{"id":18899,"date":"2025-04-24T09:00:00","date_gmt":"2025-04-24T16:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/insidetrack\/blog\/?p=18899"},"modified":"2025-04-23T11:02:56","modified_gmt":"2025-04-23T18:02:56","slug":"top-five-ways-data-is-the-fuel-that-powers-it-at-microsoft","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/insidetrack\/blog\/top-five-ways-data-is-the-fuel-that-powers-it-at-microsoft\/","title":{"rendered":"Top five ways data is the fuel that powers IT at Microsoft"},"content":{"rendered":"\n
\"Microsoft<\/figure>\n\n\n\n

At Microsoft Digital, our mission is to transform the digital employee experience across devices, applications, and hybrid infrastructure.<\/p>\n\n\n\n

But what fuels that transformation?<\/p>\n\n\n\n

Data.<\/p>\n\n\n\n

Enterprise data, backed by a strong strategy and culture, powers the technologies that drive Microsoft forward.<\/p>\n\n\n\n

Let\u2019s explore some of the ways data is the fuel that powers IT at Microsoft. <\/p>\n\n\n\n

Modernizing our corporate functions<\/h2>\n\n\n\n

In the era of AI, data plays a crucial role in our effort to modernize corporate functions like Human Resources, Finance, and Corporate, External, and Legal Affairs (CELA). For instance, employees can use our Employee Self Service (ESS) agent<\/a> to retrieve information and insights in real time using natural language queries. In the past, these actions were either conducted manually or required an IT support ticket, both of which resulted in a negative user experience. Now, employees can use ESS to complete their tasks more efficiently and with the confidence that the data they\u2019re working with is secure.<\/p>\n\n\n\n

Another impact of the powerful combination of data and AI is being realized by HR generalists. The Microsoft HR Business Intelligence team manages an immense number of Power BI dashboards<\/a>. Instead of manually sifting through vast amounts of information to locate required data, our Microsoft Digital team has developed new capabilities that allow HR generalists to use natural language queries to find the Power BI dashboards they need in real time. These tools use generative AI to optimize the time employees spend searching for information, so they can focus on their strategic deliverables.<\/p>\n\n\n\n

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“AI is not just about algorithms\u2014it\u2019s about the data that powers them. A well-structured enterprise data strategy ensures that AI can drive automation, insights, and business transformation at scale. At Microsoft Digital, we\u2019re using data to redefine how employees work and innovate,” says Faisal Nasir, a principal architect in Microsoft Digital.<\/p>\n<\/blockquote>\n\n\n\n

In Microsoft Digital, we\u2019ve defined a data standard for our AI workloads that we refer to as \u201cAI-ready data.\u201d AI-ready data<\/a> is data that\u2019s available, complete, accurate, and high quality. AI-ready data allows tools like Microsoft Fabric for data management and Microsoft Purview for governance make the most of AI tools and machine learning for the company\u2019s corporate functions. By using data mesh<\/a>, a decentralized data architecture approach, data scientists and engineers build data products across organizational siloes, or workspaces, using the One Lake feature of Fabric, illuminating cross-domain insights. By using Purview\u2019s Shortcut feature, engineering teams can more easily request and receive approval for access to the information they need to manage their organization more effectively and efficiently.<\/p>\n\n\n\n

In the race to adopt machine learning and AI, organizations often focus on model sophistication while ignoring the foundation: the data. Without clean, labeled, contextualized, and well-governed data, even the most advanced algorithms will falter. AI-ready data isn\u2019t just nice to have\u2014it\u2019s the make-or-break factor in every successful AI initiative. Models trained on poor-quality data will amplify bias, hallucinate patterns, and make dangerously flawed predictions. Meanwhile, organizations that invest in high-quality, AI-ready data gain not just better models, but a long-term competitive edge. They build systems that learn faster, adapt better, and scale smarter. In short, AI-ready data isn\u2019t the backend. It is the strategy.<\/p>\n\n\n\n

