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In 2025, AI may drive the headlines — but trust drives the results. For mid-market leaders, confidence in data is now the line between progress and paralysis. The Data Confidence Gap Is Growing Data is everywhere. Confidence in it? Not so much. In an era where business leaders are expected to back every move with data, trust in that data is eroding. According to Salesforce’s March 2025 Trust in Business Data Leaders Survey , fewer than half of U.S. executives say their data strategies align with business priorities — a drop of 14 percentage points from 2023. This disconnect isn’t just frustrating — it’s expensive. Bad data now costs U.S. companies more than $3 trillion annually . And the impact isn’t limited to dashboards: data-related trust gaps are directly slowing down digital transformation, AI initiatives, and cross-functional alignment. When leaders don’t trust their data, they delay action — or, even worse, act on assumptions. Why Mid-Market Teams Feel It More Mid-sized organizations face a unique challenge. They have enterprise-level complexity, but often without enterprise-level infrastructure or data teams. The result? A patchwork of systems, spreadsheets, and self-serve analytics that frequently operate on slightly different truths. The average mid-market company now runs nearly 900 cloud applications but integrates only 29% of them . It’s no wonder nearly half of mid-market CFOs say poor data integration is blocking timely, critical decisions. Trusted data isn’t just a tech-team problem — it’s the foundation every AI initiative depends on. It’s a confidence accelerator that spans the entire organization and determines whether insights actually turn into impact. What Makes Data Trusted? Trust doesn’t come from volume or visualization. It comes from consistency, clarity, and context — regardless of who’s asking the question. To earn confidence from decision-makers, data must be: Reliable:  Clean, complete, and accurate Governed:  Defined the same across systems and teams Traceable:  Easy to audit, explain, and reverse-engineer Accessible:  Available to the right people at the right time Integrated:  Not stuck in siloed apps or shadow spreadsheets In short, trusted data is data that people don’t need to second-guess. AI Is Only as Trustworthy as Its Data AI has become a boardroom priority — but too often, it’s launched without a stable foundation. In 2025, a whopping 95% of organizations report little to no measurable business return on their generative-AI investments ( MIT Media Lab / Campus Technology ). And according to Salesforce’s Your Data, Your AI  study, more than half of professionals don’t fully trust the data powering their company’s AI tools. Trusted data, on the other hand, turns AI from risky to ready: Predictive insights become believable Generative tools get smarter — and more explainable Self-service analytics (like Microsoft Fabric’s Copilot tools) deliver the same answer every time — regardless of who’s asking The message is clear: trust is the real unlock for AI adoption. Microsoft Fabric: Unifying Data for Trust at Scale Enter Microsoft Fabric  — a platform built from the ground up to unify your data stack. Fabric consolidates data engineering, transformation, warehousing, and analytics into one seamless experience. That means less duplication, fewer silos, and more transparency into where your numbers come from and how they were calculated. With OneLake , built-in lineage views, and centralized security, Fabric helps teams: Align on a single source of truth Trace metrics from dashboard to data source Streamline governance without slowing down access Microsoft Fabric isn’t just unifying your data stack — it’s redefining how trust scales. With OneLake and Copilot integrations, Fabric creates traceable, explainable systems that keep AI and analytics aligned with how teams think and make decisions. For mid-market organizations, it’s a chance to modernize without rebuilding from scratch. 🔗 Explore Interloop’s Microsoft Fabric Overview Interloop’s Role: The Bridge, Not the Bottleneck Many organizations think of trust-building as a compliance task. Interloop takes a different approach in that we design data systems that earn trust through architecture — not red tape. Our mission is simple: help mid-market teams activate data they can trust and AI they can scale. That means helping clients: Connect tools like Fivetran to automate clean, consistent ingestion Implement Microsoft Fabric to unify reporting and metrics Build integrated pipelines that surface trusted, explainable insights — not just data dumps Because Interloop is both technical and strategic, we serve as a bridge between systems and stakeholders, aligning IT, ops, and leadership around the same metrics and definitions. 🔗   See how we go from file drop to business action in 60 seconds Trusted Data in Action According to Gartner’s 2025 Future of Data & Analytics brief, organizations that establish clear data-trust frameworks are twice as likely to outperform peers in revenue growth and operational efficiency.  The takeaway is simple: trusted data drives better business performance. Interloop clients have seen similar measurable gains after adopting Microsoft Fabric — from cutting report latency from days to hours to strengthening visibility and consistency across key business metrics. Fabric’s built-in lineage tools help teams trace data in minutes instead of days, turning once-siloed information into shared understanding. Trusted data isn’t flashy — it’s foundational. How to Start Building Trust If your team is second-guessing data — or not using it at all — start here: Audit your core KPIs.  Are they defined and calculated the same way across tools? Check your lineage.   Can your analysts explain where your most-used metrics come from? Connect your systems.  Integrate critical data sources (CRM, ERP, finance) into a unified platform. Decide what “trust” means.  Set measurable goals for data availability, accuracy, and access. Bring in the right partner.  Choose a consultancy that can bridge strategy, systems, and team needs — not just implement tooling. The Bottom Line: Trust Moves Business Forward You don’t need more data dumps or dashboards — you need data you can count on. When your teams trust the numbers, decisions get faster, strategies get sharper, and AI turns from buzzword to business engine. Interloop builds that kind of confidence into your architecture — keeping every insight aligned and every decision on course. Ready to activate your data? Start with Interloop — and see how we can help you connect Fabric, Fivetran, and your customer tools for seamless, scalable impact. Get looped in today .

