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.
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, 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.
At Microsoft, MS Sales is our standard revenue reporting system—it’s critical to data analysis, strategic management, and financial decisions. Microsoft IT plans to migrate its on-premises infrastructure to Azure to meet challenges of scale, agility, and complexity. With that, we’re evaluating the system’s design and function for success in the cloud. Our migration strategy involves a variety of approaches—including the application of virtual machines, microservice components, and big data processing.
Microsoft SQL Server R Services, a new R-based analytics feature in SQL Server 2016, helps Microsoft IT manage our supply chain with insights that drive more efficient manufacturing operations and better customer service. We’ve compiled a few top scenarios for R Services in supply chain management—including linear programming to predict best outcomes, text analysis of customer feedback, machine learning to create predictive maintenance models, and association rule mining to refine shipping processes.