El Big Data Analytics donde menos te lo esperas

Transcript

1. El Big Data Analytics donde menos te lo esperas
2. Hola! Soy Alex Rayón Director Deusto BigData (www.bigdata.deusto.es) Me puedes encontrar como @alrayon, en www.alexrayon.es y alex.rayon@deusto.es
3. BIG DATA Agosto 24 y 25 | Lima – Perú 2018 ANALYTICS SUMMIT #BIGDATASUMMIT2018
4. ÍNDICE DE CONTENIDOS El Big Data Analytics donde menos te lo esperas Big Data en el Ocio #BIGDATASUMMIT2018 Aplicación en la agricultura: Mejora en la producción de Beterraga
5. EN LA MÚSICA Introducción
6. EN LA MÚSICA Introducción
7. EN LA MÚSICA Introducción
8. EN LA MÚSICA Introducción
9. EN LA MÚSICA Introducción
10. EN LA MÚSICA Introducción
11. EN LA MÚSICA Introducción
12. EN LA MÚSICA Introducción
13. EN LA MÚSICA Explorando datos
14. EN LA MÚSICA Explorando datos (II)
15. EN LA MÚSICA Explorando datos (III)
16. EN LA MÚSICA Clustering
17. EN LA MÚSICA Reglas de Asociación
18. EN LA MÚSICA Reglas de Asociación (II)
19. EN LA MÚSICA Predictivo
20. EN LA MÚSICA Predictivo (II)
21. EN LA MÚSICA Analizamos Facebook
22. EN LA MÚSICA Analizamos Facebook (II) ¿Qué se dice con la palabra “bilbao”...?
23. EN LA MÚSICA Analizamos Facebook (II)
24. EN LA MÚSICA ¿Hacemos escucha digital?
25. #BIGDATASUMMIT2018 Un mejor azúcar
26. #BIGDATASUMMIT2018 Beterraga
27. #BIGDATASUMMIT2018
28. #BIGDATASUMMIT2018
29. AGRICULTURA ¿Para qué? (II))
30. AGRICULTURA ¿Para qué? (III))
31. AGRICULTURA ¿Tabla de Contenidos 1. Why? Project’s use cases 2. How? Descriptive and predictive models 3. What for? Conclusions for stakeholders
32. AGRICULTURA ¿Tabla de Contenidos 1. Why? Project’s use cases 2. How? Descriptive and predictive models 3. What for? Conclusions for stakeholders
33. AGRICULTURA 1. Why? Project’s use cases - Both business and instrumental Business focuses Use Cases 1. What variables affect the performance and quality of beet? 2. A cost analysis to determine how to optimize the profitability obtained 3. Multivariable characterization of grower: with what profiles of farmers do we work? 4. What makes a grower change to a sowing alternative? Instrumental Use Cases 5. Organize and centralize data and information to be able to activate it in data analysis processes 6. Data Quality: identify information that we do not have well documented to date to reinforce and enhance future campaigns
34. AGRICULTURA 1. Why? Project’s use cases - Data pre-processing tasks ✓ 4 Data sources ✓ 205 Data sets ✓ 16 Field notebooks ✓ 22.249 Grower contracts ✓ 5.231 Variables ✓ 524.491 Observations
35. AGRICULTURA 1. The project description (First logical data model)
36. AGRICULTURA 1. Why? Project’s use cases - (Current logical data model)
37. AGRICULTURA ¿Tabla de Contenidos 1. Why? Project’s use cases 2. How? Descriptive and predictive models 3. What for? Conclusions for stakeholders
38. AGRICULTURA What happened? ➢ Currently, we are able to analyze in one shot all the grower´s performance in order to implant the best strategy minimizing errors. ➢ At the same time, we´ll find hidden trends so we will anticipate future problems that may happen . 2.1. Descriptive models
39. AGRICULTURA What will happen and why happened? ➢ There are variables we can't manage, but we can do it in some others, specially if we know the importance of them. ➢ We'll make a “tailor-made” advice report for each grower. We´ll improve productivity or quality areas of the grower´s performance 2.1. Descriptive models
40. AGRICULTURA 2.3. Business cases Business focuses Use Cases 2.3.1. What variables affect the performance and quality of beet? 2.3.2. A cost analysis to determine how to optimize the profitability obtained 2.3.3. Multivariable characterization of growers: with what profiles of farmers do we work? 2.3.4. What makes a grower change to a sowing alternative?
41. AGRICULTURA 1. What variables affect the performance and quality of beet? 2.3.1. Business Use Case: Performance and Quality of Beet
42. AGRICULTURA 1. What variables affect the performance and quality of beet? 2.3.1. Business Use Case: Performance and Quality of Beet
43. AGRICULTURA 2.3.1. Business Use Case: Performance and Quality of Beet 1. What variables affect the performance and quality of beet? Recommendation: Search within the variables that can be managed those that improve the performance and quality of each grower. For example: number of fungicide treatments in the case of performance.
44. AGRICULTURA 2. A cost analysis to determine how to optimize the profitability obtained 2.3.2. Business Use Case: Cost Analysis
45. AGRICULTURA 2. A cost analysis to determine how to optimize the profitability obtained 2.3.2. Business Use Case: Cost Analysis
46. AGRICULTURA 2.3.2. Business Use Case: Cost Analysis 2. A cost analysis to determine how to optimize the profitability obtained Recommendation: Study those growers whose irrigation costs are lower to transfer knowledge to those growers whose irrigation costs are higher
47. AGRICULTURA 2.3.3. Cost benefit analysis: Grower´s benchmark
48. AGRICULTURA 2.3.3. Business Use Case: Growers characterization 3. Multivariable characterization of growers: with what profiles of growers do we work?
49. AGRICULTURA 2.3.3. Business Use Case: Growers characterization 3. Multivariable characterization of growers: with what profiles of growers do we work? Recommendation: Continue to obtain data and new variables to define groups of growers with equal characterization in order to advise them on common problems and best practices.
50. AGRICULTURA 2.3.4. Business Use Case: Farmers’ churn prevention 4. What makes a farmer change to a sowing alternative?
51. AGRICULTURA 2.3.4. Business Use Case: Farmers’ churn prevention 4. What makes a farmer change to a sowing alternative? Recommendation: Continue contributing new data sources such as news in digital media and its impact on social networks, keywords ... etc. to look for relationship patterns.
52. AGRICULTURA ¿Tabla de Contenidos 1. Why? Project’s use cases 2. How? Descriptive and predictive models 3. What for? Conclusions for stakeholders
53. AGRICULTURA 3. What for? Final Conclusions ➢ This company is now prepared to start managing the long distance data race. ➢ This is a never ended way as this is a iterative project. Actually, the more data the models ingest the more strong the results are. ➢ The success of the project begins at the data collection point. It is necessary to coordinate this stage in the near future to obtain more detail in the data as well as more fluency. ➢ The next challenges will be to upload all the data to the private cloud, the data ingest automation and start to define new business cases.
54. GRACIAS! ALGUNA PREGUNTA? Puedes encontrarme como: @alrayon www.alexrayon.es alex.rayon.jerez@gmail.com
55. El Big Data Analytics donde menos te lo esperas