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.

By Luisa Torres   About the Series   Microsoft Fabric Functions is Interloop’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.   The Promise of Built-In AI (Without the Buzzwords)  AI is everywhere — but where does it actually add value for your business?  With Microsoft Fabric’s built-in AI Functions, the potential is right at your fingertips. No complex setup, no training models, no separate infrastructure. Just smart, simple capabilities that live inside the tools you already use — ready to help you work faster, smarter, and more consistently.  These functions are especially useful for text-heavy tasks: summarizing survey results, tagging customer feedback, extracting data from messy notes, translating content for global teams, and more.  The best part? You don’t need a data science team to use them.  What You Need to Get Started  If your organization is already using Microsoft Fabric, you're nearly there. Here’s what’s required:  A Fabric workspace (F2 or P SKU license)  AI Functions enabled in your environment  Optional: your own Azure OpenAI resource (for additional customization or during trial periods)  No servers. No outside APIs. No training loops. Microsoft handles the heavy lifting — so your team can focus on applying the insights.  What These AI Functions Actually Do  Fabric currently includes eight AI Functions designed to streamline and elevate how your team works with unstructured text. Here's a look at what’s available today — and why it matters.  Text Similarity   Quickly compare two pieces of text to assess how closely they align in meaning. Useful for matching customer complaints to known issues or grouping support tickets by theme.  Text Classification   Automatically tag or categorize incoming messages, forms, or documents. For example, sort customer feedback into categories like billing, tech support, or feature request — without manual review.  Sentiment Analysis   Gauge emotional tone across surveys, product reviews, or emails. Get a clear pulse on how customers or employees are feeling at scale.  Information Extraction   Pull specific details — like names, dates, locations, or product codes — from large batches of text. A fast track to turning unstructured text into structured, usable data.  Grammar and Spell Correction   Clean up typos, fix punctuation, and apply professional polish to rough input. Great for internal reports, customer comms, and audit readiness.  Summarization   Shorten long reports, meeting notes, or customer feedback into digestible highlights — without losing the important stuff.  Translation   Instantly translate content between languages. Ideal for global businesses managing multi-market communications.  Response Generation   Draft helpful, on-brand replies to common customer questions or internal prompts. Think of it as a built-in assist for everyday messaging needs.  Each of these tools can be used directly within your Fabric workflows — often as easily as applying a formula in Excel.  Why It Matters for Mid-Market Businesses  These AI Functions aren’t about replacing people — they’re about freeing them up to focus on higher-value work.  Save time   AI can scan, sort, and summarize in seconds — leaving your team with more time for strategy, interpretation, and action.  Improve decision-making   When sentiment analysis or pattern detection is applied early, teams spot trends faster — and react sooner.  Scale operations without scaling headcount   Whether you’re managing 100 survey responses or 100,000, AI applies the same logic, instantly and consistently.  Ensure consistency and quality   These functions help eliminate manual variability and reduce the risk of human error — great for audits, reporting, and customer trust.    Start Small, Think Big  You don’t need to be an AI expert to start seeing results.  Apply a function to your next customer feedback export. Use summarization on a long report. Try translation on your international reviews. Small use cases build trust and open the door to larger automation and insights efforts.  And if you’re not sure where to begin — we’re here to help.  Fabric AI Functions are currently in public preview. As with any AI tool, always review output before taking action.  Curious where Fabric AI Functions could make a difference in your business?   Let’s explore it together.  Get looped in today.

