performance or customer behavior and react in the moment, not after the quarter closes.  AI amplifies that impact. Predictive models can surface churn risks before they hit the bottom line or forecast demand to inform smarter inventory decisions.  The key is activation , embedding AI and analytics into daily workflows, not side projects. Unified data shortens the distance between question and answer.  PWC research   shows organizations with unified data see up to 10× higher ROI  on AI initiatives than those still operating in silos.  🔗 Explore Fabric’s AI capabilities in Built-In AI, Real-World Potential .   Planning for 2026  Looking ahead, unified analytics will shape how companies plan. Scenario modeling and “what-if” simulations, powered by connected data, allow leaders to test strategies before committing budgets. It’s the closest thing to business time travel — and it starts with clean, connected information.  4. Stay the Course  Digital transformation rarely fails because of technology. It fails because organizations lose alignment midway.  In that same McKinsey study , the findings tell a familiar story — about 70% of large-scale transformations still fall short , most often from weak planning or cultural resistance. Fabric adoption is no exception.  Common pitfalls include:  Over-governing:  slowing adoption with excessive control  Cost sprawl:  neglecting consumption monitoring  Change fatigue:  underestimating the human side of transformation  AI overreach:  chasing headlines instead of business outcomes  The antidote is steady navigation. Start with clear metrics, celebrate small wins and communicate progress often.  As we remind clients, AI is the amplifier — Fabric is the foundation.  One without the other won’t get you to orbit.  Your Next Orbit  The leaders who will thrive in 2026 aren’t the ones who adopted AI fastest — they’re the ones who built the infrastructure to make it work.  Unified analytics through Microsoft Fabric transforms data from an operational burden into a strategic asset. It lets teams see the whole picture, act with confidence and plan with precision.  At Interloop, we help organizations bridge strategy and execution — from Fabric architecture and migration to enablement and AI activation. We help your teams build the frameworks that keep insights trustworthy and scalable.  Ready to activate your data?  Get started with Interloop  and see how we can help you connect Fabric, Fivetran  and your customer tools for seamless, scalable impact.

By Mclain Reese Microsoft Fabric   is a powerful analytics platform — but it isn’t built to handle every data integration need. When it comes to reverse ETL (Extract, Transform, Load) and connecting to the wide range of tools that marketing and sales teams rely on, Fabric needs a wingman.   That’s where   Fivetran   — and its newly acquired   Census   technology — comes in. Together, they make it possible to turn data stored in Fabric into actionable insights that drive personalization, automation, and growth.  What It Means  Fabric excels at analytics and warehousing. Fivetran’s reverse ETL capabilities excel at connectivity. Many organizations have valuable customer and product data siloed in systems outside their engagement stack — CRMs, CMSs, or marketing automation platforms.   Fabric alone can’t close those gaps, but Fivetran’s reverse ETL functions bridge the divide, syncing curated data from Fabric directly into customer engagement tools.  This enables high-value use cases like dynamic segmentation, lifecycle marketing, and hyper-personalized campaigns — all powered by a single, trusted data source.  How It Works  Here’s how Fabric and Fivetran’s reverse ETL capabilities work together in a typical workflow:  Extract siloed data   — Pull customer, product, support, and sales data from multiple systems into Fabric.  Transform in Fabric  — Use Fabric’s notebooks and transformation tools to clean, join, and enrich the data — preparing it for your engagement tool’s schema.  Sync with Fivetran  — Connect Fabric’s curated tables to your CRM or marketing automation platform, mapping fields that populate key data points for Sales Sequences and Marketing Emails.  Activate in the customer engagement tool  — With enriched, up-to-date data, teams can build dynamic user segments and launch targeted campaigns that meet customers where they are.  Why It Matters  Fabric alone can’t connect directly to many popular business tools — a limitation that can stall activation. Fivetran fills this gap with its broad connector library , empowering marketing and sales teams to use all available data for smarter segmentation, campaign automation, and better engagement.  By using events and attributes sourced from Fabric, organizations can send personalized messages and recommendations at every stage of the buyer’s journey — turning static analytics into real-time action.  Example: Turning Manual Workflows into Automated Wins  A client recently used this approach to unify customer data stored across multiple systems outside of  HubSpot . By extracting it into Fabric, transforming it to match HubSpot’s schema, and syncing via Fivetran, they automated what had been a manual, error-prone process.  The result — cleaner data, faster execution, and personalized email campaigns that drove measurable engagement and conversion gains. What once took hours of spreadsheet manipulation and manual imports now happens automatically — freeing teams to focus on strategy instead of data wrangling.  Closing Thoughts  Microsoft Fabric is redefining how teams analyze data — but when paired with Fivetran’s reverse ETL, it also redefines how they activate  it. The combination bridges the last mile of data integration, helping organizations unlock siloed insights, enrich their CRMs, and deliver personalized experiences at scale.  Ready to activate your data?   Get started with Interloop   — and learn how we can help you connect Fabric, Fivetran, and your customer tools for seamless, scalable impact.

