Insights y novedades
While there is an infinite number of use cases, we present below compilation of interesting use cases structured into eight major categories applicable to a wide variety of industries. They have been selected based on the areas with the greatest impact on businesses and the activities within them that advanced analytics can optimize.
Clariba SEIDOR's act.in|extensions for Corporater help customers go beyond the capabilities of relying solely on the out-of-the-box Corporater BMP software. With our custom-built extension for forecasting, customers can visualize and forecast data more accurately using machine learning models in an easy-to-use format with stunning visuals right from within Corporater BMP.
Optimizing the customer experience with better resource allocation is often a critical objective for businesses or governmental agencies. The following case study concerns the Emergency Services Contact Center of a large European metropolitan city. Their objective was to accurately predict the volume of inbound calls to be able to improve agent rostering and ensure optimal response times for callers in serious need of assistance.
Prediction models are trending globally, and are now more widely used than ever before. If you’re looking to take advantage of this technology to better understand your data, don’t worry, our experts are right here to support you, whether you are an existing SAP HANA customer or you are planning to move to SAP HANA soon.
En estos días, el "machine learning" es una palabra comúnmente utilizada en una amplia gama de industrias. La necesidad de administrar, analizar e informar sobre conjuntos de datos a gran escala ya no se limita a las empresas tecnológicas: los sectores desde el comercio minorista hasta la manufactura y la atención médica dependen de los científicos de datos para dar sentido a los enormes volúmenes de datos recopilados en la ejecución de sus negocios e intentar usarlo para planificar el futuro, optimizar procesos y predecir riesgos y oportunidades.
La ingeniería de características es un paso crucial en el proceso de construcción en el desarrollo del aprendizaje de máquinas, ya que el algoritmo utilizará las características como predictores.
Por lo tanto, es aconsejable priorizar la creación y la optimización de nuestras características para asegurarnos de que comencemos con un modelo de datos robusto, lo que dará como resultado que nuestro modelo de aprendizaje automático obtenga buenos resultados.
