Seeing at the Speed of Thought: Empowering Others Through Data Exploration

Transcript

1. Seeing at the speed of thought Empowering others through data

   exploration Greg Goltsov Senior Data Engineer @gregoltsov www.gregory.goltsov.info (will have link to slides)

2. Seeing at the speed of thought Empowering others through data

   exploration

3. Seeing at the speed of thought Empowering others through data

   exploration

4. Seeing at the speed of thought Empowering others through data

   exploration yourself

5. Seeing at the speed of thought Empowering others through data

   exploration yourself your team

6. Seeing at the speed of thought Empowering others through data

   exploration yourself your team your company

7. Touch Surgery Built marketing/sales dashboards for Fortune 10 companies Built

   educational dashboards for 4 of the top 10 world-rated medical universities All from scratch

8. Appear Here World’s biggest online marketplace for retail spaces Internal

   recommendation system Highly visual debug interface for non-tech people

9. Southern Cross Austereo Modernising the data pipeline Spearheading data-driven culture

   throughout the company Datasets covering 80% Australians weekly

10. BI/DW tools

11. BI/DW tools

12. Remove barriers Make feedback fast Remove yourself

13. Remove barriers

14. Remove barriers Catalogued datasets with one-line import in Python Messy

    dataset in PDFs

15. Remove barriers Dashboard with right filters, Excel export “Can you

    run a query?”

16. Remove barriers. Foster curiosity.

17. Make feedback fast

18. Make feedback fast Found a new trend via tinkering “Tomorrow

    I’ll see results of the batch job”

19. Make feedback fast “Check the dash in 15 mins” “I

    put your request into the backlog”

20. Make feedback fast. Let people tinker.

21. Remove yourself

22. Remove yourself Data pipeline + products Ad-hoc

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25. Remove yourself. Don’t stand in the way.

26. Remove barriers Make feedback fast Remove yourself

27. The goal is to turn data into information, and information

    into insight. – Carly Fiorina, former HP CEO

28. Insight Information Data

29. Insight Information Data Value ↑ Abundance

30. Insight Information Data Fraud Access pattern Logs

31. Insight Information Data Key influencers MOM trends Tweets

32. Ad-hoc queries Data pipeline Fast to develop Every query gets

    thrown away after Upfront investment Every integration builds foundations

33. Visualise your ETL. Augment your Data Warehouses with Data Lakes.

34. None

35. Extract Transform Load Sources Data Warehouse

36. Extract Transform Load Sources Data Warehouse Data Insight Time

37. Volume Variety Velocity "3D Data Management: Controlling Data Volume, Velocity

    and Variety”, Gartner Inc. 2001

38. Volume Variety Velocity "3D Data Management: Controlling Data Volume, Velocity

    and Variety”, Gartner Inc. 2001

39. Analysis of Unstructured Data: Applications of Text Analytics and Sentiment

    Mining ~80% of all data is unstructured

40. ~80% of your data is unstructured

41. http://www.ft.com/cms/s/0/de15414e-ebad-11e1-985a-00144feab49a.html#axzz2F3CM6G7g “Making sense of unstructured data isn’t about technology, it’s

    a business challenge”

42. Aberdeen Group research Don’t use unstructured data Use unstructured data

    Happy with the ability to share data 18% 60% Pleased with the accessibility 20% 50%

43. Volume Variety Velocity Machine learning "3D Data Management: Controlling Data

    Volume, Velocity and Variety”, Gartner Inc. 2001

44. Ingest quickly Real-time schema-on- read exploration Push vetted insights into

    DW/BI Example: Spark, AWS Athena, Microsoft’s PowerBI

45. Collect Store Process/ Analyse Sources Data Warehouse Data Insight Insight

    Time

46. Collect Store Process/ Analyse

47. Collect Store Process/ Analyse

48. Collect Store Process/ Analyse

49. None

50. Look at data. A lot.

51. Look at data. A lot. http:/ /www.forbes.com/sites/gilpress/2016/03/23/data- preparation-most-time-consuming-least-enjoyable- data-science-task-survey-says

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54. Scale computation and storage separately Go from non-trivial data to

    dashboard in minutes Spark is 20-100x faster than MapReduce Turnkey solution: www.databricks.com OSS: Apache Zeppelin on AWS EMR Spark

55. We made it! Now what?

56. We made it! Now what? Human scale.

57. AirBnB Scaling Tribal Knowledge

58. AirBnB Scaling Tribal Knowledge

59. AirBnB Scaling Tribal Knowledge

60. AirBnB Scaling Tribal Knowledge

61. AirBnB Scaling Tribal Knowledge

62. None

63. THANK YOU Speaker Name: Greg Goltsov Email: gregory@goltsov.info Organized by

    UNICOM Trainings & Seminars Pvt. Ltd.
 contact@unicomlearning.com http://www.unicomlearning.com/2017/Big_Data_Visualization_Summit_Sydney