Industry Solutions in Microsoft Fabric
Microsoft Fabric offers industry-specific data solutions that provide a robust platform for data management, analytics, and decision-making. These data solutions address the unique challenges faced by different industries, enabling businesses to optimize operations, integrate data from different sources, and use rich analytics.
Retail data solutions in Microsoft Fabric helps in managing large volumes of data, integrating data from various sources, and providing real-time analytics for prompt decision-making. Retailers can use these solutions for inventory optimization, customer segmentation, sales forecasting, dynamic pricing, and fraud detection. The platform facilitates the unification, enrichment, and modeling of industry data, enabling retailers to generate insights faster and more efficiently.
Sustainability data solutions in Microsoft Fabric (preview) supports the ingestion, standardization, and analysis of Environmental, Social, and Governance (ESG) data. With prebuilt data pipelines and sustainability data models, organizations can compute ESG metrics, prepare datasets for advanced analytics, and meet various disclosure reporting requirements. This capability is crucial for companies aiming to enhance their sustainability efforts and reporting.
Healthcare data solutions in Microsoft Fabric (preview) are strategically designed to accelerate the time to value for customers by addressing the critical need to efficiently transform healthcare data into a suitable format for analysis. With these solutions, healthcare organizations can seamlessly conduct exploratory analysis and run large-scale analytics, unlocking the full potential of their data assets. By providing intuitive tools such as data pipelines and notebooks, users are empowered to navigate and process complex datasets with ease, overcoming the inherent challenges associated with unstructured data formats.
After healthcare data is transformed into a unified format, healthcare data explorer in Microsoft Fabric (preview) streamlines the process of analyzing and querying the data. It prepares the data for clinicians to build patient cohorts by using multimodal data for health trend studies, quality assessments, clinical trials, historical research, and AI development. Healthcare data explorer uses multimodal data sources such as medical images, structured clinical data, and unstructured clinical notes.