Search Interloop

By Meaghan Frost  Your Power BI dashboards are only as powerful as the people who use them. But if accessing insights means bouncing between platforms, logging into separate tools, or dealing with disconnected data, adoption suffers—and so does decision-making.  Embedding Power BI reports directly within operational platforms like Salesforce or Sugar CRM removes those barriers, delivering analytics exactly where users need them. The result? Faster insights, fewer disruptions, and better business outcomes.  Bring Insights to the Right Place  Data is most valuable when it’s available at the right moment. With embedded Power BI dashboards, your teams can access critical insights without switching between tools or disrupting their workflow. Whether it’s a sales team reviewing real-time customer data inside a CRM or a finance team analyzing trends within an ERP, keeping insights in-platform eliminates unnecessary friction.  Cost-Effective, No Extra Tools Needed  Advanced analytics shouldn’t require an expanding tech stack. By embedding Power BI reports directly into the platforms your team already uses, you eliminate the need for additional analytics tools or redundant reporting systems. Instead of paying for separate visualization tools, teams can leverage Power BI’s robust capabilities exactly where they work—without added cost or complexity.  One Dashboard, Multiple Destinations  A common frustration for business analysts and data engineers is building dashboards only to be asked to recreate the same insights elsewhere. Embedding removes this redundancy by allowing teams to create one intelligence solution that seamlessly integrates into  multiple environments. This ensures data consistency across applications while freeing up technical resources for higher-value work.  Boost Productivity, Cut the Noise  The less time employees spend searching for insights, the more time they have to act on them. Embedding Power BI reports eliminates context switching, reducing wasted effort and improving focus. Instead of logging into multiple platforms to piece together information, teams get a centralized, real-time view of the data they need—without distraction.  Security That Adapts to Your Needs  Embedding Power BI isn’t just about accessibility—it’s about smart security. With built-in filtering and row-level security, reports dynamically adjust based on user roles, permissions, and application context. That means employees, customers, and partners see only the data relevant to them—no custom-built dashboards, no unnecessary duplication, just efficient, secure data access.  The Impact in Action  One of our clients, a leading material handling equipment dealer, embedded Power BI reports directly within their Sugar CRM. This allowed their sales team to access real-time ERP data and customer insights inside Salesforce, eliminating the need to jump between platforms.  The impact?  Time savings for the sales team when preparing for customer meetings.  Centralized data access that enabled management to make faster, more proactive decisions.  Stronger adoption and engagement with analytics, since insights were available where the team already worked.  Let’s Make Your Data Work for You  Your teams shouldn’t have to chase insights—insights should come to them. Embedded Power BI dashboards bring real-time, actionable data directly into the platforms your business already relies on, transforming decision-making and operational efficiency.  Want to explore what this could look like for your business? Get looped in today.

Microsoft’s AI Tour is more than just a showcase of cutting-edge technology—it’s a glimpse into the future of business. From AI-driven productivity to the critical role of data in fueling intelligence, the event reinforced a clear theme: AI success starts with a strong data foundation.  Interloop was on-site as a proud sponsor of the Detroit stop, braving the snow to connect with industry leaders and explore the latest advancements in Microsoft Fabric, Copilot, and Azure AI. Here’s what stood out.  Key Themes from the Keynote  Copilot is the UI for AI   Microsoft’s vision for AI is centered around Copilot as the primary interface between users and intelligent systems. With deep contextual understanding, Copilot doesn’t just assist—it anticipates, automates, and optimizes workflows.  Tools like Copilot Studio, Visual Studio, GitHub, and Azure AI Foundry give organizations everything they need to build custom AI-powered experiences.  Data is the Fuel for AI  Without data, AI is just a concept. Clean, curated, and connected data is what makes AI work. Companies that invest in their data estate today are the ones best positioned to harness AI’s full potential tomorrow.  The takeaway? AI isn’t a magic switch—it’s the result of a well-executed data strategy.  Microsoft Fabric: The Fast Track to AI Value  Microsoft Fabric is reshaping how organizations manage, unify, and leverage their data. As a fully integrated SaaS platform, Fabric removes silos and allows businesses to work with data no matter where it lives.  Organizations leveraging Fabric are accelerating their time to AI and BI insights, reducing complexity, and making data-driven decisions faster.  Emerging AI Macro Trends  Microsoft highlighted four major shifts AI is driving in business today:  Enhanced employee productivity and well-being  Reinvention of customer engagement  Reshaping business processes  Accelerating innovation at scale  AI is no longer a future ambition—it’s an operational reality. Organizations that embrace these shifts will set the standard for AI-powered success.  What the Interloop Team is Most Excited About  The event was packed with insights, but here’s what stood out most to our team:  Meaghan  – "With the imminent release of the OneLake catalog, the untapped potential for analytics within an organization's data estate is immense. These insights will pave the way for data to become a pivotal contributor to an organization's operations."  Jordan  – "AI is truly going to transform how organizations empower their employees, enhance customer experiences, and drive innovation. With the Azure AI Foundry Agent Service, we're one step closer to making this a reality across businesses of all sizes."  Tony  – "The Copilot AI Stack is set to revolutionize the way we approach AI development and deployment. With its cutting-edge tools and seamless integration capabilities, the possibilities are endless. We're on the brink of a new era in AI, and the excitement is palpable."  Final Takeaway: AI Strategy Starts with Data Strategy  The biggest lesson from Microsoft’s AI Tour? You can’t have an AI strategy without a data strategy.  Organizations that invest in scalable, well-governed data architectures today will be the ones driving real AI impact tomorrow. That’s where Interloop comes in.  Need help setting your data strategy for AI? As a Microsoft Certified Partner, Interloop is uniquely positioned to help businesses unlock the full potential of Fabric, Copilot, and AI—faster.  Achieve more with your data. Get looped in today .

