Big Data Berlin (28 August 2014)

On why tools are so often discussed when organizational culture seems to be the real problem.

Transcript

1. Data Problems: Tools vs. Culture Adam Drake 2014-08-28
2. Introduction This talk is for a general audience
3. Introduction This talk is for a general audience There will be no math, algorithms, or heavy tech parts
4. Introduction This talk is for a general audience There will be no math, algorithms, or heavy tech parts Background
5. Current Events What is ‘Big Data’ anyway?
6. Data Problems Most are not ‘Big Data’ (whatever that means) problems
7. Data Problems Most are not ‘Big Data’ (whatever that means) problems Most are not problems for lack of a tool
8. Data Problems Most are not ‘Big Data’ (whatever that means) problems Most are not problems for lack of a tool Most are culture problems
9. Culture Organizational Behavior and meanings attached to behaviors Role of Data Science
10. Culture Organizational Behavior and meanings attached to behaviors Role of Data Science The primary job of a Data Scientist is to help change the culture
11. Mission of a Data Scientist
12. Short-term Help the organization identify the right problems
13. Short-term Help the organization identify the right problems Do what is necessary to solve the problems
14. Short-term Help the organization identify the right problems Do what is necessary to solve the problems Evaluate the solution
15. Short-term Help the organization identify the right problems Do what is necessary to solve the problems Evaluate the solution Repeat
16. Long-term Help people become better at identifying and solving their own problems
17. What was missing? Hadoop
18. What was missing? Hadoop Storm
19. What was missing? Hadoop Storm Spark
20. What was missing? Hadoop Storm Spark Big Data
21. What was missing? Hadoop Storm Spark Big Data Data Lake
22. What was missing? Hadoop Storm Spark Big Data Data Lake HBase
23. What was missing? Hadoop Storm Spark Big Data Data Lake HBase Cassandra
24. What was missing? Hadoop Storm Spark Big Data Data Lake HBase Cassandra Kafka
25. What was missing? Hadoop Storm Spark Big Data Data Lake HBase Cassandra Kafka Flume
26. What was missing? Hadoop Storm Spark Big Data Data Lake HBase Cassandra Kafka Flume Lambda Architecture
27. What was missing? Hadoop Storm Spark Big Data Data Lake HBase Cassandra Kafka Flume Lambda Architecture d3.js
28. What was missing? Hadoop Storm Spark Big Data Data Lake HBase Cassandra Kafka Flume Lambda Architecture d3.js Tableau
29. What was missing? Hadoop Storm Spark Big Data Data Lake HBase Cassandra Kafka Flume Lambda Architecture d3.js Tableau HDFS
30. What is the problem?
31. Why focus on tools? Concrete Sad possibility
32. Why focus on tools? Concrete Quantifiable Sad possibility
33. Why focus on tools? Concrete Quantifiable Financial Sad possibility
34. Why focus on tools? Concrete Quantifiable Financial Unsure of real problem Sad possibility
35. Why focus on tools? Concrete Quantifiable Financial Unsure of real problem Sad possibility Too weak, or unmotivated, to fix the real problem
36. Now what? Stop talking about tools as solutions
37. Now what? Stop talking about tools as solutions Start/continue asking questions
38. Now what? Stop talking about tools as solutions Start/continue asking questions Be Socratic: use questions to teach people how to ask the right questions
39. Now what? Stop talking about tools as solutions Start/continue asking questions Be Socratic: use questions to teach people how to ask the right questions Lead by example
40. Questions? Twitter: @aadrake
41. Questions? Twitter: @aadrake LinkedIn: http://de.linkedin.com/in/aadrake
42. Questions? Twitter: @aadrake LinkedIn: http://de.linkedin.com/in/aadrake Blog: http://aadrake.com