Search Interloop

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.

When thinking about setting up and managing Microsoft Fabric, you can either go it alone or you can work with experts like Interloop to deliver predictable results that drive real impact for your organization. INTERLOOP vs DO IT YOURSELF Fuel your organization’s growth by unlocking value from your data Interloop® takes care of the heavy lifting so you can focus on the data & insights that drive growth. Review the Top 5 Reasons to choose Interloop for your next Data, Analytics, or AI Project. Insights Automated™: Solution Accelerators Reliable and Efficient Data Solutions Interloop Insights offers a suite of automated solution accelerators that provide a kitted, reliable approach to accelerate the delivery of tailored outcomes for your organization. These solutions include Fast Dash, Smart Sync, Predict, Activate, and more. By leveraging these tools, you can streamline your data operations, enhance decision-making, and achieve your business goals more effectively. Learn More Leading The Way With Microsoft Fabric Unify Your Data & Analytics Microsoft Fabric offers a comprehensive suite of analytics tools that seamlessly integrate to transform your data into a competitive advantage. As a unified platform, it supports all your data needs, from storage to advanced analytics, and is designed to harness the power of AI. Schedule Walkthrough Mission Control: Your Data Command Center Accelerate Your Data Insights Interloop Mission Control is designed to deliver rapid value from your data. By simplifying the process of connecting, organizing, and analyzing your data, it ensures that you can make informed decisions quickly. With over 500+ connectors, you can integrate all your operational tools without writing a single line of code. Requst A Demo Providing Support and Expert Guidance Your Trusted Data Partner At Interloop, we pride ourselves on being more than just a service provider. Our team of data experts is dedicated to guiding you through every step of your data journey, from initial discovery to ongoing support. As a Microsoft Certified Partner, we bring the latest innovations and best practices to help you succeed. Get Started Supercharging Your Team with AI Copilots Boost Productivity and Innovation From AI-powered marketing tools that save time and increase lead generation to AI assistants that optimize operations, our solutions help small businesses tackle their biggest challenges. By integrating AI into your daily operations, you can unlock new efficiencies and drive growth. Talk To An Expert Explore the possibilities with Microsoft Fabric & Interloop Seeing is believing. Schedule time with an expert to see how a unified data platform can impact your business from day one - and why our team is the leader in helping you get there. Get Started

The act of retrieving or fetching data from a Connection. Ingestion The act of retrieving or fetching data from a Connection. Ready To Get Started? You're one small step from starting your data-driven journey. LOOP ME IN

A SQL-based API created by Sun Microsystems to enable Java applications to use SQL for database access. A JDBC Source is one that supports JDBC connectivity. The JDBC driver for that data source and the URL format is required to set up the connection. Java Database Connectivity (JDBC) A SQL-based API created by Sun Microsystems to enable Java applications to use SQL for database access. A JDBC Source is one that supports JDBC connectivity. The JDBC driver for that data source and the URL format is required to set up the connection. Ready To Get Started? You're one small step from starting your data-driven journey. LOOP ME IN