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.
Today, companies generate vast amounts of data—and it’s critical to have a strategy to handle it. To automate common data management tasks, Microsoft IT created a solution based on Azure Data Factory. The service, Data Lifecycle Management, makes frequently accessed data available and archives or purges other data according to retention policies. Teams across the company use the service to reduce storage costs, improve app performance, and comply with data retention policies.
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.
The revenue reporting platform at Microsoft must be fast, accurate, and reliable, yet handle increasingly complex transaction data. To improve performance while keeping costs flat, Microsoft IT worked with the Azure Customer Advisory Team and upgraded the platform to SQL Server 2016—a step toward moving to Microsoft Azure. We saw immediate performance improvements for critical processes and reporting queries. These gains enable high availability of services and seamless connection to data for faster decisions.