Dynamics Saturday Madrid 2019 - AI to improve productivity
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This document discusses using AI to improve productivity. It provides examples of the costs of distraction for workers and tools. It then poses questions about understanding patterns in employee behavior and usage. The rest of the document outlines Microsoft's Azure tools that can be used to ingest, analyze, and store different types of data from various sources. These include Azure Functions, Data Factory, Databricks, SQL Data Warehouse, Cosmos DB, and setting up operational databases. It provides demos of using Azure Functions, Data Factory, Databricks, and Cosmos DB for ingesting, analyzing, and storing data.
Dynamics Saturday Madrid 2019 - AI to improve productivity
- 1. - Mayo 2019 AI para mejorar la productividad • Adrian Diaz Cervera • Alberto Diaz Martin
- 5. La era de la distracción Coste de la distracción 50% de los trabajores no saben que esperan de ellos2 50% 59% de los vendedores dicen tener muchas herramientas de ventas1 59% Distracciones reducen el rendimiento de los vendedores un 14%1 1 Accenture 2 Gallup
- 6. ¿Por qué nuestros empleados no finalizan una venta? ¿Quienés son los empleados más insatisfechos? ¿Por qué los empleados no encuentran el contenido? ¿Qué está impactando en el ambiente de trabajo? Entender el POR QUÉ detrás del QUÉ Entender los patrones de mis empleados Descubrir comportamie ntos emergentes y tendencias Descubrir patrones inesperados que inspieren nuevas ideas
- 7. ¿Qué uso hacemos de Dynamics CRM? ¿Cuántas actividades tengo como Owner? ¿Cuántas actualizacione s de Cuentas hago? ¿Cuántas actualizacione s de Contactos hago? La adopción de Dynamics es la adecuada Detección de anomalías en el uso Rendimiento de uso vs Ventas Detección de patrones uso y comportamie nto
- 8. Log files Spatial & GPS coordinates Data market feeds eGov feeds Weather Text/image Web 2.0Advertising Mobile Collaboration eCommerce Web logs Digital Marketing Search Marketing Recommendations 8 Más “decision makers” BigData Megabytes Gigabytes Terabytes Petabytes Data Complexity: Velocity, Variety ERP/CRM Payables Payroll Inventory Contacts Deal Tracking Sales Pipeline Click streams Wikis, Blogs Sensors/RFID/ Devices Social Sentiment Audio/Video
- 9. ServeStore Prep and trainIngest Batch data Streaming data Azure Kubernetes service Power BI Azure analysis services Azure SQL data warehouse Cosmos DB, SQL DB Azure Data Lake Storage Ingest Data Events Azure Databricks Azure Machine Learning service Apps Model Serving Ad-hoc Analysis Operational Databases Sales Marketing Customer Service
- 11. © Microsoft Corporation INGEST with API REST
- 12. Modelo conceptual ServeStore Prep and trainIngest Batch data Streaming data Azure Kubernetes service Power BI Azure analysis services Azure SQL data warehouse Cosmos DB, SQL DB Azure Data Lake Storage Azure Databricks Azure Machine Learning service Apps Model Serving Ad-hoc Analysis Operational Databases Azure Functions Sales Marketing Customer Service Webhooks
- 13. Dynamics CRM API REST • OData 4.0 Web API • Use Common Data Service Web API • Use AdalJS for Authentication • Limit request x minutes
- 14. © Microsoft Corporation Serverless con Azure Function
- 15. © Microsoft Corporation Azure Functions model
- 16. © Microsoft Corporation DEMO INGEST WITH AZURE FUNCTIONS
- 17. © Microsoft Corporation INGEST with Azure Data Factory
- 18. ServeStore Prep and trainIngest Batch data Streaming data Azure Kubernetes service Power BI Azure analysis services Azure SQL data warehouse Cosmos DB, SQL DB Azure Data Lake Storage Azure Data Factory Events Azure Databricks Azure Machine Learning service Apps Model Serving Ad-hoc Analysis Operational Databases Sales Marketing Customer Service
