Author: ultroni1

Real-time analytics is the practice of processing, analyzing, and acting on data as it’s generated, typically within seconds to minutes of when events occur. Unlike traditional analytics that works with static snapshots of historical data stored in databases, real-time analytics operates on data that’s actively flowing through your systems, enabling immediate insights and rapid responses to changing conditions. This approach is also known as near real-time analytics, since there’s always some degree of processing and network latency involved.

Understand events and streams

Events are records of things that happen in a system. They capture moments when something occurs, changes, or is completed. Examples include website clicks, stock price changes, customer purchases, patient vital sign changes, or equipment sensor readings. Think of them as digital records or log entries that document activity across your systems.

A stream is essentially a sequence of events, typically ordered by the time an event occurred. Each event in the stream represents something that happened at a specific moment. Events flow through streams continuously as they occur. For example, a stream of equipment temperature sensor readings contains temperature readings over many points of time. This continuous flow of event information allows you to detect patterns over time, identify opportunities or risks, and take action immediately after something happens, or in real-time.

Streams are the delivery mechanism that carries events from where they happen to where they need to be processed, analyzed, or acted upon.

Components of real-time analytics solutions

To build real-time analytics solutions, you need several integrated capabilities working together:

Real-time data ingestion: Collect data from multiple sources simultaneously, as information is generated. For example: database changes from change data capture, sensors, applications, system logs, and APIs.

Stream processing: Transform and analyze data while it flows from sources to destinations. This includes filtering, aggregating, joining with other data sources, and detecting patterns with minimal latency.

Low-latency storage: Use specialized databases and storage systems designed to handle high-velocity data writes and provide fast query responses.

Interactive dashboards: Create visualizations that update automatically as new data arrives, show current state and trends in real-time.

Automated decision making: Set up event-driven rules and triggers that can initiate actions, send alerts, or start workflows based on real-time conditions.

Use real-time analytics

To use real-time data effectively, information has to be ingested, processed, stored, analyzed, and presented to be actionable. Real-time analytics enables you to:

• Respond immediately to opportunities or problems as they emerge
• Optimize operations by adjusting resources and configurations based on current conditions
• Enhance customer experiences through personalized, contextual interactions
• Prevent issues by detecting anomalies before they become critical problems

Real-Time Intelligence in Microsoft Fabric brings all these capabilities together in a single platform. Through components like Eventstreams for data ingestion and transformation, Eventhouses for analytics-optimized storage, the Real-Time hub for data discovery, Real-Time Dashboards for visualization, and Activator for automated alerts and actions, Real-Time Intelligence enables you to monitor critical events, trigger automated responses, track business processes, and analyze patterns in real-time, turning what happens in your systems into actionable insights.

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As technology advances, human and AI interaction grows more important. AI isn’t just for automation; it’s transforming industries, improving our lives, and sparking innovation. This video examines AI’s societal impact and the key considerations for its integration.

AI is changing industries by enabling data-driven decisions, automating processes, and fostering innovation. By following these guidelines, we can harness the power of AI responsibly and shape a future that aligns with our shared values and aspirations.

Data privacy: Balance the need for data with the protection of individual privacy rights.

Algorithmic bias: Detect and mitigate biases in AI systems to prevent societal biases from being reflected.

Transparency: Ensure AI decision-making processes are clear and understandable to foster trust and accountability.

Legal liability: Address responsibility for AI decisions, considering the roles of developers, users, and the AI itself.

Innovation and accountability: Striking a balance between innovation and accountability is essential for responsible AI usage.

Data sharing: Encourage data sharing while safeguarding privacy to improve AI systems without compromising individual rights.

AI research: Invest in AI research to fuel innovation and ensure the benefits of AI are accessible to all.

Digital education: Promote digital education and workforce development to equip people with the skills needed in an AI-transformed job market.

AI advisory committees: Establish AI advisory committees to provide oversight, insights, and guidance on AI development and deployment.