By Ralph Jaquez  About the Series   Microsoft Fa bric Functions is  Interloo p’s deep dive into the features that make Fabric not just powerful, but practical. We’re cutting through the noise to show how these tools drive real business results. Whether you're leading strategy or scaling systems, this series is built to help you turn complexity into clarity – and manual tasks into momentum.   Not Just a Translator — A Game Changer for Self-Service  There’s been a lot of buzz lately around Fabric Data Agents  — and for good reason. If you’ve read the Microsoft docs, you already know the core pitch: ask questions in plain English, get answers from your data without writing SQL, DAX, or KQL.  But the real value of a Data Agent isn’t just in translating language. It’s in how it changes the way people access and trust data  — especially for teams that have long been stuck behind bottlenecks.  Why You’d Actually Use One  Let’s walk through a (fictional but familiar) example: PalmettoEV , a growing electric scooter and small EV rental company operating in cities across the U.S. and Europe.  PalmettoEV’s data lives in multiple systems:  App usage in Firebase  Kiosk activity in SQL Server  Loyalty data in MySQL  Vehicle telemetry in BigQuery  All of it has been harmonized in Microsoft Fabric so the team can analyze trips, vehicles, customers, and payments in one place.  Before Data Agents, a simple question like “How many unique riders did we have in Barcelona last month?”  meant:  Submitting a request to the data team  Waiting for someone to write and validate a query  Going back and forth when definitions didn’t match  With a properly configured Fabric Data Agent , that same question can be asked in natural language — and answered instantly.  Why? Because the Data Agent already knows:  Which tables to query  How the business defines a “rider”  What counts as “Barcelona”  Who is allowed to access what  Where It Shines for Teams Like PalmettoEV  Think of the Data Agent as a domain-specific concierge for your data.  It allows users to ask targeted, business-relevant questions like:  “Which scooters had repeated downtime last week in Madrid?”   “List riders who booked more than three trips in Paris this month.”   “Show daily revenue in Austin for the last 30 days.”   Each of these works — and delivers trusted answers — because the agent is built on a clean, business-aligned model.  The Real Magic: Your Data Model  Here’s the hidden truth behind every successful Data Agent: the magic isn’t in the AI — it’s in the model.   For PalmettoEV, that meant doing the foundational work:  Harmonizing vehicle IDs across systems to track downtime  Defining a “trip” in one consistent, business-accepted way  Linking customers, trips, and payments with clean, trusted relationships  When the underlying model is strong, the agent becomes a reliable tool — not a novelty. Everyone gets the same answer to “What’s our average trip duration per city?”  — and it’s the right one.  When the model is weak or inconsistent? You get vague results, mismatched metrics, and frustration. That’s when trust erodes, and teams revert back to manual queries (or give up altogether).  Why This Matters for Mid-Market Leaders  Fabric Data Agents make it possible to scale trusted, self-service access to data — without overburdening your data team or compromising on accuracy.  But here’s the catch: self-service only works when the answers are right.   That’s why the real win isn’t just spinning up a Data Agent — it’s investing in the modeling work that makes it useful:  Aligning terms across teams  Cleaning up relationships  Standardizing definitions  Harmonizing across platforms  Done right, you get a tool that meets users where they are — and gives them answers they can act on.  This Is Where We Come In  At Interloop, we specialize in building harmonized, business-friendly models that make tools like Fabric Data Agents shine — whether your data lives across ERPs, CRMs, telemetry feeds, or a mix of cloud and on-prem systems.  Our goal is simple: make sure when your users ask a question, they get an answer they can trust.   Final Word  Fabric Data Agents aren’t a shortcut around modeling — they’re the best reason to double down on it.  If you want analytics your team can actually use (and trust), this is a powerful tool worth exploring. But like most things in data, it works best when the foundation is solid.  Let’s make sure it is.  Curious whether your data model is ready for a Fabric Data Agent?   Let’s get you there.   Get looped in today.

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A place to store the data accumulated from a wide range of heterogeneous Sources, generally used for data analysis and reporting. For example, Microsoft Azure, Amazon Redshift, Google BigQuery or Snowflake. Interloop has both Data Lake & Data Warehouse capabilities baked in. Warehouse A place to store the data accumulated from a wide range of heterogeneous Sources, generally used for data analysis and reporting. For example, Microsoft Azure, Amazon Redshift, Google BigQuery or Snowflake. Interloop has both Data Lake & Data Warehouse capabilities baked in. Ready To Get Started? You're one small step from starting your data-driven journey. LOOP ME IN

A software distribution model where the application is hosted by a company on its servers and is accessed by clients via the internet by paying a subscription fee. For example, Salesforce. SaaS (Software as a Service) A software distribution model where the application is hosted by a company on its servers and is accessed by clients via the internet by paying a subscription fee. For example, Salesforce. Ready To Get Started? You're one small step from starting your data-driven journey. LOOP ME IN