By Mclain Reese   About the Series:   Microsoft Fabric Functions is Interloop’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.   Rethinking File-Based Processing  Event Streams are Microsoft Fabric’s solution for real-time data detection and intelligent automation. While often associated with IoT sensors or clickstream activity, their value goes far beyond that. For organizations managing files day in and day out — spreadsheets, exports, inventory reports — Event Streams offer a faster, smarter way to work.  They allow you to detect when a file hits a storage location, evaluate its content or naming convention, and trigger an automated workflow within seconds. No polling, no batch jobs, no delays.  The File Bottleneck You’re Probably Living With  Every business handles file-based data. Daily sales reports from POS systems. Customer lists exported from CRMs. Payout summaries from processors. Inventory files passed between warehouses. It’s all part of the operational hum — and for many teams, it’s also a manual headache.  We’ve seen this story before:  Analysts refreshing folders, waiting on file drops.  Batch jobs running on fixed schedules — whether the data’s there or not.  Processing delayed by time zones, weekends, or simple oversight.  Human error sneaking into spreadsheets and cascading into campaigns.  Manual handling isn’t just inefficient. It introduces risk — of missed revenue, broken trust, and wasted effort.  A Real Example: How a Bank Automated the Pain Away   One of our clients, a national banking institution, came to us with a common challenge. Their marketing team needed regular exports from Salesforce — files delivered by dealership clients — to power their targeted outreach campaigns. But their internal process was slowing them down.  Here’s what that looked like:   A full analyst day, every other week, just to prep the data.  Messy Excel workflows full of VLOOKUPs and room for error.  Campaign delays when files arrived late.  Risk of emails going to the wrong people — or not going out at all.  This wasn’t just a workflow problem — it was a credibility issue.  What We Implemented: Fast, Smart, and Fully Automated  By introducing Event Streams and Data Activator Reflexes, we gave them a system that reacts in real time and operates with business logic baked in.  Now, when a Salesforce export file lands in their ADLS Gen2 folder:  The Event Stream detects it instantly.  A Data Activator Reflex checks that it meets the right criteria.  The appropriate workflow is triggered within 60 seconds.  Built-in validations clean and standardize the data.  Final outputs are pushed directly into their marketing system — ready to go.    The Payoff: Tangible Business Wins  Saved time:  They reclaimed over 8 analyst hours a month — hours now spent on strategy, not spreadsheets.  Reduced risk:  No more human error in targeting or processing. Every campaign hits the right audience.  Better performance:   Campaigns launch on time. Client relationships stay intact. The system scales effortlessly as volume grows.  Event Streams didn’t just make things faster — they made them smarter and safer.  Why This Matters for Mid-Market Leaders  1. You get immediate business responsiveness.   Event Streams eliminate the lag between file arrival and business action. Whether a file drops at 3 p.m. or 3 a.m., processing begins right away. This keeps you on pace with global partners, campaign schedules, and operational SLAs.  2. You gain intelligent, built-in business logic.  With Reflexes, you can route files based on name, type, or even content. Set conditions. Apply logic. Automate decisions that used to live in someone’s head — or worse, in a clunky spreadsheet macro.  3. You connect directly into the systems that matter.  Need to trigger a pipeline? Write to a Lakehouse? Send data to your CRM? Event Streams play well with the entire Fabric ecosystem, giving you full control from intake to output.  So, When Do Event Streams Make Sense?  If your team regularly handles files that arrive on unpredictable schedules, vary by format or business function, or tie into critical downstream systems — Event Streams are for you. They’re especially valuable when timeliness and accuracy are non-negotiable.  They may be overkill, however, for ultra-simple workflows with consistent delivery and no branching logic. But if your operations are scaling or your file processes are getting more complex, they’re a smart, future-ready investment.  Making the Case Internally  To start small but smart, identify one manual workflow that happens often:  Estimate how many hours your team spends on it monthly.  Add up the risk or cost of error.  Pilot an Event Stream flow on that one scenario.  From there, it’s easier to identify and prove ROI — then scale it up.  Final Word  Event Streams turn file chaos into clarity. They remove the guesswork, cut the delay, and protect your most valuable resources: time, trust, and talent.  For our banking client, that meant cleaner data, faster campaigns, and happier clients. For you, it might mean freeing up a bottleneck that’s been quietly costing more than you realized.  This post is part of our Microsoft Fabric Functions Series. Stay tuned for our next breakdown: taking your data to the next level with AI functions.  Have file-processing friction or automation questions? Let’s solve them.  Get looped in today .

