Fountain of Youth or Polluted Swamp: Is your data lake revitalizing your business or eroding the foundation?

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by Vincent Yates Director of Analytic Engineering at Zillow Group Fountain of Youth or Polluted Swamp: Is your data lake revitalizing your business or eroding the foundation? We’ve all been promised the shangri-la that is data lakes: more data means more insights—synergy! But has it really panned out? The trouble is that data lakes are more like the early days of the internet than they are a panacea of pristine useful information. Anyone can publish data, and even when they have the best of intentions, priorities shift, people leave and ultimately the priceless data become worthless. Those data may have been reliable when they were first published but are now wrong. Yet like many stale webpages, there is no way to tell, and the business continues to rely on those wrong data to make decisions. We at Zillow faced the same problem and decided to change it. I will describe the tools we’ve built and the tenants behind our team to help you ensure your lake rejuvenates your organization. Einstein said it best, “whoever is careless with the truth in small matters cannot be trusted with important matters.”

Tecnologia

11 ZILLOW | TRULIA | STREETEASY | HOTPADS | NAKED APARTMENTS
Vincent Yates, Director of Analytics Engineering
@VincentYates8
FOUNTAIN OF YOUTH OR POLLUTED SWAMP:
IS YOUR DATA LAKE REVITALIZING YOUR BUSINESS OR ERODING THE FOUNDATION?

2
One of these is worth $42,000 more
Finished sq-
ft 2,602 2,602
Lot Size 4,400 5,342
Bathrooms 3 3
Bedrooms 4 4
Year Built 2004 2005
Sale Price 861,000 819,000

3
One of these is worth $164,000 more
Finished sq-
ft 1,620 1,620
Lot Size 1,620 1,620
Bathrooms 2.5 3
Bedrooms 3 3
Year Built 2007 2007
Sale Price 499,000 663,000

4
One of these is worth >$10M annually
http://www.exp-platform.com/Pages/SevenRulesofThumbforWebSiteExperimenters.aspx

7
Unknowns ≠ Seasonality
Seasonality
Seasonality
Seasonality
Seasonalit
y
Seasonality
Seasonality

9
Product &
Communicatio
n
Programming
Statistics

1010
24% of data scientists
UNSURE OF HOW MUCH OF
THEIR DATA ARE
INACCURATE
IBM Big Data Hub

1313
66% of data scientists
CLEANING DATA IS THE MOST
TIME CONSUMING TASK
CroundFlower 2015 Data Science Report

1414
My data is pretty good.
DOES IT REALLY MATTER?

1515
52.3% of data scientists
POOR DATA QUALITY IS THEIR
BIGGEST HURDLE
CroundFlower 2015 Data Science Report

1616
The cost of poor data quality
15-25% OF OPERATING
PROFIT
Kaufman,Morgan: The Accuracy Dimension

1717
Someone would have noticed and fixed it
HOW DID WE GET HERE?

18
Cracks start to show under pressure
Data Quality: The Accuracy Dimension
The Morgan Kaufmann Series in Data Management Systems
OperationalIntegration Replication

19
Complexity/Agility is the scapegoat
Transaction
applications,
APIs, Third-
party data
producers
Transactio
n
databases
Data
Marts
Data Lake

20
Complexity/Agility is the scapegoat
Transaction
applications,
APIs, Third-
party data
producers
Transactio
n
databases
Data
Marts
Data Lake

21
Complexity/Agility is the scapegoat
Transaction
applications,
APIs, Third-
party data
producers
Transactio
n
databases
Data
Marts
Data Lake

24
Complexity/Agility is the scapegoat
Data
Marts
Data Lake

2626
HOW DO WE GET OUT?
A few simple tricks to head in the right direction

2727
PROACTIVE NOT REACTIVE
Data scientist is not great under duress

3131
MODELING IS HARD
Build tools to make reactive easier

35
Many mistakes are required for catastrophe
• Climate caused more icebergs
– Ignored Forecasts
• Tides sent icebergs southward
– Poor/Wrong Measurement
• The ship was going too fast
– Business needs over best data
• Iceberg warnings went unheeded
– Data was Disregarded for Intuition
• The binoculars were locked up
– Tools were behind lock and key
• The steersman took a wrong turn
– Reactive action under stress lead to wrong
decisions
• The iron rivets were too weak
– Cost savings over best data
• There were too few lifeboats
– Marketing owned the message
http://cosmiclog.nbcnews.com/_news/2012/04/01/10970732-10-causes-of-the-titanic-tragedy

3636
VincentYa@zillowgroup.com
@VincentYates8
THANK YOU!

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Fountain of Youth or Polluted Swamp: Is your data lake revitalizing your business or eroding the foundation?

1. 11 ZILLOW | TRULIA | STREETEASY | HOTPADS | NAKED APARTMENTS Vincent Yates, Director of Analytics Engineering @VincentYates8 FOUNTAIN OF YOUTH OR POLLUTED SWAMP: IS YOUR DATA LAKE REVITALIZING YOUR BUSINESS OR ERODING THE FOUNDATION?