Government engagement: Engage with government officials to shape policies that affect AI use in communities.

These guidelines are designed to ensure the responsible and ethical use of AI, fostering a positive and fair influence on society.

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Deepfake technology added complexity to the rapid production and distribution of digital content. AI-generated deepfakes, which can mimic real people, pose serious ethical and legal issues, especially concerning copyright and intellectual property.

This video investigates deepfakes and their impacts, highlighting strategies to protect creators’ rights and verify digital content. It addresses the challenges of deepfakes, industry’s fight against misinformation, and emerging legal frameworks.

Creating a safer digital space involves enforcing safety protocols, updating regulations, and increasing public education. Being well-informed is crucial for managing AI-created content.

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It is essential to develop AI systems based on trustworthy, fair, and privacy-conscious principles. This video outlines six key principles: accountability, inclusiveness, reliability & safety, fairness, transparency, privacy & security. Discover how these principles foster trust and help create AI systems that respect individual rights and serve society.

The journey to responsible AI begins with trust, a trust that is built on these six principles:

Accountability: Define clear roles and responsibilities for AI impacts.

Inclusiveness: Ensuring AI benefits everyone and is accessible to all.

Reliability & Safety: Extensive testing, validation, ongoing monitoring, and safety protocols with robust error handling.

Fairness: Equitable treatment of all individuals, with regular assessments to prevent bias.

Transparency: Enable users to comprehend AI decisions in order to build trust.

Privacy & Security: Safeguard user data, collect only necessary information, and implement strong security protocols.

These principles aim to develop AI systems that benefit society while upholding individual rights and values.

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As AI becomes more integrated into our lives, it’s important to understand how to use it ethically and effectively.

In this video, you explore the best practices to follow while using AI.

Responsible collaboration with AI is essential. Here are some key best practices:

Understand AI: It’s important to grasp the basics of AI and its broad capabilities. This knowledge is the foundation for using AI effectively.

Stay informed: Keep up with the latest advancements and ethical discussions in AI. This will help you leverage AI responsibly.

Recognize AI’s blind spots: AI can reflect societal biases present in the data it learns from. Actively seek unbiased information and understand how AI uses data to navigate these blind spots.

Prioritize safety and privacy: Your data is precious. Choose AI services that value user privacy and prioritize security and transparency.

Cross-verify AI-generated content: Don’t accept AI-generated content at face value. Always cross-verify information from various sources and engage your critical thinking skills.

Evaluate and refine content: Use critical thinking to evaluate and refine AI-generated content by verifying facts and sources, understanding the content’s goals and target audience while considering a range of viewpoints.

Ensure clear policies: Make sure the AI tool or service you’re using has clear policies and guidelines for secure usage.

Promote AI for good: AI should be a tool for good, aiding in areas like healthcare, education, and environmental conservation.

Join the conversation: Start discussions in your community and workplace about responsible AI use. Encourage people to think about how AI will be utilized and take steps to prevent misuse.

These methods will enable you to use AI responsibly and contribute positively to society.

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Now let’s find inspiration for more complex prompts, and discover practical applications for AI at work.

Explore ways to use AI at work

Microsoft 365 Copilot Chat is a shared chat experience within Microsoft 365 Copilot, and is available at no additional cost with eligible Microsoft 365 and Office 365 licenses. With a Microsoft 365 Copilot license, Copilot Chat becomes more powerful by connecting to your work data across Microsoft 365 applications like Teams, Word, Outlook, PowerPoint, and Excel. This helps connect your questions with business information and apps, bringing out important details from the company’s data.

Some common examples include:

• Outlook: Summarize the content of a large email thread.
• PowerPoint: Turn a text-heavy slide into concise bullet points for greater clarity.
• Word: Rewrite a paragraph in a different tone or style.
• Teams: Summarize meetings and chat threads.

