Great talk by Michael Stonebraker. Both interesting for executives as specialists. 00:20 Blunder #1: Not Planning to Move EVERYTHING to the Cloud 05:25 Blunder #2: Not Planning for AI/ML to be Disruptive 07:47 Blunder #3: Not Solving your REAL Data Science Problem 11:30 Blunder #4: Belief that Traditional Data Integration Techniques Will Solve Issue #3 20:30 Blunder #5: Belief that Data Warehouses will Solve all your Problems 22:10 Blunder #6: Belief that Hadoop/Spark will Solve all your Problems 26:00 Blunder #7: Belief that Data Lakes will Solve all your Problems 31:30 Blunder #8: Outsourcing your new stuff to Palantir, IBM, Mu Sigma... 33:55 Blunder #9: Succumbing to the "Innovator's Dilemma" 35:30 Blunder #10: Not Paying Up for a Few "Rocket Scientists" 36:05 Blunder #11 (Bonus): Working for a Company That is not Trying to do Something about the "Sins of the Past"
A thought provoking and candid presentation. I was wondering why are there so few likes or comments on this page? Would this would mean acknowledging our blunders? At a C-level do we do just enough to say that we have ticked off the box on having a Data Science team? Really, have we set up our Data Science team to fail? Is data really seen as a clear source of innovation with our organisations? Does it have the loudest voice when we are talking strategy? Taking on and trying to avoid these blunders really needs a singular focus, and I am not sure many organisations are structured around being data-led.
Great talk by Michael Stonebraker. Both interesting for executives as specialists.
00:20 Blunder #1: Not Planning to Move EVERYTHING to the Cloud
05:25 Blunder #2: Not Planning for AI/ML to be Disruptive
07:47 Blunder #3: Not Solving your REAL Data Science Problem
11:30 Blunder #4: Belief that Traditional Data Integration Techniques Will Solve Issue #3
20:30 Blunder #5: Belief that Data Warehouses will Solve all your Problems
22:10 Blunder #6: Belief that Hadoop/Spark will Solve all your Problems
26:00 Blunder #7: Belief that Data Lakes will Solve all your Problems
31:30 Blunder #8: Outsourcing your new stuff to Palantir, IBM, Mu Sigma...
33:55 Blunder #9: Succumbing to the "Innovator's Dilemma"
35:30 Blunder #10: Not Paying Up for a Few "Rocket Scientists"
36:05 Blunder #11 (Bonus): Working for a Company That is not Trying to do Something about the "Sins of the Past"
A thought provoking and candid presentation. I was wondering why are there so few likes or comments on this page?
Would this would mean acknowledging our blunders?
At a C-level do we do just enough to say that we have ticked off the box on having a Data Science team?
Really, have we set up our Data Science team to fail?
Is data really seen as a clear source of innovation with our organisations? Does it have the loudest voice when we are talking strategy?
Taking on and trying to avoid these blunders really needs a singular focus, and I am not sure many organisations are structured around being data-led.
Slams Citi in a talk sponsored by Citi at 35:32. Stonebraker is a legend.
Great talk !!!
Legend!