By Matt Poisson   At Interloop, we know that speed alone isn’t the key to success—precision is. That’s why we take an iterative and kitted delivery approach , ensuring every project moves from concept to outcome efficiently and effectively.  Whether it’s custom dashboards, integrations, or AI-powered copilots, our delivery process is designed to help organizations activate their data with confidence. By aligning with unique customer requirements—including specific data sources, key metrics, and business objectives—we ensure solutions that don’t just function, but fuel impact.  A Flight Plan for Smarter Data Decisions  Every engagement begins with a Flight Plan, our structured discovery process that maps out your current data strategy and future state goals. This deep dive results in a customized data roadmap that helps clients move forward with clarity, efficiency, and scalability while understanding potential risks and opportunities. z For many organizations, the next step after a Flight Plan is building a strong data foundation. Using Interloop’s Orbit architecture, we unify data from disparate sources, preparing it for seamless activation. z Take, for example, a recent client who needed a dashboard to track revenue trends, sales performance, and inventory levels across multiple systems, including NetSuite (ERP), Salesforce (CRM), and a proprietary Oracle-based inventory tool. With a completed Flight Plan in place, we aligned on future objectives and began executing against a structured path—allowing the client to reach their outcome faster, with fewer roadblocks. z From Data to Decisions: The Kitted Delivery Process in Action  As data integration begins, our team works in parallel to design the best way to represent insights visually. Data is transferred into a Microsoft Fabric Lakehouse, following our data foundation process, while business intelligence experts collaborate with the client to design dashboards and reporting structures that match real-world needs.  Through structured design sessions, we develop a visual mockup of dashboards, refining the concept before build-out. This iterative approach allows for feedback and refinement before development begins, reducing surprises and ensuring alignment from the start.  Once the dashboard design is confirmed, the build phase kicks off. Throughout this process, the client remains actively engaged, with ongoing touchpoints to fine-tune user experience, optimize functionality, and ensure every decision aligns with business goals. By the time development is complete, the client receives a fully realized solution that’s been shaped by their insights every step of the way—not a black-box final product.  Why Kitted and Iterative Delivery Works   Every Flight Plan is uniquely tailored, but the process we follow remains consistent. Over years of working with clients across industries, we’ve developed a kitted approach—structured outcome steps that eliminate friction, optimize efficiency, and maximize value.  By creating repeatable, dependable processes, we ensure that:  Our team delivers with precision and reliability  Clients gain faster, more predictable outcomes  Every solution is scalable, adaptable, and aligned with long-term success  This methodology isn’t just about execution—it’s about building trust, delivering impact, and helping organizations achieve more with their data.  Unlock the full potential of your data. Get looped in today .

The People Side of AI: Change Management for Seamless Adoption  By Mclain Reese and Will Austell  Data is the fuel that drives modern business, but without seamless integration, critical insights get trapped in silos. Interloop’s Smart Sync ensures your data doesn’t just sit in storage—it moves, activates, and powers decisions across your organization in real time.  By synchronizing data from your Microsoft Fabric lakehouse to operational systems like CRMs, marketing platforms, and other SaaS applications, Smart Sync eliminates inefficiencies and transforms data into action.  What is Smart Sync?  Powered by Census, a leading data activation and reverse ETL platform, Smart Sync is Interloop’s advanced data synchronization service for Microsoft Fabric. It enables frictionless data movement between your data warehouse and sales, marketing, and customer engagement tools, ensuring consistency and accessibility across all business applications.  Instead of manually transferring or reconciling data, Smart Sync automates the process, keeping information in sync across your entire tech stack. The result is a unified, real-time view of your data that drives better decisions and stronger business outcomes.  How Smart Sync Works  Interloop’s Smart Sync extracts data from the Microsoft Fabric lakehouse and activates it across operational systems through a reverse ETL process. That means customer insights, sales activity, and operational data can flow effortlessly to:  CRM platforms like Salesforce and HubSpot  Marketing automation tools like Marketo and Mailchimp  ERP systems like SAP and NetSuite  Customer support platforms like Zendesk and ServiceNow  With more than 150 supported SaaS tools, Smart Sync enables real-time data activation without the need for custom engineering or complex scripts.  Why Smart Sync Matters: Real-World Benefits   Seamless multi-system integrations  – Push ERP sales data into your CRM to automate lead nurturing and marketing campaigns.  Proactive customer retention  – Sync churn risk analysis from your ERP to your CRM and automatically enroll at-risk customers in nurture campaigns.  Predictive maintenance for IoT  – Feed IoT machine failure predictions into service dispatch systems to schedule preventive maintenance.  Key Advantages of Smart Sync  Real-time synchronization  – Eliminate data delays. When one system updates, all others reflect changes instantly.  Data enrichment and unified customer views  – Combine and activate data from multiple sources for a more complete picture of your customers.  Improved efficiency and productivity  – Automate data entry, updates, and reconciliation, reducing errors and manual work.  Scalability and flexibility  – Whether you're a fast-growing startup or an enterprise, Smart Sync scales with your needs.  Use Cases: Where Smart Sync Delivers Value  CRM integration  – Sync customer records between CRMs, ensuring a unified view of interactions, purchases, and service history.  Data enrichment  – Aggregate data from multiple sources to enhance customer profiles and drive more personalized experiences.  Cross-platform reporting  – Break down silos by combining insights from different systems for more comprehensive analytics and reporting.  Why Choose Smart Sync?  The real power of data lies in its ability to drive action. Smart Sync ensures your business isn’t just collecting data—it’s using it in real time to inform decisions, personalize experiences, and improve operational efficiency.  By breaking down data silos and activating insights across your organization, Interloop’s Smart Sync transforms your Microsoft Fabric lakehouse into a real-time engine for business growth.  Ready to launch a smarter way to sync your data? Get looped in today .