2. 2 One of these is worth $42,000 more Finished sq- ft 2,602 2,602 Lot Size 4,400 5,342 Bathrooms 3 3 Bedrooms 4 4 Year Built 2004 2005 Sale Price 861,000 819,000

3. 3 One of these is worth $164,000 more Finished sq- ft 1,620 1,620 Lot Size 1,620 1,620 Bathrooms 2.5 3 Bedrooms 3 3 Year Built 2007 2007 Sale Price 499,000 663,000

4. 4 One of these is worth >$10M annually http://www.exp-platform.com/Pages/SevenRulesofThumbforWebSiteExperimenters.aspx

5. 55 DATA SCIENCE’S DIRTY LITTLE SECRET

6. 66 $3.1 TRILLION IBM Big Data Hub

7. 7 Unknowns ≠ Seasonality Seasonality Seasonality Seasonality Seasonalit y Seasonality Seasonality

8. 88 Seriously DATA SCIENCE IS HARD

9. 9 Product & Communicatio n Programming Statistics

10. 1010 24% of data scientists UNSURE OF HOW MUCH OF THEIR DATA ARE INACCURATE IBM Big Data Hub

11. 11 Errors Propagate in Dynamic Ways

12. 12

13. 1313 66% of data scientists CLEANING DATA IS THE MOST TIME CONSUMING TASK CroundFlower 2015 Data Science Report

14. 1414 My data is pretty good. DOES IT REALLY MATTER?

15. 1515 52.3% of data scientists POOR DATA QUALITY IS THEIR BIGGEST HURDLE CroundFlower 2015 Data Science Report

16. 1616 The cost of poor data quality 15-25% OF OPERATING PROFIT Kaufman,Morgan: The Accuracy Dimension

17. 1717 Someone would have noticed and fixed it HOW DID WE GET HERE?

18. 18 Cracks start to show under pressure Data Quality: The Accuracy Dimension The Morgan Kaufmann Series in Data Management Systems OperationalIntegration Replication

19. 19 Complexity/Agility is the scapegoat Transaction applications, APIs, Third- party data producers Transactio n databases Data Marts Data Lake

20. 20 Complexity/Agility is the scapegoat Transaction applications, APIs, Third- party data producers Transactio n databases Data Marts Data Lake

21. 21 Complexity/Agility is the scapegoat Transaction applications, APIs, Third- party data producers Transactio n databases Data Marts Data Lake

22. 22 Complexity/Agility is the scapegoat

23. 23 Complexity/Agility is the scapegoat

24. 24 Complexity/Agility is the scapegoat Data Marts Data Lake

25. 25 Moral Hazard is the culprit

26. 2626 HOW DO WE GET OUT? A few simple tricks to head in the right direction

27. 2727 PROACTIVE NOT REACTIVE Data scientist is not great under duress

28. 28 Get Back to Raw Data

29. 29 Centralize Definitions

30. 30 Model Where Possible

31. 3131 MODELING IS HARD Build tools to make reactive easier

32. 32

33. 33

34. 34 Data Problems are as Old as Data

35. 35 Many mistakes are required for catastrophe • Climate caused more icebergs – Ignored Forecasts • Tides sent icebergs southward – Poor/Wrong Measurement • The ship was going too fast – Business needs over best data • Iceberg warnings went unheeded – Data was Disregarded for Intuition • The binoculars were locked up – Tools were behind lock and key • The steersman took a wrong turn – Reactive action under stress lead to wrong decisions • The iron rivets were too weak – Cost savings over best data • There were too few lifeboats – Marketing owned the message http://cosmiclog.nbcnews.com/_news/2012/04/01/10970732-10-causes-of-the-titanic-tragedy

36. 3636 VincentYa@zillowgroup.com @VincentYates8 THANK YOU!

Notas do Editor

http://www.ibmbigdatahub.com/infographic/four-vs-big-data
Election Fake News NeXT Market Data
http://www.ibmbigdatahub.com/infographic/four-vs-big-data
https://whatsthebigdata.com/2015/02/11/being-a-data-scientist-in-2015-infographic/
https://whatsthebigdata.com/2015/02/11/being-a-data-scientist-in-2015-infographic/
https://books.google.com/books?id=x8ahL57VOtcC&lpg=PP1&dq=Olson%2C%20J.E.%2C%202003%2C%20Data%20quality%3A%20The%20accuracy%20dimension%2C%20Morgan%20Kaufmann%20publishers%2C%20Burlington&lr&pg=PA8#v=onepage&q&f=false
Restate numbers to public investors Manually fixing my one problem is much cheaper than fixing the root issue

Fountain of Youth or Polluted Swamp: Is your data lake revitalizing your business or eroding the foundation?

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Fountain of Youth or Polluted Swamp: Is your data lake revitalizing your business or eroding the foundation?

Notas do Editor