Unlocking Innovation and Efficiency for Growth-Stage Companies  By Tony Berry  Today’s data teams are facing pressure from all sides. The mandate is clear: deliver faster insights, better decisions, and smarter AI-driven solutions—without compromising accuracy, compliance, or performance.  The opportunity is massive. But so is the complexity.  To stay competitive, growth-stage companies need more than raw data—they need modern infrastructure, AI-embedded workflows, and seamless collaboration across tools and teams. That’s where Microsoft Fabric and Copilot come in.  At Interloop, we’ve seen how teams move faster and think bigger when data and AI work hand in hand. In this Launchpad edition, we explore the most common challenges facing data teams today—and how Microsoft Fabric  and Copilot  can help solve them at scale.  Common Data Challenges Slowing Teams Down  Even experienced data professionals run into familiar friction. Here are five hurdles we see again and again:  Data Quality & Consistency   Incomplete, outdated, or mismatched data remains a top barrier to decision-making. Poor data quality breaks trust—and breaks pipelines.  Scalability & Performance   Teams are managing more data than ever before. Processing, storing, and analyzing at scale without degrading performance is a constant balancing act.  Siloed & Disparate Systems   Legacy platforms, point solutions, and department-specific tools create fragmented data environments and manual workarounds.  Security & Compliance Pressures   With evolving privacy laws and increased data volume, maintaining compliance and safeguarding access is mission-critical—and increasingly complex. Talent Shortages & Skill Gaps   Hiring experienced data engineers and AI specialists is tough. Upskilling internally takes time most teams don’t have.  How Fabric + Copilot Help Teams Scale Smarter  Microsoft Fabric and Copilot offer a modern, AI-driven approach to solving these challenges. Together, they form a unified platform that enables faster, safer, and more collaborative data work—whether you’re wrangling spreadsheets, building dashboards, or deploying models.  Here’s how they help teams overcome real obstacles:  Improve Data Quality   Copilot automatically flags inconsistencies, fills missing fields, and standardizes formatting. Example: A retail chain used Copilot to unify sales data across 300+ stores, improving forecasting accuracy and reducing stockouts.   Scale Workloads On-Demand   Fabric intelligently allocates compute resources in real time, while Copilot prioritizes workloads based on business needs.  Example: A financial services firm cut processing time in half using Copilot to scale analytics during peak reporting cycles.   Unify Disconnected Systems   Copilot leverages natural language processing and smart connectors to harmonize legacy and modern platforms.  Example: A healthcare org unified patient records across five databases, enabling a full 360° view of care.   Ensure Real-Time Security & Compliance   Copilot monitors access, flags anomalies, and enforces policies dynamically.  Example: A pharmaceutical company used Copilot to streamline internal audits and strengthen regulatory compliance.   Empower More People, Faster   With low-code/no-code functionality, Copilot enables non-technical users to participate in data workflows.  Example: A manufacturer used Copilot to equip frontline teams with simple, self-serve reporting tools—reducing bottlenecks and boosting efficiency.   More Ways to Use Copilot Across Fabric  Because Copilot is embedded across Microsoft Fabric’s unified architecture, data teams can tap into AI support at every layer—from data ingestion to transformation to visualization.  Data Factory   Generate transformation code with natural language prompts.  Example: A retail company automated data ingestion from multiple e-commerce platforms, accelerating reporting cycles.   Data Science & Engineering   Copilot can interpret existing code, automate data prep, and enrich datasets for advanced analytics.  Example: Marketing teams used Copilot to segment audiences and design more targeted campaigns.   Power BI   Create reports and visuals simply by describing what you need.  Example: BI teams reduced turnaround time on custom dashboards while improving clarity and impact.   Real-Time Intelligence   Translate natural language into KQL to analyze streaming data.  Example: Analytics leads used this feature to monitor operations in real time and act on live trends.   OneLake   Manage and discover data across the business in a unified layer.  Example: A logistics firm used OneLake and Copilot to centralize shipping and warehouse data, improving transparency enterprise-wide.   Ready for Takeoff?  Whether you're building the foundation or scaling your AI strategy, Fabric and Copilot offer the tools to accelerate your journey. These platforms aren’t just for enterprise giants—they’re built for growth-stage companies ready to work smarter and unlock new value from their data.  Need help charting your roadmap? Interloop’s Flight Plan consultation can help you map what’s possible—and make it actionable.  From insight to action - let’s get you looped in .