 Note

AI Agents, also known as virtual assistants, help you with different tasks based on what you need. They can handle routine tasks like scheduling meetings, managing emails, real-time data analysis, and personalized recommendations. In Microsoft 365, you can now build your own agents to fit your specific needs, making your daily tasks easier and more efficient.

To learn more, read Introducing Copilot agents and watch the short video Copilots & Agents.

The real power of Microsoft 365 Copilot comes from the flow of using it across apps. There are many use cases for Copilot in Microsoft 365, including in communications, HR, legal, IT, sales, project management, market research, finance, and more. The Copilot scenario library can help you get started.

The following table describes one sample functional scenario, Using Copilot to draft an internal communications post.

In the following short video, learn how one communications manager uses Microsoft 365 Copilot to create and enhance announcement posts.

Now that you learned about generative AI, how to interact with it, and some ways to take your prompts further, let’s practice prompting in Copilot.

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Now it’s time to dive into various resources that can spark your creativity and enhance your prompting skills in your personal life. In this unit, find inspiration for more complex prompts, and explore new ways to use AI in your daily life.

Explore ways to use AI in your personal life

Let’s take a closer look at a couple of ways you might use AI at home.

Plan a trip

Could you use some help or inspiration to plan a trip? Plan the Perfect Trip Itinerary Using Copilot suggests some prompts to get you started so you can spend less time creating itineraries and more time enjoying them. The following table suggests some good, better, and best prompts.

 Tip

Good prompts are often simple and direct, but leave room for interpretation. Better prompts add specific elements for clarity. The best prompts are clear, actionable, and structured for a specific response.

Train for a marathon

Say you’ve decided to run a marathon, or maybe your friends have talked you into participating in your first race. Whether you’re a seasoned marathon runner or this is your first race, Train for a Marathon with Copilot guides you through how to set running goals, make a training schedule, and get ready for race day using AI.

The following table suggests some good, better, and best prompts to set your running goals and make a training schedule.

Now that you learned about using AI in your personal life, let’s discover some ways to use AI at work.

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To get the best out of AI, you want to create prompts that help it give you useful responses. A prompt is simply a question or instruction you submit to get a specific response. It’s like asking a friend or coworker for help or information, but instead you’re asking an AI, like Microsoft Copilot. The clearer and more detailed your prompt, the better Copilot can understand and respond. It’s often more effective to tell Copilot what you want it to do, rather than what you don’t want it to do.

Write effective prompts

An effective prompt should be clear, specific, contextual, and goal oriented. By incorporating these elements into your prompts, you can ensure that you get more accurate and relevant responses from AI tools. Remember, the more information and guidance you provide in your prompt, the better Copilot can assist you.

A prompt can be simple or detailed, but you must have a clear goal. If you want to be more specific, add additional information. Often, you need more than just a goal to achieve the results you want. The following video describes the four elements of a good prompt: Goal, Context, Sources, and Expectations.

Here are some tips to keep in mind when writing a prompt:

• Be specific about what you want Copilot to do. Clear goals lead to better responses.
• Add some context to help Copilot understand what you’re asking. Context makes the response more relevant.
• Provide some data or information for Copilot to use. This helps ground the response in the right context.
• Let Copilot know how you want the response to be formatted. This sets clear expectations.

Explore good, better, and best prompts

The following table includes some everyday tasks that you could co-create with AI, and include examples of good, better, and best prompts. These examples help you understand how to create prompts to make your tasks more simple and effective.

 Tip

When you see text in brackets within a prompt, it indicates where you should either paste your own content or replace the bracketed text with your own ideas. After personalizing the prompt, remove the brackets. For example, you might replace [my workout] with morning run and [upbeat and energetic] with relaxing and calm to make this prompt your own.

The following image shows the playlist Copilot generated using the “best” prompt example. Note that it’s formatted as a table and includes all of the requested details for each song.