The People Side of AI: Change Management for Seamless Adoption  AI isn’t just about cutting-edge algorithms and powerful automation—it’s about people. Successful AI adoption depends on how well an organization manages change, ensuring that employees feel empowered rather than overwhelmed. Without a clear strategy for guiding teams through the transition, even the most advanced AI solutions can struggle to take off.  At Interloop, we understand that the real challenge isn’t just implementing AI—it’s ensuring your people are ready to embrace it. That’s why change management is the fuel that propels AI from concept to transformation. And with our trusted partner, Microsoft, leading the charge in AI readiness, we have a proven flight path to guide organizations through the complexities of adoption.  Why Change Management Matters in AI  Change management is the structured approach to preparing and supporting individuals as they navigate organizational shifts. When it comes to AI, the goal isn’t just deployment—it’s alignment. Without the right strategy, AI initiatives risk turbulence, from employee resistance to operational inefficiencies. The key? Focusing on the human element.  Organizations that invest in change management see higher adoption rates, stronger engagement, and, ultimately, greater returns on their AI investments. By applying structured change management principles, leaders can transform apprehension into enthusiasm, ensuring AI technologies are not just implemented—but embraced.  Charting the Course: Microsoft’s People-Centric AI Adoption Framework  Microsoft’s approach to AI readiness reflects leading change management methodologies, such as Prosci. Their strategy emphasizes three essential pillars: leadership, communication, and training. When combined, these elements create a launchpad for AI success.  Leadership Engagement   AI adoption begins at the top. Leaders set the tone by championing AI initiatives, demonstrating commitment, and fostering trust. When leaders actively participate, employees are more likely to follow.  "Do as I say, not as I do" is a surefire way to derail an AI initiative. Instead, leaders must embody the change, not just endorse it. This means engaging in training, using AI-driven tools, and reinforcing a culture of continuous learning.  Transparent Communication   AI implementation can spark uncertainty, making clear and consistent communication essential. Microsoft advises organizations to:  Be upfront about the ‘why’ behind AI adoption and its benefits.  Leverage multiple channels—emails, town halls, internal forums—to reach diverse audiences.  Encourage feedback to address concerns early and ensure employees feel heard.  Highlight success stories to showcase real-world benefits and reinforce momentum.  Comprehensive Training Knowledge is the gravitational pull that keeps AI initiatives on track. Employees must feel confident using AI tools, and training plays a pivotal role in ensuring competency. Microsoft recommends a mix of:  Self-paced online learning for foundational knowledge.  Hands-on workshops for real-world application.  Peer learning opportunities to encourage knowledge-sharing.  At Interloop, we believe in the Train-the-Trainer  model—equipping internal champions with the knowledge to train their teams. This approach not only accelerates adoption but also fosters long-term retention, as employees learn from familiar voices within their own organization.  Navigating Common Challenges  AI adoption isn’t without challenges. But with the right strategy, organizations can steer through turbulence and keep their mission on course.  Resistance to Change   Employees may worry about job displacement or struggle to see the benefits of AI. The solution? Involve them early, clearly articulate the ‘what’s in it for me?’ factor, and provide hands-on training to ease the transition.  Skills Gaps AI can be complex, and not everyone feels ready to dive into advanced analytics or automation. Investing in upskilling programs, mentorship, and external expertise ensures employees feel capable—not left behind.  Data Privacy & Security AI systems thrive on data, but that data must be handled responsibly. Implementing robust governance policies and aligning with compliance standards reassures employees and stakeholders alike.  Sustaining Momentum: Leadership & Support  Even after an AI solution is deployed, continuous leadership and support are essential to keeping innovation on course. Leaders must:  Champion the change by reinforcing the value of AI in daily operations.  Allocate resources to ensure employees have the tools they need to succeed.  Celebrate milestones to recognize progress and sustain engagement.  Additionally, creating ongoing support systems, such as help desks and dedicated AI adoption teams, ensures that employees always have a safety net as they integrate AI into their workflows.  Future-Proofing with a Culture of Innovation  For AI adoption to truly take off, organizations need more than just technology—they need a mindset shift. Cultivating a culture that values experimentation and adaptability is the key to long-term success.  Encourage hands-on experimentation with pilot projects and AI innovation labs.  Recognize and reward creative problem-solving and AI-driven improvements.  Promote cross-functional collaboration to integrate AI seamlessly across departments.  By making AI a part of the organization’s DNA, companies position themselves not just to adapt—but to lead.  Ready for Takeoff? Let’s Chart Your AI Journey  AI transformation isn’t just about technology—it’s about empowering people to embrace what’s next. With the right change management approach, organizations can ensure their AI initiatives don’t just launch but thrive.  At Interloop, we specialize in guiding organizations through AI adoption with strategic, people-first change management solutions. If you’re ready to accelerate your AI journey, let’s connect. Reach out today to discuss how we can help your team embrace AI with confidence.