By Ralph Jaquez  If you’re working with Microsoft Fabric or managing Power BI workloads, understanding how Capacity Units (CUs) work is essential to planning, optimizing, and scaling effectively. Whether you’re choosing the right SKU, trying to make sense of your existing capacity, or just troubleshooting mysterious slowdowns, this post breaks down what CUs are, how they’re metered, and how they affect your workloads.  What Are Capacity Units (CUs)?  A Capacity Unit (CU) is the core metric Microsoft uses to measure and provision compute power in Microsoft Fabric. It defines the amount of compute  and memory resources available to a Fabric capacity—think of it like a fixed-size engine powering your workloads.  When you purchase a Fabric capacity (e.g., F8, F64), you’re buying a fixed number of CUs per hour. For example, an F8 SKU provides 8 CUs that are continuously available—regardless of whether they’re actively being used.  Importantly, CUs are only consumed when compute work is actively being done. Creating or storing a lakehouse, warehouse, dataset, or pipeline does not use compute. Similarly, OneLake storage doesn’t pull from your CU balance.    How CU Consumption Is Metered  CU usage is tracked in CU seconds —measured based on how many CUs are used and for how long. This “metering” works like a utility bill—continuously recording usage as your workloads run.  Examples:  A job that uses 4 CUs for 100 seconds = 400 CU seconds  Eight jobs using 1 CU each for 50 seconds = also 400 CU seconds  This metering helps you understand both volume and intensity—not just how many jobs ran, but how heavily they taxed your environment.  Capacity Units Explained: The Highway Analogy  Think of Microsoft Fabric  as a highway.  Your F SKU determines how many lanes your highway has. An F8 gives you 8 lanes. An F64 gives you 64. These lanes are always available to you, and you’re billed for reserving them—regardless of whether they’re at full capacity.  Each job is like a vehicle. Some are small sedans that use one lane. Others are oversized trucks that need 4, 8, or more lanes. The wider and longer the vehicle stays on the road, the more CU seconds it consumes.  Managing Workload Flow: Smoothing, Bursting, Throttling, and Queuing  Fabric uses several mechanisms to keep things running efficiently:  Smoothing  spreads out the impact of short-term spikes across a 24-hour window. One heavy job won’t immediately result in throttling or queuing—its average usage over time.  Bursting  allows workloads to temporarily exceed your lane count— if  spare capacity is available elsewhere. It’s opportunistic, not guaranteed.  Throttling  slows jobs down rather than blocking them. A truck needing four lanes might only get two, so it moves—but more slowly.  Queuing  delays execution entirely when there’s no available capacity. Jobs sit at the on-ramp until lanes clear.  When Are CUs Consumed? Interactive vs. Background Workloads  Microsoft Fabric distinguishes between interactive and background operations:  Interactive workloads  are user-initiated—like opening Power BI dashboards. They’re optimized for speed and usually prioritized unless the system is under strain.  Background workloads  include dataset refreshes, pipelines, notebooks, and T-SQL queries—basically anything that runs behind the scenes. Even if triggered manually, these jobs draw from compute capacity.  Important note: Power BI reports hosted in shared workspaces don’t directly consume Fabric CUs. But if they connect to Fabric-powered sources (lakehouse, warehouse, etc.), any compute work performed by those sources will consume CUs—whether through Import or DirectQuery mode.  Final Note  This post offers a simplified framework to help you understand how Capacity Units work in Microsoft Fabric. In real-world use, CU consumption will vary based on workload complexity, concurrency, and data size. And as Microsoft evolves Fabric, details around metering may change—so we always recommend checking the official docs for the latest.  If you’re evaluating SKUs, troubleshooting workload delays, or want help optimizing performance, our team is here to help.  In the next post, we’ll cover how to monitor CU usage with the Fabric Capacity Metrics App—including how to read CU second charts, interpret trends, and catch early signs of performance bottlenecks.  References  Microsoft Fabric licences and concepts   Optimize your capacity   Fabric Operations   The Fabric throttling policy   Surge Protection   🚀 Get looped in.  Explore how Interloop helps you make the most of Microsoft Fabric. From implementation to optimization, we’re your copilots for modern data.  Get looped in today .