 Note

One of the best ways to effectively work with Copilot is to ask it to refine the results. When you give a prompt to an AI, the first response might not be exactly what you want. So, look at the result, think about what you want to change, and then try again with a slightly different prompt. Each time you make a change, you’re getting closer to the perfect result. This is called iterating.

For example, let’s say you used Copilot to create a menu for a dinner party, but you forgot you need to include vegetarian options. All you need to do is ask Copilot to update the menu to include some vegetarian dishes. This way, you can keep refining your prompts until you get the perfect result.

Find inspiration

The following table includes some prompt resources you might find helpful to get started with more complex prompting.

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AI can simplify everyday tasks and enhance productivity. This unit introduces the basics of AI, including how generative AI creates new content, and how tools like Microsoft Copilot can enhance productivity. You also explore the importance of using AI responsibly to ensure fairness, transparency, and trust.

What is AI, and how does it work?

Generative AI focuses on creating new, unique content, based on the input you provide. This input is called prompting, which just means asking AI for specific things. Generative AI can even produce creative content, such as writing poems, composing melodies, or designing graphics, based on the patterns and styles it learned from existing data.

People usually interact with generative AI built into a chat application. One example of such an application is Microsoft Copilot, an AI-powered productivity tool designed to enhance your work experience by providing real-time intelligence and assistance. In other words, it’s a smart tool that helps you work better by giving you quick answers and help when you need it.

 Note

Behind the scenes

Generative AI uses large language models (LLMs), which are AI systems trained on massive amounts of text, to understand and generate human-like language. They are like a very intelligent autocomplete. LLMs are a type of AI model that uses natural language processing (NLP) techniques to work.

NLP is a broader technology that helps computers understand and use human language to make things like chatbots, voice assistants, and translations work smoothly. LLMs are one powerful example of NLP in action.

Help with everyday tasks

Copilot is a smart assistant that can simplify your daily routines by helping you with various tasks and make your life a bit easier and more organized. Whether you’re looking to get creative, plan your day, or need some personal assistance, AI can make your life more manageable. Let’s look at some everyday examples of how you might use AI.

Write creative content

Struggling to start a project or come up with new ideas? Whether you’re drafting an email, writing a blog post, or brainstorming for a podcast, AI can give you a starting point. It offers suggestions, generates text, and can even create entire pieces of content. For example, Copilot can help you:

• Compose emails. Quickly draft professional or personal emails by inputting the main message and letting AI create a draft.
• Generate blog posts. Create engaging blog posts by providing a topic and key points, and AI generates a well-structured article.
• Brainstorm ideas. Get creative ideas for your next project, whether it’s a slogan, a story, or a social media post.

By using AI for creative writing and content creation, you can enhance your productivity and ensure that your materials are engaging and effective.

Organize and plan

AI can help you stay organized and plan your activities by providing personalized recommendations and streamlining your planning process. For example, Copilot can help you:

• Plan a trip. AI can help you organize your travel itinerary, suggest destinations, flights, and accommodations, and even recommend activities based on your preferences.
• Manage and organize your calendar. AI can help you schedule appointments, set reminders, and organize your daily tasks to ensure you stay on track.
• Plan an event. Whether you’re organizing a business conference or a family gathering, AI can help you plan the event by suggesting venues, creating schedules, and managing guest lists. It can also provide reminders and updates to keep everything on track.
• Manage tasks. AI can help you manage your to-do list by prioritizing tasks, setting deadlines, and providing reminders. It can also suggest ways to break down larger projects into manageable steps.

By using AI for organizing and planning, you can save time, reduce stress, and ensure that everything runs smoothly.

Get personal assistance

Copilot can act as your personal assistant and help you manage your life. Whether you need reminders, information, or assistance with tasks, AI is there to support you. For example, Copilot can help you:

• Answer questions. Get quick answers to your questions, whether it’s finding information online, checking the weather, or getting directions.
• Provide recommendations. Receive personalized recommendations for movies, books, restaurants, and more based on your preferences and past choices.
• Automate tasks. Automate routine tasks such as setting up meetings, sending follow-up emails, or managing your to-do list.