By Meaghan Frost Artificial Intelligence is everywhere. This is leading to new feature announcements, new capabilities, and... sometimes leading to confusion. There are so many terms and tools to know, after all! This blog is intended to help explain some of Microsoft's key AI platforms and tools, noting what's what and supporting you on your AI learning journey. Let's dive in... Copilot Studio   Copilot Studio is a platform designed to extend and customize the capabilities of Microsoft 365 Copilot. It allows developers to create custom copilots tailored to specific business needs by integrating various data sources and actions. Key features include the ability to add knowledge from Dataverse tables, create topics with generative answers, and extend functionalities using plugins and connectors.    Azure Studio  Azure Studio is a comprehensive platform for developing, deploying, and managing AI applications. It brings together models, tools, services, and integrations necessary for AI development. Key features include drag-and-drop functionality, visual programming environments, prebuilt templates, and tools for advanced data integration and workflow orchestration.    Bot Framework  The Bot Framework is a set of tools and services for building conversational AI experiences. It includes Bot Framework Composer for designing bots, Bot Framework Skills for adding capabilities, and Power Automate cloud flows for integrating with other services. Key features include the ability to create and manage actions, define business rules, and integrate with various APIs.    Key Features and Use Cases  Copilot Studio :  Key Features : Customizable copilots, integration with Dataverse , generative answers, plugins, and connectors.  Use Cases : Enhancing productivity by creating domain-specific copilots, automating repetitive tasks, and providing contextual information to users.  Azure Studio :  Key Features : Drag-and-drop functionality, visual programming, prebuilt templates, advanced data integration, and workflow orchestration.  Use Cases : Rapid prototyping, building and refining AI applications, deploying scalable AI solutions, and managing AI workflows.  Bot Framework :  Key Features : Bot design with Composer, adding skills, integrating with Power Automate, defining business rules, and API integration.  Use Cases : Creating conversational AI experiences, automating customer support, integrating with enterprise systems, and enhancing user interactions.    Empowering Developers and Data Engineers  These tools empower developers and data engineers by simplifying the process of creating and deploying AI-driven applications.     Copilot Studio  allows developers to create custom copilots without deep technical knowledge, enabling them to focus on business-specific needs and integrate various data sources seamlessly.    Azure Studio  provides a comprehensive platform that supports the entire AI lifecycle, from model selection to deployment. Its user-friendly interface and prebuilt capabilities accelerate development and reduce the need for extensive coding.    Bot Framework  offers a robust set of tools for building conversational AI, allowing developers to create sophisticated bots with minimal effort. Its integration with Power Automate and other services streamlines the development process and enhances functionality.    Supporting the Future of AI and Machine Learning  These platforms are at the forefront of AI and machine learning innovation. In the next year, we can expect several advancements:  Enhanced Integration : Improved integration between Copilot Studio, Azure Studio, and Bot Framework, allowing for more seamless workflows and data sharing.  Advanced AI Capabilities : New AI models and tools that provide more accurate and context-aware responses, enhancing the overall user experience.  Increased Automation : More automation features that reduce manual intervention and streamline processes, making it easier to deploy and manage AI applications.    Preparing for the Future  Businesses should start preparing by:  Investing in Training : Ensuring that their teams are well-versed in using these platforms and understanding their capabilities.  Exploring Use Cases : Identifying areas where AI can add value and experimenting with pilot projects to understand the potential benefits.  Building a Data Strategy : Developing a robust data strategy to ensure that the necessary data is available and accessible for AI applications.    By leveraging these tools and preparing for the future, businesses can stay ahead of the curve and harness the full potential of AI and machine learning.  Get Looped In Trying to understand how to set your organization up for the best possible AI foundation?  We have a team of experts to support with that. Let us know you'd like to connect, and we'll happily support you on anything Microsoft, data, or artificial intelligence. Get Looped In today.

