Datadope

Observabilidad, analítica operacional y gestión de datos con una solución propia. Reconocida como Pyme Innovadora.

General Information
Company Name
Datadope
Founded Year
2016
Location (Offices)
Spain +2
Founders / Decision Makers
Number of Employees
53
Industries
Analytics, Information Technology, Machine Learning
Funding Stage
-
Social Media

Datadope - Company Profile

Datadope is a Spanish startup founded in 2016 that specializes in observability and intelligent operational analytics. The company's slogan "Observability, analítica operacional y gestión de datos con una solución propia. Reconocida como Pyme Innovadora." reflects its commitment to providing a complete solution for monitoring and data analysis, offering an end-to-end view of a company's infrastructure through to its business layer. Datadope sets itself apart by not only providing advanced open-source technologies but also incorporating machine learning and artificial intelligence to offer real-time observability and predict potential anomalies that may impact a company's bottom line and customer experience. By leveraging these technologies, Datadope aims to help companies increase their revenue by reducing downtime and enhancing strategic decision-making with detailed metrics. The startup positions itself as a partner in a company's digital transformation journey, emphasizing the crucial role this plays in today's business landscape. Operating within the Analytics, Information Technology, and Machine Learning industries, Datadope is located in Spain. Although there is no information available regarding the startup's last investment or its investors, it presents itself as an innovative, forward-thinking company with a strong focus on utilizing advanced technologies to drive business success.

Taxonomy: Observabilidad, Analítica operacional, Gestión de datos, Inteligencia artificial, Aprendizaje automático, Transformación digital, Open source, Monitoreo de datos, Análisis en tiempo real, Innovación tecnológica, Métricas detalladas, Experiencia del cliente, Infraestructura de datos, Predicción de anomalías

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