Why “Where” is a Must Have for Data Science - ERP Spain.com - España y América Latina

Empowering Data Scientists with Geospatial Data at Scale - Why "Where" is a Must Have for Data Science In the first of our "Empowering Data Scientists" mini-series, we begin by exploring how to leverage location-based information to bring data together for use in analytical models. Finding and munging geospatial data is tricky and more challenging than a simple Python or notebook call while quality data is also important to avoid inaccurate results. Knowing how to source and incorporate valuable location information is critical to deliver increased business insight while keeping project costs down. When thinking about the complexities of using geospatial data, it can be daunting to know where to begin. Find out what your organisation needs to know to tap the potential of geo-enrichment and geospatial data. Topics will include: Best practices for getting started with geo-enrichment Geo-enrichment key concepts and understanding large-scale geospatial data processing Strategies for integrating business address data into data science projects with Spark and Kubernetes Using metadata quality indicators to inform processes with business insight A live demonstration of Spectrum Spatial for Big Data in a cloud native environment