By Mclain Reese  Marketing agencies have more tools than ever—but, with that, more pressure.   Clients expect fast, personalized, data-driven campaigns. Internal teams are juggling tight timelines, shifting scopes, and tech stacks that don’t always play nice. Both the demand and the churn is constant.   That’s where smarter data infrastructure and AI automation come in. Not to replace creativity—but to make the backend operations smoother, quicker, and more reliable.  The Common Headaches  Even with a solid CRM and campaign tools in place, most agencies still run into the same issues:  Contact-Company Confusion  When contact records aren’t properly tied to companies, communications miss their mark. It makes targeting harder and reporting murky.  Messy Account Hierarchies  Parent-child account relationships—especially in platforms like HubSpot or SugarCRM—can be tough to track. But they’re essential for segmentation and performance insight.  Hitting the Wrong People  All it takes is one wrong data point to send your best campaign to the wrong inbox. Without clean data, even brilliant creative falls flat.  What Better Data + AI Actually Do  This isn’t about buzzwords—it’s about real fixes to recurring problems.  Automatic Contact-Company Associations  AI-powered data pipelines can keep your CRM clean by automatically linking contacts to the right companies. Updates push straight into HubSpot, so your campaigns stay on target.  Clearer Account Structures  Build and maintain multi-level account hierarchies in your data layer, then sync them to your CRM. The result? Better reporting and smarter targeting.  Smarter Segmentation Using Data Flags  AI can tag contacts based on job title, seniority, company match, and more. That way, your list pulls the right people—and your message lands better.  What It Looks Like in Practice  One advertising agency partnered with Interloop to improve how they were targeting email campaigns in HubSpot. The team was manually uploading Excel files with lists of contacts—a time-consuming process that often led to errors.  Interloop helped them automate that flow. Using dynamic data flags, we mapped each contact to the right company, role, and campaign segment—then synced it all into HubSpot.  The result?  Sharper targeting, more reliable execution, and several hours a week freed up to focus on campaign planning and creative. Less time in the weeds. More time on the work that moves the needle.  Ready to Take This Off Your Plate?  Whether you’re deep in campaign season or just trying to clean up your CRM, Interloop meets you where you are. Our agency partners count on us to simplify the backend—so their teams can stay focused on what they do best.  Let’s talk about what’s possible and your marketing agency’s potential to do more with data – start here .

We’re excited to share some big news. Interloop has officially joined Atlantic , a leading provider of managed services and digital transformation solutions.  This move marks a new chapter for Interloop. It allows us to scale our mission, reach more organizations, and continue helping businesses connect, analyze, and act on their data to drive smarter decisions and strategic growth.  Since 2015, our team has collaborated with clients across various industries to address one of the most significant challenges in modern business: making data usable, accessible, and actionable. From integration to automation to advanced analytics, we’ve delivered real-world results by helping customers move from fragmented systems to unified platforms. Our deep expertise in Microsoft Fabric has played a key role in making that possible.  Now, with Atlantic’s backing, we’ll be able to do even more.  "Joining Atlantic marks an exciting new chapter for Interloop," said our CEO, Jordan Berry, who will step into the role of General Manager. "This move allows us to scale our impact, reach more organizations, and stay laser-focused on our mission: helping businesses achieve more with their data. With Atlantic’s backing, we can deliver a more complete, end-to-end experience that combines our data modernization expertise with the infrastructure, security, and support growing companies need."  What This Means for You   We’re still Interloop. Same team. Same mission. Same commitment to helping you succeed with your data. But now, you’ll have access to even more resources, services, and expertise.  As part of Atlantic, we’re expanding our ability to help organizations:  Build a scalable data foundation for automation and AI  Unlock faster, more flexible analytics with Microsoft Fabric  Improve operational visibility and decision-making  Modernize systems and simplify complex tech stacks  Atlantic brings deep expertise in managed IT, cybersecurity, and cloud infrastructure. That means you’ll benefit from an end-to-end approach, from data strategy to the systems that support it.  "This partnership marks an exciting chapter for both companies," Berry added. "By joining forces with Atlantic, we can amplify our impact and bring even greater value to customers while remaining steadfast in our mission to help organizations achieve more with their data."  Want to Learn More?  You can read the official announcement from Atlantic here .  If you have questions about what this means for your business or how we can support your goals going forward, reach out to our team . We’re here and excited about what’s ahead.