With Hadoop entering the mainstream, can -- and should -- it benefit from best practices from the world of Data Warehousing. Should the same ground rules developed for capacity-constrained internal enterprise DWs apply to Hadoop data stores designed for scale out, or for harvesting data from the Internet? We will pinpoint 3 key areas: data quality, privacy & confidentiality, and lifecycle management, addressing issues such as: 1. Does it make sense to apply traditional data cleansing practices to Hadoop data? Or will removing "errors" remove the possibility for discovering new insights? 2. Do different standards for privacy protection apply when harvesting sources such as social media that are already public? Should enterprises track their customers on Facebook or Twitter? 3. Will Hadoop make conventional data archiving practices obsolete? Is it cost effective to "move" petabytes of data offline? Just because the Googles & Yahoos of the world retain all their data, should mainstream enterprises? Should Hadoop be considered the new tape?

1. Hadoop:
Do Data Warehousing rules apply?
Tony Baer
tony.baer@ovum.com
June 14, 2012
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2. Agenda
§ Challenges traditional data stewardship practice
§ Privacy – is all the world a stage?
§ Limits to data lifecycle?
§ Data quality: the big, the bad, the ugly – and it all might be good!
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3. Data stewardship challenges –
What s old is new
Remember?
§ Back to undifferentiated gobblobs of data
§ Programmatic access reigns
§ File systems, not (always) tables 10.102.8.152 - - [05/Nov/2003:00:19:54 -0500] "GET /
inventory/index.jsp HTTP/1.1" 200 4028 "http://
www.mycompany.com/index.jsp" "Mozilla/4.08 [en] (Win98;
I ;Nav)"
§ Batch is back 192.168.114.201, -, 03/20/01, 7:55:20, W3SVC2, SALES1,
172.21.13.45, 4502, 163, 3223, 200, 0, GET,/DeptLogo.gif,
-, 172.16.255.255, anonymous, 03/20/01, 23:58:11,
MSFTPSVC, SALES1, 172.16.255.255, 60, 275, 0, 0,
But… if index(tempvalue,'?') then tempvalue=scan
(tempvalue,1,'?');
else if index(tempvalue,'&')>1 then
tempvalue=scan(tempvalue,1,'&');
§ Volume, variety, velocity, and where s the
value??
§ Just because you can, should you?
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4. Data stewardship questions for Big Data
§ Can we, should we control this data?
§ Are there limits to how much we should know?
§ Can we just keep piling up data forever?
§ Can we cleanse terabytes of data?
§ Do we still need good data?
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5. Use of repeated table of contents page
§ Challenges traditional data stewardship practice
§ Privacy – is all the world a stage?
§ Limits to data lifecycle?
§ Data quality: the big, the bad, the ugly – and it all might be good!
5 © Copyright Ovum. All rights reserved. Ovum is an Informa business.

6. Privacy –
the more things change…
You have zero privacy
anyway…. Get over it
-- Scott McNealy, 1999
Facebook does not actually
delete images… but instead
merely removes the links – a fix
is in sight
-- ZDNet, 2/6/12
Facebook agrees to 20 years of
federal privacy audits
-- NY Times, 11/29/11
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7. What privacy?
Florida made $63m last
year by selling DMV
information (name, date
of birth, type of vehicle
driven) to companies like
LexusNexus & Shadow
Soft.
-- Terence Craig & Mary Ludloff
Privacy and Big Data
(O’Reilly Media, 2011)
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8. Big Data privacy 101 –
Don t be creepy
§ Governance problem first, How Companies Learn Your
technology second Secrets
§ Understand the relationship
with your customers & business
partners
§ Keep communications in
context
§ Don t catch your customers by My daughter got this in the mail! he
surprise said. She s still in high school, and
you re sending her coupons for baby
clothes and cribs? Are you trying to
§ The law still trying to catch up encourage her to get pregnant?
-- NY Times 2/16/12
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9. Use of repeated table of contents page
§ Challenges traditional data stewardship practice
§ Privacy – is all the world a stage?
§ Limits to data lifecycle?
§ Data quality: the big, the bad, the ugly – and it all might be good!
9 © Copyright Ovum. All rights reserved. Ovum is an Informa business.

10. Data lifecycle –
How long can this go on?
§ Google, Yahoo, Facebook, etc.
don t deprecate web data
§ Hadoop designed for
economical scale-out
§ Moore s Law, declining cost of
storage
§ Is Hadoop Archive the answer?
§ Is Hadoop the new tape?
Management & skills will be the limit Aerial view of Quincy, WA data ctrs
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11. Use of repeated table of contents page
§ Challenges traditional data stewardship practice
§ Privacy – is all the world a stage?
§ Limits to data lifecycle?
§ Data quality: the big, the bad, the ugly – and it all might be
good!
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12. Data Quality & Hadoop –
Big Quality Questions
§ Can we cleanse terabytes of data?
§ Do we still need good data?
§ Are there new approaches to cleansing Big Data?
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13. Framing the issue
§ Garbage in, garbage out, but DW forced the
issue
§ Traditional approaches
§ Profiling, cleansing, MDM
§ DW vs. Hadoop data quality challenges
§ Known data sets & known criteria vs. vaguely known
§ Bounded vs. less bounded tasks
§ Limitations of MapReduce*
§ Cleansing & transformation within a single Map
operation;
§ Profiling & matching of unstructured data
§ Matching of data in operations without inter-process
communications
*Source: David Loshin, "Hadoop and Data Quality, Data Integration, Data Analysis" at
http://www.dataroundtable.com/?p=8841
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14. Is data quality necessary for Hadoop?
§ The App
§ How mission-critical?
§ Regulatory compliance impacts?
§ What degree of business impact?
§ The Data
§ The 4V s (volume, variety,
velocity, value) determine what
approaches to quality are feasible
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15. Examples
§ Web ad placement optimization
§ Counter-party risk management
for capital markets
§ Customer sentiment analysis
§ Managing smart utility grids or
urban infrastructure
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16. Bad data may be good
§ Sensory data
§ Outlier or drift?
§ Time to recalibrate devices?
§ Time to perform preventive
maintenance?
§ Are new/unaccounted environmental
factors skewing readings?
§ Human-readable data
§ Flawed concept of reality?
§ Flawed assumptions on data meaning?
§ Changes producing new norm
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17. Big Data quality in Hadoop –
Emergent approaches
§ Crowdsourcing data –
§ Collect data far & wide from as many diverse sources as possible. Torrents of data
overcome the noise.
§ Comparative trend analysis of incoming streams to dynamically ID the norm or
sweet spot of good data
§ Apply data science to correct the dots
§ Don t go record by record. Statistically analyze the data set in aggregate.
§ Iteratively analyze & re-analyze nature of data, keep analyzing outliers
§ Apply off-the-wall approaches
§ Enterprise Architectural approach
§ Semantic (domain) model-driven
§ Apply cleansing logic at run time
§ Critical for sensitive, regulatory-driven apps
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18. Summary
§ Challenges traditional data stewardship practice
§ Combination of old & new
§ Privacy – is all the world a stage?
§ Best practices, legal requirements still in flux
§ Don t be creepy!
§ Limits to data lifecycle?
§ Few enterprises are Google or Facebook
§ Ability to manage large infrastructure will be major limit
§ Data quality
§ Strategy depends on type of app & data set(s)
§ A spectrum of approaches -- from none to classic ETL to aggregate statistical
§ No single silver bullet
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