With AI as your personal assistant, you can streamline your daily activities, stay organized, and focus on what matters most to you.

Responsible AI

AI should treat everyone fairly, protect privacy, and operate transparently. This helps build trust and ensures that AI systems are used in ways that respect individuals’ rights and interests. In this short video, learn about some of the challenges and solutions related to AI accuracy.

As AI becomes more integrated into our daily lives, it’s important to use it responsibly. Here are some ways you can help keep your AI-generated content fair, trustworthy, and beneficial to society:

• Provide clear and specific prompts. Avoid sensitive topics and always review and validate outputs for accuracy and relevance.
• Watch for common biases:
  • Gender bias. AI systems can perpetuate gender stereotypes if trained on biased data. For example, a hiring algorithm might favor male candidates over female candidates due to historical data biases.
  • Racial bias. AI systems can exhibit racial bias, leading to discriminatory outcomes. For instance, facial recognition systems might have higher error rates for people of color compared to white individuals.
  • Socioeconomic bias. AI systems can favor individuals from higher socioeconomic backgrounds. A credit scoring algorithm, for example, might disproportionately favor applicants from wealthier neighborhoods.

Now that you have an idea of what generative AI is, how it can enhance our productivity and creativity, and the ethical considerations of using it, let’s explore how to interact with AI through prompting.

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Raw streaming data rarely arrives in the exact format needed for analysis or action. Transformations allow you to clean, enrich, and reshape data before routing it to destinations, ensuring each endpoint receives data optimized for its specific purpose.

Common transformation scenarios include:

• Data quality: Filter out invalid or incomplete data before processing
• Content-based routing: Route different data subsets to appropriate destinations based on the actual data values or content
• Data enrichment: Add calculated fields, rename columns for clarity, or convert data types for downstream compatibility
• Aggregation and summarization: Calculate running totals, averages, or counts over time windows for dashboard displays
• Format standardization: Ensure consistent data structure across multiple data sources before combining streams

Transform event data

The eventstream canvas gives you a way to create event data processing workflows. Eventstream provide several no-code transformations that you can drag onto the canvas:

• Filter: Filter events based on the value of a field in the input. Keep only events that meet specific conditions. For example: temperature > 80°, status = “error”, customer type = “premium”.
• Manage fields: This transformation allows you to add, remove, change data type, or rename fields coming in from an input or another transformation. Add calculated fields, remove unnecessary columns, rename fields, or change data types to match destination requirements.
• Aggregate: Use the aggregate transformation to calculate an aggregation (Sum, Minimum, Maximum, or Average) every time a new event occurs over a period of time. This operation also lets you rename calculated columns, and filter the aggregation based on other dimensions in your data. You can have one or more aggregations in the same transformation.
• Group by: Calculate aggregations across events within time windows, for example, hourly sales totals, or daily temperature averages. This transformation supports various time windows including tumbling windows (fixed intervals) and sliding windows (overlapping intervals).
• Union: Use the union transformation to connect two or more nodes in the event canvas and add events with shared fields (with the same name and data type) into one table. Fields that don’t match are dropped and not included in the output.
• Join: Combine data from two streams based on a matching condition between them.
• Expand: Use this array transformation to create a new row for each value within an array.

Create transformation workflows

Transformations can be used together to create data processing pipelines. For example, if you had a stream of equipment temperature readings, you could start by using filter to remove sensor errors from incoming IoT data. Next, you might use manage fields to add a calculated “priority” column based on temperature thresholds. Then group by could calculate hourly averages by location. Finally, you’d route the processed data to appropriate destinations: temperature data to Fabric Activator for rule evaluation and hourly summaries to a Lakehouse for historical analysis.

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