In today's rapidly evolving digital landscape, data has become the lifeblood of organizations, driving decision making and strategic initiatives. As businesses strive for operational excellence, a new discipline has emerged to streamline and optimize data processes: DataOps. This methodology, which combines agile development, DevOps, and lean manufacturing principles, is revolutionizing how organizations manage and utilize their data. Let's explore the rise of DataOps and how it can create a true competitive advantage for organizations of all sizes in the era of AI. What is DataOps? Data Operations (DataOps) is an automated, process-oriented methodology used by analytics and data teams to improve the quality and reduce the cycle time of advanced analytics. By fostering collaboration among data scientists, engineers, and technologists, DataOps ensures that every team works in sync to use data more effectively and efficiently. This approach encompasses the entire data lifecycle, from ingestion and processing to modeling and insights, enabling organizations to gain more value from their data. The Benefits of DataOps Accelerated Time to Value: DataOps enables faster development and deployment of analytics models by automating repetitive tasks and streamlining processes. This acceleration allows organizations to quickly adapt to market changes and make data-driven decisions in real-time. Improved Data Quality: By implementing continuous code quality checks and early detection of data inconsistencies, DataOps reduces errors and enhances data reliability . This approach leads to more accurate analysis and better business insights. Enhanced Collaboration: DataOps fosters a culture of collaboration across multidisciplinary teams, breaking down silos and ensuring that data is accessible and usable by all stakeholders. This collaborative environment drives innovation and improves overall productivity. Cost Reduction: Automation of data processes reduces the need for manual intervention, cutting down on operational costs. Additionally, by optimizing data workflows, organizations can achieve significant savings in IT expenses. Scalability + Flexibility: DataOps provides a scalable framework that can be tailored to the specific needs of an organization. Whether it's a small startup or a large enterprise, DataOps can be adapted to handle varying data volumes and complexities. Creating a Competitive Advantage In the era of AI, the ability to harness data effectively is a key differentiator. DataOps empowers organizations to leverage advanced analytics and AI technologies to gain a competitive edge. By enabling faster, more accurate decision-making, DataOps helps businesses stay ahead of the curve and respond proactively to market demands. Moreover, DataOps supports the creation of personalized customer experiences by providing deeper insights into customer behavior and preferences. This customer-centric approach fosters loyalty and drives growth, positioning organizations as leaders in their respective industries Conclusion The rise of DataOps marks a significant shift in how organizations approach data management and analytics. By striving for operational excellence with data, businesses can unlock new opportunities, drive innovation, and achieve sustainable growth. As the digital landscape continues to evolve, embracing DataOps will be crucial for organizations looking to thrive in the AI era. Get Looped In Are you ready to harness the power of DataOps for your organization? Let's  loop you in   - learn more about Mission Control, our DataOps Platform for Microsoft Fabric and explore how this emerging discipline can transform your data strategy for a true competitive advantage.

Everyone knows they need to better understand and adopt AI. Where do you begin? With your data, of course. But not all data is AI-ready. Let’s learn a bit more about the steps you need to take to make your data ready to adopt artificial intelligence.  Critical Steps to Prepare Data for Copilot (Extensions & Custom Agents)  Data Collection and Aggregation  Conduct a comprehensive data inventory to understand what data you have, where it is located, and its current state.  Gather relevant data from internal systems, external databases, and third-party sources. The goal is to create a comprehensive dataset that reflects the diverse and unique aspects of the business operations.  Aggregating data ensures that the AI model has access to a wide range of information.  2. Data Cleaning and Normalization  Remove duplicates, correct errors, and standardize formats of your data.  Data normalization ensures that all data points are consistent and comparable.  Inaccurate or inconsistent data can lead to inaccurate predictions and insights, undermining the trust in the AI system.   3. Curation  Transforming clean and normalized data into something that can be used by the AI model by selecting the most relevant variables and reducing dimensionality if necessary.  Establish clear and logical relationships between different data sets. This helps Copilot understand the context and connections within your data.  Use standardized calculation logic for measures and adopt clear naming conventions to enhances the efficiency of report generation.  4. Feature Engineering and Selection  Level of complexity depends on the development path: extension of Copilot for Microsoft 365 or completely custom agent.   Imposing a cutoff on the number of attributes that can be considered when building a model can be helpful. Feature selection helps solve two problems: having too much data that is of little value or having too little data that is of high value. Your goal in feature selection should be to identify the minimum number of columns from the data source that are significant in building a model. Check out this further insight in Microsoft Learn .  With extensions, features are handled by Microsoft  If you are building custom machine learning models or performing specific data analysis tasks, you will need to handle feature selection yourself. This involves applying statistical methods via modeling tool or algorithm to discard attributes based on their usefulness to the intended analysis  Reference Learn link above to list the different algorithms that Microsoft supports in feature selection.  