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\u201cAI is only as smart as the data it\u2019s fed\u2014and if that data isn\u2019t AI-ready, you\u2019re not building intelligence, you\u2019re building illusions,\u201d says Patrice Pelland, a partner engineering manager in Microsoft Digital.<\/p>\n<\/blockquote>\n\n\n\n

{<\/em>Learn how we\u2019re transforming our data governance at Microsoft with Microsoft Purview and Microsoft Fabric.<\/em><\/a>}<\/em><\/em><\/strong><\/p>\n\n\n\n

Optimizing our employee experience<\/h2>\n\n\n\n

Microsoft Digital\u2019s user-centric, coherent design philosophy puts the user\u2014an employee or a guest\u2014at the heart of every decision and aligns all our facility\u2019s services\u2014physical and digital\u2014to the needs of people. We employ a data-driven approach to the employee experience, using AI to aid in decision-making, improving how Microsoft employees interact with technology, physical spaces, and other Microsoft resources.<\/p>\n\n\n\n

\"Nasir,
Faisal Nasir (left to right) Oana Nica, Damon Gray, Johnson Samuel, Naval Tripathi, and Patrice Pelland, with the help of AI, drive the data strategy for transforming the digital employee experience.<\/em><\/figcaption><\/figure>\n\n\n\n

A few examples of how we\u2019re using data and AI to improve the employee experience include:<\/p>\n\n\n\n

Commute optimization: <\/strong>AI is being used to predict the best routes and Connector buses for employees, improving their commute experience. Microsoft Digital observes frequency and travel patterns to predict and recommend optimal routes to the desired destination, thereby decreasing the amount of time spent in transit.<\/p>\n\n\n\n

Dining solutions:<\/strong> Microsoft Digital is actively working to enhance the dining experience through the integration of AI capabilities. We\u2019re enabling insights based on data related to dining popularity\u2014essentially, where and what do employees like to eat? By using AI and machine learning, we aim to offer a richer Copilot experience to users. For instance, we\u2019re developing features that analyze cafe station popularity over time, and calculate the average fulfillment times for orders at each station. With this data, employees can use Copilot to make informed decisions about where to dine based on real-time insights into station capacity, foot traffic, and average efficiency.<\/p>\n\n\n\n

Occupancy predictions: <\/strong>Predicting employee occupancy in on-campus facilities helps to optimize utility usage. By using AI and machine learning to understand footfall patterns, the team can adjust heating and cooling systems to save energy and improve efficiency.<\/p>\n\n\n\n

{<\/em>Learn more about our fresh approach to accessibility powered by inclusive design.<\/em><\/a>}<\/em><\/p>\n\n\n\n

Managing our network infrastructure<\/h2>\n\n\n\n

Managing Microsoft\u2019s network infrastructure effectively is crucial for maintaining productivity and collaboration. A data-driven approach can provide the necessary insights and tools to ensure a seamless connectivity experience, efficient diagnostics, performance and cost optimization, and proactive security management.<\/p>\n\n\n\n

Pillars of data transformation<\/h3>\n\n\n\n
\"A
Transform your data journey with the four pillars: Observability, Data platform, Data products and insights, and Data democratization.<\/em><\/figcaption><\/figure>\n\n\n\n

The Infrastructure and Engineering Services team (IES) in Microsoft Digital has a close partnership with our data engineering team to transform network infrastructure IT. A consolidated data lakehouse<\/a> delivers data on inventory, configurations, health, device hardware and software compliance, vulnerability analytics, and more. The data platform supports an ecosystem of data citizens (network engineers, site operators, and security engineers) to self-serve on monitoring, dashboards, and diagnostics. The same data lakehouse enables applications to build rich network visualizations, implement AIOps to automatically manage incidents, and deliver network dedicated AI agents.<\/p>\n\n\n\n

The IES data team also plays a crucial role as Customer Zero for Microsoft’s data analytics and governance products, like Fabric, Purview, and Copilot. Their contributions to testing and providing feedback are invaluable for the development and improvement of these products:<\/p>\n\n\n\n