- 19. Data Integration Service: Serverless, Scalable, Hybrid Hybrid Pipeline Model Seamlessly span: on prem, Azure, other clouds & SaaS Run on-demand, scheduled, data-availability or on event Data Movement @Scale Cloud & Hybrid w/ 75+ connectors provided Up to 1 GB/s SSIS Package Execution Lift existing SQL Server ETL to Azure Use existing tools (SSMS, SSDT) Author & Monitor Programmability w/ multi-language SDK Visual Tools Azure
- 21. © Microsoft Corporation DEMO INGEST WITH AZURE DATA FACTORY
- 22. © Microsoft Corporation Dataflow y Common Data Model
- 23. ServeStore Prep and trainIngest Batch data Streaming data Azure Kubernetes service Power BI Azure analysis services Azure SQL data warehouse Cosmos DB, SQL DB Azure Data Lake Storage Power BI Dataflow Events Azure Databricks Azure Machine Learning service Apps Model Serving Ad-hoc Analysis Operational Databases Sales Marketing Customer Service
- 24. © Microsoft Corporation Dataflow Azure Data Factory Azure Databricks Azure SQL DW Azure ML CDM folder
- 26. © Microsoft Corporation CDM Folder
- 27. © Microsoft Corporation DEMO INGEST WITH DATAFLOW
- 28. © Microsoft Corporation Analysis with Azure Databricks
- 29. ServeStore Prep and trainIngest Batch data Streaming data Azure Kubernetes service Power BI Azure analysis services Azure SQL data warehouse Cosmos DB, SQL DB Azure Data Lake Storage Ingest Data Events Azure Databricks Azure Machine Learning service Apps Model Serving Ad-hoc Analysis Operational Databases Sales Marketing Customer Service
- 30. Apache Spark • Unified, open source, parallel, data processing framework for Big Data Analytics Spark Core Engine Spark SQL Interactive Queries Yarn Mesos Standalone Scheduler Spark MLlib Machine Learning Spark Streaming Stream processing GraphX Graph Computation
- 31. Databricks within Azure Landscape DATABRICKS I/O SERVERLESS Cloud storage Data warehouses Hadoop storage IoT / streaming data Rest APIs Machine learning models BI tools Data exports Data warehouses Azure Databricks Enhance Productivity APACHE SPARK MULTI-STAGE PIPELINES DATA ENGINEER JOB SCHEDULER NOTIFICATION & LOGS DATA SCIENTIST BUSINESS ANALYST Build on secure & trusted cloud Scale without limits Optimized Databricks Runtime Engine Collaborative Workspace Deploy Production Jobs & Workflows
- 32. © Microsoft Corporation DEMO ANALYSIS WITH DATABRICKS
- 33. © Microsoft Corporation Operational Databases
- 34. ServeStore Prep and trainIngest Batch data Streaming data Azure Kubernetes service Power BI Azure analysis services Azure SQL data warehouse Cosmos DB, SQL DB Azure Data Lake Storage Ingest Data Events Azure Databricks Azure Machine Learning service Apps Model Serving Ad-hoc Analysis Operational Databases Sales Marketing Customer Service
- 35. MongoDBTable API Turnkey global distribution Elastic scale out of storage & throughput Guaranteed low latency at the 99th percentile Comprehensive SLAs Five well-defined consistency models Azure Cosmos DB DocumentColumn-family Key-value Graph Core (SQL) API
- 36. Importante el modelo de datos LastUserAdoptionScoring TeamAdoptionScoring UserAdoptionScoring
- 37. Importante el modelo de datos Data model thinking on query performs AI to generate de Data Differents data sources from Office 365, Dynamics 365, … LastUserAdoptionScoring TeamAdoptionScoring UserAdoptionScoring
- 38. © Microsoft Corporation DEMO COSMO DB
- 39. Take away • Extraer la información de Dynamics 365 • API REST • Azure Data Factory • Power BI Dataflow • Modelar el Data Lake con CDM • Crear un modelo de datos de análisis
- 40. ¡Muchas gracias!