Potential Risks   Inaccurate or Biased Models can have serious consequences, especially in critical areas like healthcare and finance, where decisions based on faulty AI predictions can lead to harmful outcomes.  Overly Simplistic Models can cause insufficient or incomplete data. This can lead to models that fail to capture the complexity of real-world scenarios. This can result in AI systems that are unable to make accurate predictions or provide meaningful insights.  Data Security -  Poorly integrated AI systems can be vulnerable to data security issues such as data leaks, data poisoning, and prompt injection attacks. These risks can compromise the integrity and confidentiality of both internal and client data.  Biased Predictions: Incomplete datasets can lead to biased AI predictions, while erroneous data, often due to human or measurement errors, can mislead AI into making incorrect decisions.  Poor Performance: AI models trained on deficient data inputs will produce inaccurate outputs, leading to poor performance and unreliable results. This can undermine the trust and effectiveness of AI systems.  Successful Example of Using Copilot After Proper Data Preparation  Case Study: Interloop Client Success  One notable example of a business successfully using Copilot after data preparation is an Interloop client in the construction materials industry. By following the critical steps of data collection, cleaning, and feature engineering, the company achieved impressive results:  Operational Efficiency: The AI-driven solution streamlined various operational processes, resulting in faster and more convenient way to input data.  Improved Production Insights: The clean and well-structured data enabled the AI to generate detailed production insights, helping the business to adjust engineering strategies for certain product specifications proactively.  Increase Access: The AI solution enhanced accessibility to data through integrations with productivity apps like Microsoft Teams desktop and mobile. Users no longer had to navigate through layers of SharePoint to access information.     The client ensured a smooth AI implementation through several key practices:  Defining a Minimum Valuable Experience (MVE)  – AI solutions are easily subject to scope creep. This client worked with Interloop to set a clear definition of what the first iteration of Copilot should be like  Depth over Width – the client was steadfast in maintaining depth of the project. In other words, they chose 1-3 specific use cases that they wanted copilot to master instead of trying to envision all potential use cases / questions their organization could ask  Launch to a Pilot Group – when launching the MVE, the client released the copilot to a small group of employees. This way they could control security, mitigate risk of failure, incorporate user feedback and test resonance with target audience. The pilot group also allowed the client to build momentum and excitement within the organization for the AI solution in hopes to drive internal adoption. Get Looped In Looking to achieve more with your data? Get looped in   with one of our data experts today to explore how we can support getting your data ready for AI and for scale.

By Tony & Jordan Berry Coming out of the Microsoft Ignite conference, we and many other organizations are abuzz with excitement about feature updates and announcements. We’ve summarized our thoughts on the top 10 most impressive feature updates in Fabric, designed to enhance the capabilities and efficiency of data management and analytics. These new features cater to a wide range of users, from data scientists and engineers to business analysts and decision-makers.   Fabric SQL Databases  Microsoft has announced the public preview of the SQL database in Microsoft Fabric, introducing several key features:  Simplified AI Development : Designed to streamline and accelerate the creation of AI applications.  Autonomous and Secure : Offers a self-managing, secure environment for your data.  Integrated Platform : Transforms Fabric from an analytics platform to a comprehensive data platform by integrating operational databases.  Optimized for AI : Enhances efficiency and effectiveness in building AI applications, with user studies showing significant improvements in task completion times.    Check out this blog  for more details.  Fabric Open Mirroring  Open mirroring in Microsoft Fabric enables seamless and continuous data replication from operational databases into Fabric OneLake, enhancing data accessibility and analytics capabilities:  Continuous Data Replication: Automatically mirrors data from operational databases into Fabric OneLake.  Landing Zone Integration: Provides a landing zone folder for applications to create metadata files and push data into OneLake.  Efficient Data Storage: Supports Parquet file format with various compression options for efficient analytics.  Read-Only Access: Ensures data integrity by providing read-only access to mirrored data via the SQL analytics endpoint.  Easy Setup: Simplifies the setup process, allowing you to mirror all data or select specific objects to mirror.    For more details, check out the full article .     PowerBI Write Back  A significant announcement was made about the new native write-back capabilities in Power BI, revolutionizing data interaction:  Direct Data Updates : Users can now update data directly within Power BI reports and dashboards.  Enhanced Interactivity : Real-time data manipulation with immediate reflection of changes.  Seamless Integration : Smoothly integrates with existing Power BI features, enhancing overall functionality.  Improved Efficiency : Streamlines workflows by reducing the need for external tools or manual data entry.  User-Friendly Interface : An intuitive interface makes data updates accessible to users of all skill levels.    Check out this LinkedIn post  from Prabhakaran Sethuraman  to see it further explained.  Fabric Workload Hub GA  The general availability of the Fabric Workload Development Kit was announced, introducing several key features:  Comprehensive Toolkit : Provides a complete set of tools for developing, testing, and deploying workloads in Microsoft Fabric.  