Mohit Sharma, Program Manager, talks about how Microsoft IT has implemented predictive analytics built on Azure Machine Learning for the sales team—making the jobs of sellers, sales managers, and sales executives at Microsoft a bit easier. He discusses how our consolidated tools have reduced manual sales processes and provide business insights through Power BI and Cortana Intelligence Suite, resulting in accelerated pipeline management and more accurate sales forecasting.
Moving the company’s revenue reporting platform to Microsoft Azure is giving Core Services Engineering (formerly Microsoft IT) the opportunity to redesign the platform’s infrastructure and functionality. With the major components in Azure, we’ve already seen how Spark Streaming and Azure Data Factory have made dramatic improvements in the platform’s performance and scalability. As our journey to host this solution in Azure continues, we’re finding new ways to improve it with Azure capabilities.
At Microsoft IT, providing great user experiences for employees is more than just product improvement—it’s about enhancing end-to-end experiences that span products and services. Anisha Gautam, Senior Program Manager in Microsoft IT, discusses broad experiences like finding, meeting, collaborating, supporting, and communicating. She talks about how we’re applying data science—using machine learning and algorithms—to analyze user feedback and data from products and services.
To help keep Microsoft employees productive and efficient, Microsoft IT looks at broad user experiences that go beyond individual products and services—experiences such as finding information and support, and meeting, collaborating, and communicating with colleagues. To drive improvements to the Microsoft employee experience, we get insights from analyzing data and user feedback across products and services.
Core Services Engineering (formerly Microsoft IT) is enhancing user experiences for Microsoft employees—such as finding information and communicating and collaborating with coworkers. Using Azure Data Factory and Azure Data lake, we built a platform to capture instrumentation—which includes data from products and services like Skype for Business and Office 365. We analyze this data, along with user feedback, using data science, machine learning, and algorithms—key phrase extraction, deep semantic similarity, and sentiment analysis—for insights to help people be productive.
When the treasury team at Microsoft wanted to streamline the collection process for revenue transactions, Core Services Engineering (formerly Microsoft IT) created a solution built on Microsoft Azure Machine Learning to predict late payments. Using a third-party algorithm, XGBoost, we spotted trends in five years of historical payment data. Aligned with our mission of digital transformation, these insights join data, technology, processes, and people in new ways—helping the collections team to optimize operations by focusing on customers who are likely to pay late.