Enhanced Developer Experience : Streamlines the development process with integrated debugging, monitoring, and optimization tools.  Seamless Integration : Easily integrates with existing Fabric services and workflows.  Scalability and Performance : Optimized for high performance and scalability, supporting complex workloads.  User-Friendly Interface : Offers an intuitive interface for developers of all skill levels.    We are actually building  Interloop’s Mission Control  with this capability and more info can be found here.     Fabric AutoML UI  The low-code AutoML interface in Microsoft Fabric offers powerful features for simplifying machine learning:  User-Friendly Interface : Allows users to specify ML tasks and configurations easily.  Pre-Configured Notebooks : Generates tailored notebooks based on user inputs for streamlined workflows.  Comprehensive ML Tasks : Supports regression, binary classification, multi-class classification, and forecasting.  Efficient Data Handling : Integrates with Fabric's lakehouses, supporting various file types like CSV, XLS, and JSON.  Automated Logging : Tracks all model metrics and iterations within existing ML experiments for organized management.    A detailed example with screen images of this exciting capability can be found here .  Real-Time Intelligence GA  A very exciting update was the general availability of the new Real-Time Intelligence workload in Fabric that introduces several powerful features for real time analytics:  End-to-End Solution : Provides comprehensive capabilities for ingesting, processing, analyzing, visualizing, monitoring, alerting, and acting on events.  Seamless Integration : Integrates smoothly with existing Fabric services, enhancing overall functionality.  Real-Time Analytics : Enables immediate insights and actions based on real-time data.  Enhanced Monitoring : Offers advanced monitoring and alerting features to keep track of critical events.  User-Friendly Interface : Designed to be intuitive and accessible for users of all skill levels.    A blog and short demo of this ground-breaking capability can be found here .    API for GraphQL GA  The API for GraphQL was also brought into General Availability “GA” introducing  several powerful features for efficient data querying and management:  GraphQL Development Environment : Provides an interactive in-browser playground for composing, testing, and visualizing GraphQL queries and mutations.  Automatic Schema Discovery : Automatically discovers data source schemas and generates queries, mutations, and resolvers.  Multi-Source Support : Supports querying multiple data sources, including Fabric Data Warehouse, Lakehouse, and SQL databases.  Relationship Management : Allows creation of one-to-one, one-to-many, and many-to-many relationships between data objects.  Code Generation : Generates boilerplate Python or Node.js code for local testing and development.    A good description of this feature can be found here .  Azure AI Foundry - Fabric Connector, AI Search, etc.  While not a Fabric specific announcement Azure AI Foundry was launched bringing together several tools for data and AI within Azure:  Unified AI Platform : Azure AI Foundry offers a comprehensive platform for designing, customizing, and managing AI solutions, bridging the gap between cutting-edge AI technologies and practical business applications.  Enhanced Developer Tools : New tools like the Azure AI Foundry SDK and an evolved Azure AI Studio improve the development, testing, and deployment of AI models.  Industry-Specific Solutions : Expanded AI model catalog with specialized solutions for healthcare, manufacturing, finance, and more.  Advanced Analytics : Integration of advanced vector search and retrieval-augmented generation (RAG) capabilities into Azure Databases.  Responsible AI : New tools for AI safety and compliance, including AI reports and risk evaluations.    A great summary of all these capabilities are detailed here .     Manufacturing Data Solutions  A very comprehensive set of manufacturing data solutions was announced in public preview providing:   Comprehensive Data Integration : Seamlessly integrates data from various sources like MES, machines, sensors, and applications into a unified manufacturing-specific data model.  Enhanced Operational Visibility : Utilizes AI assistants for real-time insights and operational visibility.  Accelerated AI Deployment : Facilitates the rapid deployment of AI solutions across manufacturing operations.  Factory Operations Agent : Provides advanced monitoring and control capabilities, enhancing operational efficiency.  Prebuilt Data Ingestion : Includes plugins for leading MES providers, factory applications, and IoT management platforms.  Semantic Graph Building : Constructs a semantic graph across manufacturing processes for better data-driven decision-making.    Full details can be found here .    PowerBI Summary By Copilot  This was a very interesting example of how Copilot can be an assistant in prepping report summaries for users providing several key benefits.   Automated Insights : Copilot generates summaries for Power BI report pages or full reports, providing valuable insights directly in email subscriptions.  Enhanced Accessibility : Summaries are included in emails, ensuring recipients can quickly grasp key information without opening the full report.  Flexible Delivery : Supports delivery to OneDrive or SharePoint, with summaries still sent via email.  User-Friendly Setup : Easy to enable and customize within the Power BI service.    A great example can be found here      The new features in Microsoft Fabric significantly enhance the capabilities of data management, analytics, and AI integration. By providing robust tools and interfaces, Microsoft empowers organizations to leverage their data more effectively, driving innovation and efficiency across various domains. These advancements not only streamline operations but also enable more informed and agile decision-making, positioning businesses for success in an increasingly data-driven world.  Talk to a Fabric Expert Want to discuss what these new features mean to your data landscape?  We have a team of data experts to support you. Connect with us today!

By Tony Berry Azure Data Lake Storage vs. OneLake: A Guide for DataOps Engineers In the expansive universe of data storage solutions, Azure Data Lake Storage (ADLS) and OneLake emerge as two stellar options for organizations navigating complex data landscapes. Both platforms offer robust features tailored to different use cases, but understanding their strengths can help your team chart the right course for your data operations. In this guide, we’ll explore each platform, highlight their unique capabilities, and dive into how Microsoft Fabric enhances their value—ensuring your team has the tools to accelerate insights and simplify operations. Azure Data Lake Storage (ADLS): The Workhorse of Big Data Analytics ADLS is a scalable, secure, and highly customizable data lake service, designed for teams handling massive data workloads. Think of it as a heavy-duty cargo ship in your data galaxy, ready to carry large volumes of structured and unstructured data across complex analytics pipelines. Key Features of ADLS: Scalability: Handles massive data volumes, perfect for big data analytics. Robust Security: Features encryption at rest and in transit, along with granular access controls. Seamless Integration: Connects with Azure Databricks, Azure Synapse Analytics, and more. Cost-Efficiency: Offers tiered storage and pay-as-you-go pricing to optimize costs. Customizability: Allows full control over storage accounts, access tiers, and lifecycle policies. Blob Storage Compatibility: Built on Azure Blob Storage, offering broad compatibility. Top Use Cases for ADLS: Big Data Analytics: Powering large-scale analytics workflows with unmatched scalability. Data Warehousing: Storing and querying structured and unstructured data. Machine Learning: Supporting large datasets required for training advanced models. OneLake: The Unified Data Lake for Simplified Collaboration OneLake offers a fresh perspective on data management. Positioned as the "OneDrive for data," it simplifies the data lifecycle by unifying storage, access, and collaboration across teams. Picture it as your data mission control center, seamlessly integrating data sources for effortless collaboration and real-time analytics. Key Features of OneLake: Unified Platform: Acts as a central repository, eliminating silos. Ease of Use: A user-friendly interface accessible to technical and non-technical users alike. Data Virtualization: Query data in place, avoiding unnecessary duplication. Collaboration-Ready: Designed for cross-team data sharing and governance. Fabric Integration: Leverages Microsoft Fabric for streamlined analytics with tools like T-SQL, Power BI, and Spark. Managed Service: Simplifies maintenance and scaling, reducing administrative overhead. Top Use Cases for OneLake: Data Integration: Consolidating data from diverse sources into a single hub. Real-Time Analytics: Enabling faster insights with virtualized data access. Team Collaboration: Enhancing productivity by breaking down data silos. Choosing the Right Platform: ADLS vs. OneLake Feature ADLS Gen2 OneLake Purpose Flexible, scalable storage for big data Unified data lake for the entire organization Integration Deeply integrated with the Azure ecosystem Fully integrated with Microsoft Fabric Management User-managed, requiring setup and oversight Managed service with automated updates and scaling Instances Allows multiple instances per subscription Single instance per tenant for centralized governance Data Format Supports multiple formats Optimized for Delta Parquet format Shortcuts Not supported Supports shortcuts to external sources (e.g., ADLS, S3, Dataverse) Access Control Offers granular RBAC, ABAC, and ACLs for secure access Simplified access control with shared ownership governance Compatibility Compatible with Azure Blob Storage and many analytics services Natively supports Microsoft Fabric’s analytical engines like Power BI and T-SQL Scalability Scales with manual configuration Automatically scales with organizational demand Security Provides encryption at rest and in transit, with advanced access controls Security governed by default with distributed ownership Ease of Use Requires technical expertise for setup and maintenance User-friendly, with minimal setup for both technical and non-technical users Data Virtualization Limited virtualization options Supports data virtualization for querying external data without duplication Collaboration Collaboration is siloed, often requiring additional Azure tools Built for collaboration with enhanced sharing and access within Microsoft Fabric The Microsoft Fabric Advantage When paired with Microsoft Fabric, OneLake becomes an even more powerful tool. Fabric’s integration simplifies analytics workflows and enhances collaboration, allowing your team to focus on delivering actionable insights. With features like data virtualization and real-time analytics, Fabric and OneLake together create a secure, scalable, and collaborative data ecosystem. For teams looking to bridge the gap between technical and business users while accelerating their analytics journey, this combination offers a complete solution—one that’s ready to launch your data strategy into the stratosphere. Conclusion: Charting Your Data Path ADLS is ideal for data engineers managing large-scale analytics and machine learning workloads, offering unmatched scalability and customizability. On the other hand, OneLake, especially when paired with Microsoft Fabric, shines as a unified platform for organizations prioritizing ease of use, collaboration, and real-time analytics. No matter your data destination, understanding the capabilities of each platform ensures your team is equipped for success. Ready to take your data strategy to new heights? Choose the platform that aligns with your mission, and let the data-driven insights take flight. Get Looped In Still deciding between Azure Data Lake Storage and OneLake? Let us help you chart the right course for your data operations. Connect with one of our data experts to explore how these platforms—and Microsoft Fabric—can accelerate your insights and transform your data strategy. Get Looped In today.